bluuebunny/crossref_metadata_2025_split huggingface dataset:
Instability Resistance Training improves Working Memory, Processing Speed and Response Inhibition in Healthy Older Adults: A Double-Blinded Randomised Controlled Trial
Authors: Eckardt Nils , Braun Claudia , Kibele Armin 
Published: February 2020

AbstractAging is associated with declines in physical and cognitive performance. While there is no doubt about beneficial effects of physical exercise on proxies of strength and balance, the overall evidence for positive effects of resistance and balance training on executive functions is rather inconsistent. Whether the simultaneous exercising of strength and balance, i.e., instability resistance training, promotes executive functions in older adults is unknown. In the present trial, we tested the effects of unstable vs. stable resistance training on executive functions. Sixty-eight healthy older adults aged 65–79 years were randomly assigned to either an instability free-weight resistance training or one of two stable machine-based resistance training programs. Each group exercised twice a week on non-consecutive days for 10 weeks. Four tests to evaluate specific domains of executive functions were administered prior and following training: working memory, processing speed, response inhibition and set-shifting. The instability resistance training group improved working memory, processing speed and response inhibition from pre to post-test. In contrast, we found no improvements in executive functions for both stable resistance training groups. Our results demonstrate that 10 weeks of instability resistance training suffice to improve executive functions in older adults.
Climate model variability leads to uncertain predictions of the future abundance of stream macroinvertebrates
Authors: Kakouei Karan , Domisch Sami , Kiesel Jens , Kail Jochem , Jähnig Sonja C. 
Published: February 2020

AbstractClimate change has the potential to alter the flow regimes of rivers and consequently affect the taxonomic and functional diversity of freshwater organisms. We modeled future flow regimes for the 2050 and 2090 time horizons and tested how flow regimes impact the abundance of 150 macroinvertebrate species and their functional trait compositions in one lowland river catchment (Treene) and one mountainous river catchment (Kinzig) in Europe. We used all 16 global circulation models (GCMs) and regional climate models (RCMs) of the CORDEX dataset under the RCP 8.5 scenario to calculate future river flows. The high variability in relative change of flow among the 16 climate models cascaded into the ecological models and resulted in substantially different predicted abundance values for single species. This variability also cascades into any subsequent analysis of taxonomic or functional freshwater biodiversity. Our results showed that flow alteration effects are different depending on the catchment and the underlying species pool. Documenting such uncertainties provides a basis for the further assessment of potential climate-change impacts on freshwater taxa distributions.
Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection
Authors: Xu Yucan , Ju Lingsha , Tong Jianhua , Zhou Cheng-Mao , Yang Jian-Jun 
Published: February 2020

AbstractThe aim of this study is to explore the feasibility of using machine learning (ML) technology to predict postoperative recurrence risk among stage IV colorectal cancer patients. Four basic ML algorithms were used for prediction—logistic regression, decision tree, GradientBoosting and lightGBM. The research samples were randomly divided into a training group and a testing group at a ratio of 8:2. 999 patients with stage 4 colorectal cancer were included in this study. In the training group, the GradientBoosting model’s AUC value was the highest, at 0.881. The Logistic model’s AUC value was the lowest, at 0.734. The GradientBoosting model had the highest F1_score (0.912). In the test group, the AUC Logistic model had the lowest AUC value (0.692). The GradientBoosting model’s AUC value was 0.734, which can still predict cancer progress. However, the gbm model had the highest AUC value (0.761), and the gbm model had the highest F1_score (0.974). The GradientBoosting model and the gbm model performed better than the other two algorithms. The weight matrix diagram of the GradientBoosting algorithm shows that chemotherapy, age, LogCEA, CEA and anesthesia time were the five most influential risk factors for tumor recurrence. The four machine learning algorithms can each predict the risk of tumor recurrence in patients with stage IV colorectal cancer after surgery. Among them, GradientBoosting and gbm performed best. Moreover, the GradientBoosting weight matrix shows that the five most influential variables accounting for postoperative tumor recurrence are chemotherapy, age, LogCEA, CEA and anesthesia time.
Continued versus Suspended Cardiac Resynchronization Therapy after Left Ventricular Assist Device Implantation
Authors: Roukoz Henri , Bhan Adarsh , Ravichandran Ashwin , Ahmed Mustafa M. , Bhat Geetha , Cowger Jennifer , Abdullah Munazzah , Dhawan Rahul , Trivedi Jaimin R. , Slaughter Mark S. , Gopinathannair Rakesh 
Published: February 2020

