bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conductance artery stiffness impairs atrio-ventriculo-arterial coupling before manifestation of arterial hypertension or left ventricular hypertrophic remodelling
Authors: Kyhl Kasper , von Huth Sebastian , Bojer Annemie , Thomsen Carsten , Engstrøm Thomas , Vejlstrup Niels , Madsen Per Lav 
Published: July 2021

AbstractAs part of normal ageing, conductance arteries lose their cushion function, left ventricle (LV) filling and also left atrial emptying are impaired. The relation between conductance artery stiffness and LV diastolic function is normally explained by arterial hypertension and LV hypertrophy as needed intermediaries. We examined whether age-related aortic stiffening may influence LV diastolic function in normal healthy subjects. Aortic distensibility and pulse wave velocity (PWV) were related to LV emptying and filling parameters and left atrial emptying parameters as determined by magnetic resonance imaging in 36 healthy young (< 35 years) and 16 healthy middle-aged and elderly (> 35 years) with normal arterial blood pressure and myocardial mass. In the overall cohort, total aorta PWV correlated to a decrease in LV peak-emptying volume (r = 0.43), LV peak-filling (r = 0.47), passive atrial emptying volume (r = 0.66), and an increase in active atrial emptying volume (r = 0.47) (all p < 0.001). PWV was correlated to passive atrial emptying volume even if only the > 35-year-old were considered (r = 0.53; p < 0.001). Total peripheral resistance demonstrated similar correlations as PWV, but in a regression analysis only the total aorta PWV was related to left atrial (LA) passive emptying volume. Via impaired ventriculo-arterial coupling, the increased aortic PWV seen with normal ageing hence affects atrio-ventricular coupling, before increased aortic PWV is associated with significantly increased arterial blood pressure or LV hypertrophic remodelling. Our findings reinforce the existence of atrio-ventriculo-arterial coupling and suggest aortic distensibility should be considered an early therapeutic target to avoid diastolic dysfunction of the LV.
Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases
Authors: Samani Zahra Riahi , Parker Drew , Wolf Ronald , Hodges Wes , Brem Steven , Verma Ragini 
Published: July 2021

AbstractTumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.
Physiological and transcriptome analyses for assessing the effects of exogenous uniconazole on drought tolerance in hemp (Cannabis sativa L.)
Authors: Jiang Ying , Sun Yufeng , Zheng Dianfeng , Han Chengwei , Cao Kun , Xu Lei , Liu Shuxia , Cao Yanyong , Feng Naijie 
Published: July 2021

AbstractUniconazole (S-(+)-uniconazole), a plant growth retardant, exerts key roles in modulating growth and development and increasing abiotic stress tolerance in plants. However, the underlying mechanisms by which uniconazole regulates drought response remain largely unknown. Here, the effects of exogenous uniconazole on drought tolerance in hemp were studied via physiological and transcriptome analyses of the drought-sensitive industrial hemp cultivar Hanma No. 2 grown under drought stress. Exogenous uniconazole treatment increased hemp tolerance to drought-induced damage by enhancing chlorophyll content and photosynthesis capacity, regulating activities of enzymes involved in carbon and nitrogen metabolism, and altering endogenous hormone levels. Expression of genes associated with porphyrin and chlorophyll metabolism, photosynthesis-antenna proteins, photosynthesis, starch and sucrose metabolism, nitrogen metabolism, and plant hormone signal transduction were significantly regulated by uniconazole compared with that by control (distilled water) under drought stress. Numerous genes were differentially expressed to increase chlorophyll content, enhance photosynthesis, regulate carbon–nitrogen metabolism-related enzyme activities, and alter endogenous hormone levels. Thus, uniconazole regulated physiological and molecular characteristics of photosynthesis, carbon–nitrogen metabolism, and plant hormone signal transduction to enhance drought resistance in industrial hemp.
Physico-chemical properties of an innovative gluten-free, low-carbohydrate and high protein-bread enriched with pea protein powder
Authors: Wójcik Monika , Różyło Renata , Schönlechner Regine , Berger Mary Violet 
Published: July 2021