AbstractCardiac resynchronization therapy (CRT) improves outcomes in heart failure patients with wide QRS complex. However, CRT management following continuous flow Left Ventricular Assist Device (LVAD) implant vary: some centers continue CRT while others turn off the left ventricular (LV) lead at LVAD implant. We sought to study the effect of continued CRT versus turning off CRT pacing following continuous flow LVAD implantation. A comprehensive retrospective multicenter cohort of 295 patients with LVAD and pre-existing CRT was studied. CRT was programmed off after LVAD implant in 44 patients. We compared their outcomes to the rest of the cohort using univariate and multivariate models. Mean age was 60 ± 12 years, 83% were males, 52% had ischemic cardiomyopathy and 54% were destination therapy. Mean follow-up was 2.4 ± 2.0 years, and mean LVAD support time was 1.7 ± 1.4 years. Patients with CRT OFF had a higher Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) mean profile (3.9 vs 3.3, p = 0.01), more secondary prevention indication for a defibrillator (64.9% vs 44.5%, p = 0.023), and more pre-LVAD ventricular arrhythmias (VA) (77% vs 60%, p = 0.048). There were no differences between the CRT OFF and CRT ON groups in overall mortality (Log rank p = 0.32, adjusted HR = 1.14 [0.54–2.22], p = 0.71), heart transplantation, cardiac and noncardiac mortality, all cause hospitalizations, hospitalizations for ICD shocks, and number and frequency of ICD shocks or anti-tachycardia pacing therapy. There were no differences in post LVAD atrial arrhythmias (AA) (Adjusted OR = 0.45 [0.18–1.06], p = 0.31) and ventricular arrhythmias (OR = 0.65 [0.41–1.78], p = 0.41). There was no difference in change in LVEF, LV end diastolic and end systolic diameters between the 2 groups. Our study suggests that turning off CRT pacing after LVAD implantation in patients with previous CRT pacing did not affect mortality, heart transplantation, device therapies or arrhythmia burden. A prospective study is needed to confirm these findings.
Observer-Agent Kinematic Similarity Facilitates Action Intention Decoding
Authors: De Marco Doriana , Scalona Emilia , Bazzini Maria Chiara , Avanzini Pietro , Fabbri-Destro Maddalena 
Published: February 2020

AbstractIt is well known that the kinematics of an action is modulated by the underlying motor intention. In turn, kinematics serves as a cue also during action observation, providing hints about the intention of the observed action. However, an open question is whether decoding others’ intentions on the basis of their kinematics depends solely on how much the kinematics varies across different actions, or rather it is also influenced by its similarity with the observer motor repertoire. The execution of reach-to-grasp and place actions, differing for target size and context, was recorded in terms of upper-limb kinematics in 21 volunteers and in an actor. Volunteers had later to observe the sole reach-to-grasp phase of the actor’s actions, and predict the underlying intention. The potential benefit of the kinematic actor-participant similarity for recognition accuracy was evaluated. In execution, both target size and context modulated specific kinematic parameters. More importantly, although participants performed above chance in intention recognition, the similarity of motor patterns positively correlated with recognition accuracy. Overall, these data indicate that kinematic similarity exerts a facilitative role in intention recognition, providing further support to the view of action intention recognition as a visuo-motor process grounded in motor resonance.
Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features
Authors: Sahlol Ahmed T. , Kollmannsberger Philip , Ewees Ahmed A. 
Published: February 2020

AbstractWhite Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.
Modeling membrane nanotube morphology: the role of heterogeneity in composition and material properties
Authors: Alimohamadi Haleh , Ovryn Ben , Rangamani Padmini 
Published: February 2020

AbstractMembrane nanotubes are dynamic structures that may connect cells over long distances. Nanotubes are typically thin cylindrical tubes, but they may occasionally have a beaded architecture along the tube. In this paper, we study the role of membrane mechanics in governing the architecture of these tubes and show that the formation of bead-like structures along the nanotubes can result from local heterogeneities in the membrane either due to protein aggregation or due to membrane composition. We present numerical results that predict how membrane properties, protein density, and local tension compete to create a phase space that governs the morphology of a nanotube. We also find that there exists a discontinuity in the energy that impedes two beads from fusing. These results suggest that the membrane-protein interaction, membrane composition, and membrane tension closely govern the tube radius, number of beads, and the bead morphology.
Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities
Authors: Kothari Rakshit , Yang Zhizhuo , Kanan Christopher , Bailey Reynold , Pelz Jeff B. , Diaz Gabriel J. 
Published: February 2020

AbstractThe study of gaze behavior has primarily been constrained to controlled environments in which the head is fixed. Consequently, little effort has been invested in the development of algorithms for the categorization of gaze events (e.g. fixations, pursuits, saccade, gaze shifts) while the head is free, and thus contributes to the velocity signals upon which classification algorithms typically operate. Our approach was to collect a novel, naturalistic, and multimodal dataset of eye + head movements when subjects performed everyday tasks while wearing a mobile eye tracker equipped with an inertial measurement unit and a 3D stereo camera. This Gaze-in-the-Wild dataset (GW) includes eye + head rotational velocities (deg/s), infrared eye images and scene imagery (RGB + D). A portion was labelled by coders into gaze motion events with a mutual agreement of 0.74 sample based Cohen’s κ. This labelled data was used to train and evaluate two machine learning algorithms, Random Forest and a Recurrent Neural Network model, for gaze event classification. Assessment involved the application of established and novel event based performance metrics. Classifiers achieve ~87% human performance in detecting fixations and saccades but fall short (50%) on detecting pursuit movements. Moreover, pursuit classification is far worse in the absence of head movement information. A subsequent analysis of feature significance in our best performing model revealed that classification can be done using only the magnitudes of eye and head movements, potentially removing the need for calibration between the head and eye tracking systems. The GW dataset, trained classifiers and evaluation metrics will be made publicly available with the intention of facilitating growth in the emerging area of head-free gaze event classification.
Downregulation of peripheral PTGS2/COX-2 in response to valproate treatment in patients with epilepsy
Authors: Rawat Chitra , Kutum Rintu , Kukal Samiksha , Srivastava Ankit , Dahiya Ujjwal Ranjan , Kushwaha Suman , Sharma Sangeeta , Dash Debasis , Saso Luciano , Srivastava Achal K. , Kukreti Ritushree 
Published: February 2020

AbstractAntiepileptic drug therapy has significant inter-patient variability in response towards it. The current study aims to understand this variability at the molecular level using microarray-based analysis of peripheral blood gene expression profiles of patients receiving valproate (VA) monotherapy. Only 10 unique genes were found to be differentially expressed in VA responders (n = 15) and 6 genes in the non-responders (n = 8) (fold-change >2, p < 0.05).PTGS2which encodes cyclooxygenase-2, COX-2, showed downregulation in the responders compared to the non-responders.PTGS2/COX-2 mRNA profiles in the two groups corresponded to their plasma profiles of the COX-2 product, prostaglandin E2(PGE2). Since COX-2 is believed to regulate P-glycoprotein (P-gp), a multidrug efflux transporter over-expressed at the blood-brain barrier (BBB) in drug-resistant epilepsy, the pathway connecting COX-2 and P-gp was further exploredin vitro. Investigation of the effect of VA upon the brain endothelial cells (hCMEC/D3) in hyperexcitatory conditions confirmed suppression of COX-2-dependent P-gp upregulation by VA. Our findings suggest that COX-2 downregulation by VA may suppress seizure-mediated P-gp upregulation at the BBB leading to enhanced drug delivery to the brain in the responders. Our work provides insight into the association of peripheralPTGS2/COX-2 expression with VA efficacy and the role of COX-2 as a potential therapeutic target for developing efficacious antiepileptic treatment.
Effective Attributes Quantification to Bridge Gap between Elastic Properties and Reservoir Parameters in Self-Resource Rocks
Authors: Abid Muhammad , Geng Jianhua 
Published: February 2020