AbstractThe study aimed to determine the effect of pea protein powder on the pasting behavior and physico-chemical properties including the composition of amino and fatty acids of gluten-free bread with low-carbohydrate content. The control bread recipe was based on buckwheat flour (50 g) and flaxseed flour (50 g) as main flours. Additionally, the improving additives for this control bread such as psyllium husk (4 g), potato fiber (2 g), and guar gum (2 g) were used. The mixture of base flour was supplemented with the addition of pea protein powder (PPP) in the amount ranging from 5 to 25%. The results of Visco analyzes measured by RVA apparatus showed that the addition of 10% PPP to the control bread did not significantly differentiate peak viscosity and pasting temperature which was at the level 3115 cP and 3149 cP and 50 °C, respectively. Supplementation of low-carbohydrate bread with 10% of PPP was acceptable and significantly increased the content of all analyzed amino acids, as well as the amount of α-linolenic acid concerning the control bread. The lowest value of chemical score was observed for leucine. The EAAI (essential amino acid index) value increased from 34 to 40 when the optimal protein supplement was added. The developed gluten-free, low-carbohydrate, and high protein bread was characterized by contents of carbohydrate of 16.9%, protein of 17.1%, fiber of 13.7%, fat of 3.3% and its calorific value was 194 kcal/100 g.
Tobacco BY2 cells expressing recombinant cardosin B as an alternative for production of active milk clotting enzymes
Authors: Folgado André , Abranches Rita 
Published: July 2021

AbstractCynara cardunculusL. or cardoon is a plant that is used as a source of milk clotting enzymes during traditional cheese manufacturing. This clotting activity is due to aspartic proteases (APs) found in the cardoon flower, named cyprosins and cardosins. APs from cardoon flowers display a great degree of heterogeneity, resulting in variable milk clotting activities and directly influencing the final product. Producing these APs using alternative platforms such as bacteria or yeast has proven challenging, which is hampering their implementation on an industrial scale. We have developed tobacco BY2 cell lines as an alternative plant-based platform for the production of cardosin B. These cultures successfully produced active cardosin B and a purification pipeline was developed to obtain isolated cardosin B. The enzyme displayed proteolytic activity towards milk caseins and milk clotting activity under standard cheese manufacturing conditions. We also identified an unprocessed form of cardosin B and further investigated its activation process. The use of protease-specific inhibitors suggested a possible role for a cysteine protease in cardosin B processing. Mass spectrometry analysis identified three cysteine proteases containing a granulin-domain as candidates for cardosin B processing. These findings suggest an interaction between these two groups of proteases and contribute to an understanding of the mechanisms behind the regulation and processing of plant APs. This work also paves the way for the use of tobacco BY2 cells as an alternative production system for active cardosins and represents an important advancement towards the industrial production of cardoon APs.
A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light
Authors: Kumpošt Vojtěch , Vallone Daniela , Gondi Srinivas Babu , Foulkes Nicholas S. , Mikut Ralf , Hilbert Lennart 
Published: July 2021

AbstractThe circadian clock is a cellular mechanism that synchronizes various biological processes with respect to the time of the day. While much progress has been made characterizing the molecular mechanisms underlying this clock, it is less clear how external light cues influence the dynamics of the core clock mechanism and thereby entrain it with the light–dark cycle. Zebrafish-derived cell cultures possess clocks that are directly light-entrainable, thus providing an attractive laboratory model for circadian entrainment. Here, we have developed a stochastic oscillator model of the zebrafish circadian clock, which accounts for the core clock negative feedback loop, light input, and the proliferation of single-cell oscillator noise into population-level luminescence recordings. The model accurately predicts the entrainment dynamics observed in bioluminescent clock reporter assays upon exposure to a wide range of lighting conditions. Furthermore, we have applied the model to obtain refitted parameter sets for cell cultures exposed to a variety of pharmacological treatments and predict changes in single-cell oscillator parameters. Our work paves the way for model-based, large-scale screens for genetic or pharmacologically-induced modifications to the entrainment of circadian clock function.
Phytoremediation of electroplating wastewater by vetiver grass (Chrysopogon zizanoides L.)
Authors: Nugroho Andhika Puspito , Butar Erni Saurmalinda Butar , Priantoro Ekaputra Agung , Sriwuryandari Lies , Pratiwi Zulfa Berliana , Sembiring Tarzan 
Published: July 2021

AbstractThe electroplating industry generates wastewater containing a variety of heavy metals which potentially contaminate water ecosystems. The available and well-known electroplating wastewater treatments are considered as an expensive and less effective method, therefore phytoremediation was used as an alternative friendly solution. This study aims to evaluate the uptake and elimination rate of heavy metals by vetiver (Chrysopogon zizanoides L.) on metal-polluted water. Vetiver was planted in artificial electroplating wastewater containing different levels (low, medium, high) of chromium (Cr) and nickel (Ni). Water, roots, and shoots were collected periodically to determine Cr and Ni contents using Atomic Absorption Spectrometry (AAS). Metal accumulation and elimination rate, Bioconcentration Factor (BCF), Biological Absorption Coefficient (BAC), and Translocation Factor (TF) were calculated to evaluate plant’s effectiveness in metal remediation processes. The results showed that vetiver (C. zizanoides L.) was able to remove 61.10% Cr and 95.65% Ni on metal-contaminated water. The highest uptake rates for Cr and Ni are 127.21 mg/kg/day and 15.60 mg/kg/day respectively, while the elimination rates for Cr and Ni tend to slow 1.09 mg/kg/day and 12.24 mg/kg/day respectively. Vetiver BCF, BAC, and TF values on Cr and Ni contaminated water were greater than 1, which indicates that vetiver work through phytoextraction and phytostabilization to treat metals. The findings showed that vetiver has promise as a phytoremediation agent thus providing implication for electroplating wastewater treatment.
Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks
Authors: Levy Orr , Kenett Yoed N. , Oxenberg Orr , Castro Nichol , De Deyne Simon , Vitevitch Michael S. , Havlin Shlomo 
Published: July 2021

AbstractAn essential aspect of human communication is the ability to access and retrieve information from ones’ ‘mental lexicon’. This lexical access activates phonological and semantic components of concepts, yet the question whether and how these two components relate to each other remains widely debated. We harness tools from network science to construct a large-scale linguistic multilayer network comprising of phonological and semantic layers. We find that the links in the two layers are highly similar to each other and that adding information from one layer to the other increases efficiency by decreasing the network overall distances, but specifically affecting shorter distances. Finally, we show how a multilayer architecture demonstrates the highest efficiency, and how this efficiency relates to weak semantic relations between cue words in the network. Thus, investigating the interaction between the layers and the unique benefit of a linguistic multilayer architecture allows us to quantify theoretical cognitive models of lexical access.
Control of stripe rust of wheat using indigenous endophytic bacteria at seedling and adult plant stage
Authors: Kiani Tehmina , Mehboob Farrakh , Hyder Muhammad Zeeshan , Zainy Zainy , Xu Liangsheng , Huang Lili , Farrakh Sumaira 
Published: July 2021

AbstractStripe rust (caused by Puccinia striiformis tritici) is one of the most devastating diseases of wheat. The most effective ways to control stripe rust are the use of resistant cultivars and the timely use of an appropriate dose of fungicide. However, the changing nature of rust pathogen outwits the use of resistant cultivars, and the use of a fungicide is associated with environmental problems. To control the disease without sacrificing the environment, we screened 16 endophytic bacteria, which were isolated from stripe rust-resistant wheat cultivars in our previous study, for their biocontrol potential. A total of 5 bacterial strains Serratia marcescens 3A, Bacillus megaterium 6A, Paneibacillus xylanexedens 7A, Bacillus subtilis 11A, and Staphyloccus agentis 15A showed significant inhibition of Puccinia striiformis f. sp. tritici (Pst) urediniospores germination. Two formulations i.e., fermented liquid with bacterial cell (FLBC) and fermented liquid without bacterial cells (FL) of each bacterial strain, were evaluated against the urediniospores germination. Formulations of five selected endophytic bacteria strains significantly inhibited the uredinioospores germination in the lab experiments. It was further confirmed on seedlings of Pakistani susceptible wheat cultivar Inqilab-91 in the greenhouse, as well as in semi-field conditions. FLBC and FL formulations applied 24 h before Pst inoculation (hbi) displayed a protective mode. The efficacy of FLBC was between 34.45 and 87.77%, while the efficacy of FL was between 39.27 and 85.16% when applied 24 hbi. The inoculated wheat cultivar Inqilab-91 was also tested under semi-field conditions during the 2017–2018 cropping season at the adult plant stage. The strains Bacillus megaterium 6A and Paneibacillus xylanexedens 7A alone significantly reduced the disease severity of stripe rust with the efficacy of 65.16% and 61.11% for the FLBC in protective effect, while 46.07% and 44.47% in curative effect, respectively. Inoculated seedlings of Inqilab-91 showed higher activities of antioxidant enzymes, superoxide dismutase (SOD), peroxidase (POD), polyphenol oxidase (PPO), and phenylalanine ammonia-lyase (PAL). The treated seedlings also showed higher expressions of pathogenesis-related (PR) protein genes, antifungal protein (PR-1), β-1,3-endoglucanases (PR-2), endochitinases (PR-4), peroxidase (PR-9), and ribonuclease-like proteins (PR-10). These results indicated that endophytic bacteria have the biocontrol potential, which can be used to manage stripe rust disease. High production antioxidant enzymes, as well as high expression of PR protein genes, might be crucial in triggering the host defense mechanism against Pst.
Using dense seismo-acoustic network to provide timely warning of the 2019 paroxysmal Stromboli eruptions
Authors: Le Pichon A. , Pilger C. , Ceranna L. , Marchetti E. , Lacanna G. , Souty V. , Vergoz J. , Listowski C. , Hernandez B. , Mazet-Roux G. , Dupont A. , Hereil P. 
Published: July 2021