AbstractThe successful production of unconventional resources such as shale gas is highly dependent on its two reservoir properties, organic matter and rock brittleness. High resolution spatially characterization of these two unconventional reservoir properties needs surface reflection seismic data. However, to delineate these two parameters on seismic scale is a challenging task because poor correlation is observed between these parameters and elastic properties of the rock. To encounter this adversity in current study we proposed effective attributes method in which organic shale reservoir properties are divided into their hard and soft elastic response. From the analysis of worldwide laboratory dataset, we find that hard and soft components have shown us much better linear correlation with P- and S- wave impedance. The proposed effective attributes, helped us to reduce the gap between unconventional reservoir properties and seismic characteristics. These attributes are the main controlling factor for rock elastic properties and exhibit information about hydrocarbon generation capacity and rock brittleness. A well data example from Sembar shale has also shown successful results for proposed effective attributes methodology. These attributes application on inverted P-wave impedance seismic data of employed organic shale reservoir have shown productive results to quantify its unconventional prospect on seismic scale. The approach used in this study can be confidently employed to assess unconventional reservoir potential in other parts of the world.
Correlative Study of Enhanced Excitonic Emission in ZnO Coated with Al Nanoparticles using Electron and Laser Excitation
Authors: Fiedler Saskia , Lem Laurent O. Lee Cheong , Ton-That Cuong , Schleuning Markus , Hoffmann Axel , Phillips Matthew R. 
Published: February 2020

AbstractRecently, metal nanoparticle surface coatings have been found to significantly enhance the ultra-violet luminescence intensity from ZnO, providing a viable means to mitigate optical losses and improve LED performance. Although there is general agreement that resonantly excited Localized Surface Plasmons (LSPs) in metal nanoparticles can directly couple to excitons in the semiconductor increasing their spontaneous emission rate, the exact mechanisms involved in this phenomenon are currently not fully understood. In this work, LSP-exciton coupling in bulk and nanostructured ZnO coated with a 2 nm Al nanoparticle layer is investigated using correlative photoluminescence and depth-resolved cathodoluminescence and time-resolved photoluminescence spectroscopy. Temperature-resolved cathodoluminescence and photoluminescence measurements from 10 K to 250 K show free exciton (FX) emission enhancement factors up to 12x at 80 K, and reveal that the FX couple more efficiently to the LSPs compared to the localized donor-bound excitons. A strong polarization dependence between the LSPs and FX is observed where FX transitions are more strongly enhanced when polarized in the same direction as the electric field of the incident excitation, which is different for laser and electron beam sources. This result indicates that selective enhancement of the excitonic emission peaks in the ZnO coated with Al nanoparticles can be achieved by choosing the appropriate ZnO substrate orientation.
Record statistics of bursts signals the onset of acceleration towards failure
Authors: Kádár Viktória , Pál Gergő , Kun Ferenc 
Published: February 2020

AbstractForecasting the imminent catastrophic failure has a high importance for a large variety of systems from the collapse of engineering constructions, through the emergence of landslides and earthquakes, to volcanic eruptions. Failure forecast methods predict the lifetime of the system based on the time-to-failure power law of observables describing the final acceleration towards failure. We show that the statistics of records of the event series of breaking bursts, accompanying the failure process, provides a powerful tool to detect the onset of acceleration, as an early warning of the impending catastrophe. We focus on the fracture of heterogeneous materials using a fiber bundle model, which exhibits transitions between perfectly brittle, quasi-brittle, and ductile behaviors as the amount of disorder is increased. Analyzing the lifetime of record size bursts, we demonstrate that the acceleration starts at a characteristic record rank, below which record breaking slows down due to the dominance of disorder in fracturing, while above it stress redistribution gives rise to an enhanced triggering of bursts and acceleration of the dynamics. The emergence of this signal depends on the degree of disorder making both highly brittle fracture of low disorder materials, and ductile fracture of strongly disordered ones, unpredictable.
Integrative data modeling from lung and lymphatic cancer predicts functional roles for miR-34a and miR-16 in cell fate regulation
Authors: Gupta Shantanu , Silveira Daner A. , Barbé-Tuana Florencia M. , Mombach José Carlos M. 
Published: February 2020

AbstractMiR-34a and miR-16 coordinately control cell cycle checkpoint in non-small cell lung cancer (NSCLC) cells. In cutaneous T-cell lymphoma (CTCL) cells miR-16 regulates a switch between apoptosis and senescence, however the role of miR-34a in this process is unclear. Both miRNAs share many common targets and experimental evidences suggest that they synergistically control the cell-fate regulation of NSCLC. In this work we investigate whether the coordinate action between miR-34a and miR-16 can explain experimental results in multiple cell lines of NSCLC and CTCL. For that we propose a Boolean model of the G1/S checkpoint regulation contemplating the regulatory influences of both miRNAs. Model validation was performed by comparisons with experimental information from the following cell lines: A549, H460, H1299, MyLa and MJ presenting excellent agreement. The model integrates in a single logical framework the mechanisms responsible for cell fate decision in NSCLC and CTCL cells. From the model analysis we suggest that miR-34a is the main controller of miR-16 activity in these cells. The model also allows to investigate perturbations of single or more molecules with the purpose to intervene in cell fate mechanisms of NSCLC and CTCL cells.
C-phycocyanin attenuates RANKL-induced osteoclastogenesis and bone resorption in vitro through inhibiting ROS levels, NFATc1 and NF-κB activation
Authors: AlQranei Mohammed S. , Aljohani Hanan , Majumdar Sunipa , Senbanjo Linda T. , Chellaiah Meenakshi A. 
Published: February 2020

AbstractExcessive bone loss occurs in inflammatory disorders such as periodontitis and osteoporosis. The underlying mechanism is related to the differentiation of macrophages into multinucleated giant osteoclasts and their bone resorptive activity. C-Phycocyanin (C-PC) is a phycobiliprotein extracted from the blue-green algae, which has been shown to have various pharmacological effects. The role of C-PC on bone metabolism needs revelation. In this study, we determined the effectiveness of C-PC as an inhibitor of osteoclast differentiation, activity, and survival in vitro. We found that C-PC strongly inhibited the differentiation of macrophages to TRAP-positive osteoclasts, distinctive osteoclast specific podosomal organization, and dentine matrix resorption without any cytotoxicity. Also, it suppressed the expression of osteoclast specific markers, such as cathepsin K and integrin β3 at mRNA and protein levels. RANKL mediated signaling utilizes reactive oxygen species (ROS) for the differentiation of osteoclasts. C-PC attenuated RANKL stimulated ROS. Mechanistic studies indicate that C-PC has the potential to reduce osteoclast formation via blocking the degradation of cytosolic IκB-α and hence, the activation of downstream markers such as c-Fos and NFATc1. However, it does not have any effect on osteoblast-mediated bone formation in vitro. Collectively, our data suggest that C-PC may be utilized as a therapeutic agent that can target bone loss mediated by excessive osteoclastic bone resorption without affecting osteoblastic activity in bone.