AbstractStromboli Volcano is well known for its persistent explosive activity. On July 3rd and August 28th 2019, two paroxysmal explosions occurred, generating an eruptive column that quickly rose up to 5 km above sea level. Both events were detected by advanced local monitoring networks operated by Istituto Nazionale di Geofisica e Vulcanologia (INGV) and Laboratorio di Geofisica Sperimentale of the University of Firenze (LGS-UNIFI). Signals were also recorded by the Italian national seismic network at a range of hundreds of kilometres and by infrasonic arrays up to distances of 3700 km. Using state-of-the-art propagation modeling, we identify the various seismic and infrasound phases that are used for precise timing of the eruptions. We highlight the advantage of dense regional seismo-acoustic networks to enhance volcanic signal detection in poorly monitored regions, to provide timely warning of eruptions and reliable source amplitude estimate to Volcanic Ash Advisory Centres (VAAC).
The first mitochondrial genome of the genus Exhippolysmata (Decapoda: Caridea: Lysmatidae), with gene rearrangements and phylogenetic associations in Caridea
Authors: Ye Ying-ying , Miao Jing , Guo Ya-hong , Gong Li , Jiang Li-hua , Lü Zhen‑ming , Xu Kai-da , Guo Bao-ying 
Published: July 2021

AbstractThe complete mitochondrial genome (mitogenome) of animals can provide useful information for evolutionary and phylogenetic analyses. The mitogenome of the genus Exhippolysmata (i.e., Exhippolysmata ensirostris) was sequenced and annotated for the first time, its phylogenetic relationship with selected members from the infraorder Caridea was investigated. The 16,350 bp mitogenome contains the entire set of 37 common genes. The mitogenome composition was highly A + T biased at 64.43% with positive AT skew (0.009) and negative GC skew (− 0.199). All tRNA genes in the E. ensirostris mitogenome had a typical cloverleaf secondary structure, except for trnS1 (AGN), which appeared to lack the dihydrouridine arm. The gene order in the E. ensirostris mitogenome was rearranged compared with those of ancestral decapod taxa, the gene order of trnL2-cox2 changed to cox2-trnL2. The tandem duplication-random loss model is the most likely mechanism for the observed gene rearrangement of E. ensirostris. The ML and BI phylogenetic analyses place all Caridea species into one group with strong bootstrap support. The family Lysmatidae is most closely related to Alpheidae and Palaemonidae. These results will help to better understand the gene rearrangements and evolutionary position of E. ensirostris and lay a foundation for further phylogenetic studies of Caridea.
Longitudinal monitoring of laboratory markers characterizes hospitalized and ambulatory COVID-19 patients
Authors: Velavan Thirumalaisamy P. , Kuk Salih , Linh Le Thi Kieu , Lamsfus Calle Carlos , Lalremruata Albert , Pallerla Srinivas Reddy , Kreidenweiss Andrea , Held Jana , Esen Meral , Gabor Julian , Neurohr Eva Maria , Shamsrizi Parichehr , Fathi Anahita , Biecker Erwin , Berg Christoph P. , Ramharter Michael , Addo Marylyn Martina , Kreuels Benno , Kremsner Peter G. 
Published: July 2021

AbstractEarly detection of severe forms of COVID-19 is absolutely essential for timely triage of patients. We longitudinally followed-up two well-characterized patient groups, hospitalized moderate to severe (n = 26), and ambulatory mild COVID-19 patients (n = 16) at home quarantine. Human D-dimer, C-reactive protein (CRP), ferritin, cardiac troponin I, interleukin-6 (IL-6) levels were measured on day 1, day 7, day 14 and day 28. All hospitalized patients were SARS-CoV-2 positive on admission, while all ambulatory patients were SARS-CoV-2 positive at recruitment. Hospitalized patients had higher D-dimer, CRP and ferritin, cardiac troponin I and IL-6 levels than ambulatory patients (p < 0.001, p < 0.001, p = 0.016, p = 0.035, p = 0.002 respectively). Hospitalized patients experienced significant decreases in CRP, ferritin and IL-6 levels from admission to recovery (p < 0.001, p = 0.025, and p = 0.001 respectively). Cardiac troponin I levels were high during the acute phase in both hospitalized and ambulatory patients, indicating a potential myocardial injury. In summary, D-dimer, CRP, ferritin, cardiac troponin I, IL-6 are predictive laboratory markers and can largely determine the clinical course of COVID-19, in particular the prognosis of critically ill COVID-19 patients.
Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
Authors: Feng Jiarui , Lee Jennifer , Vesoulis Zachary A. , Li Fuhai 
Published: July 2021

AbstractMortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization.