bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust high-dimensional memory-augmented neural networks
Authors: Karunaratne Geethan , Schmuck Manuel , Le Gallo Manuel , Cherubini Giovanni , Benini Luca , Sebastian Abu , Rahimi Abbas 
Published: April 2021

AbstractTraditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance neural networks with an explicit memory to overcome these issues. Access to this explicit memory, however, occurs via soft read and write operations involving every individual memory entry, resulting in a bottleneck when implemented using the conventional von Neumann computer architecture. To overcome this bottleneck, we propose a robust architecture that employs a computational memory unit as the explicit memory performing analog in-memory computation on high-dimensional (HD) vectors, while closely matching 32-bit software-equivalent accuracy. This is achieved by a content-based attention mechanism that represents unrelated items in the computational memory with uncorrelated HD vectors, whose real-valued components can be readily approximated by binary, or bipolar components. Experimental results demonstrate the efficacy of our approach on few-shot image classification tasks on the Omniglot dataset using more than 256,000 phase-change memory devices. Our approach effectively merges the richness of deep neural network representations with HD computing that paves the way for robust vector-symbolic manipulations applicable in reasoning, fusion, and compression.
Integrative reconstruction of cancer genome karyotypes using InfoGenomeR
Authors: Lee Yeonghun , Lee Hyunju 
Published: April 2021

AbstractAnnotation of structural variations (SVs) and base-level karyotyping in cancer cells remains challenging. Here, we present Integrative Framework for Genome Reconstruction (InfoGenomeR)-a graph-based framework that can reconstruct individual SVs into karyotypes based on whole-genome sequencing data, by integrating SVs, total copy number alterations, allele-specific copy numbers, and haplotype information. Using whole-genome sequencing data sets of patients with breast cancer, glioblastoma multiforme, and ovarian cancer, we demonstrate the analytical potential of InfoGenomeR. We identify recurrent derivative chromosomes derived from chromosomes 11 and 17 in breast cancer samples, with homogeneously staining regions for CCND1 and ERBB2, and double minutes and breakage-fusion-bridge cycles in glioblastoma multiforme and ovarian cancer samples, respectively. Moreover, we show that InfoGenomeR can discriminate private and shared SVs between primary and metastatic cancer sites that could contribute to tumour evolution. These findings indicate that InfoGenomeR can guide targeted therapies by unravelling cancer-specific SVs on a genome-wide scale.
Scalable photonic-based nulling interferometry with the dispersed multi-baseline GLINT instrument
Authors: Martinod Marc-Antoine , Norris Barnaby , Tuthill Peter , Lagadec Tiphaine , Jovanovic Nemanja , Cvetojevic Nick , Gross Simon , Arriola Alexander , Gretzinger Thomas , Withford Michael J. , Guyon Olivier , Lozi Julien , Vievard Sébastien , Deo Vincent , Lawrence Jon S. , Leon-Saval Sergio 
Published: April 2021

AbstractCharacterisation of exoplanets is key to understanding their formation, composition and potential for life. Nulling interferometry, combined with extreme adaptive optics, is among the most promising techniques to advance this goal. We present an integrated-optic nuller whose design is directly scalable to future science-ready interferometric nullers: the Guided-Light Interferometric Nulling Technology, deployed at the Subaru Telescope. It combines four beams and delivers spatial and spectral information. We demonstrate the capability of the instrument, achieving a null depth better than 10−3 with a precision of 10−4 for all baselines, in laboratory conditions with simulated seeing applied. On sky, the instrument delivered angular diameter measurements of stars that were 2.5 times smaller than the diffraction limit of the telescope. These successes pave the way for future design enhancements: scaling to more baselines, improved photonic component and handling low-order atmospheric aberration within the instrument, all of which will contribute to enhance sensitivity and precision.
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface
Authors: Rathee Dheeraj , Raza Haider , Roy Sujit , Prasad Girijesh 
Published: April 2021

AbstractRecent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.
COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning
Authors: Afshar Parnian , Heidarian Shahin , Enshaei Nastaran , Naderkhani Farnoosh , Rafiee Moezedin Javad , Oikonomou Anastasia , Fard Faranak Babaki , Samimi Kaveh , Plataniotis Konstantinos N. , Mohammadi Arash 
Published: April 2021

AbstractNovel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions.
In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution
Authors: Wang Fuyixue , Dong Zijing , Tian Qiyuan , Liao Congyu , Fan Qiuyun , Hoge W. Scott , Keil Boris , Polimeni Jonathan R. , Wald Lawrence L. , Huang Susie Y. , Setsompop Kawin 
Published: April 2021

AbstractWe present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.
A 3D high resolution MRI method for the visualization of cardiac fibro-fatty infiltrations
Authors: Haliot K. , Dubes V. , Constantin M. , Pernot M. , Labrousse L. , Busuttil O. , Walton R. D. , Bernus O. , Rogier J. , Nubret K. , Dos Santos P. , Benoist D. , Haïssaguerre M. , Magat J. , Quesson B. 
Published: April 2021

AbstractModifications of the myocardial architecture can cause abnormal electrical activity of the heart. Fibro-fatty infiltrations have been implicated in various cardiac pathologies associated with arrhythmias and sudden cardiac death, such as arrhythmogenic right ventricular cardiomyopathy (ARVC). Here, we report the development of an MRI protocol to observe these modifications at 9.4 T. Two fixed ex vivo human hearts, one healthy and one ARVC, were imaged with an Iterative decomposition with echo asymmetry and least-square estimations (IDEAL) and a magnetization transfer (MT) 3D sequences. The resulting fat fraction and MT ratio (MTR) were analyzed and compared to histological analysis of the three regions (“ARVC triangle”) primarily involved in ARVC structural remodeling. In the ARVC heart, high fat content was observed in the “ARVC triangle” and the superimposition of the MTR and fat fraction allowed the identification of fibrotic regions in areas without the presence of fat. The healthy heart exhibited twice less fat than the ARVC heart (31.9%, 28.7% and 1.3% of fat in the same regions, respectively). Localization of fat and fibrosis were confirmed by means of histology. This non-destructive approach allows the investigation of structural remodeling in human pathologies where fibrosis and/or fatty tissue infiltrations are expected to occur.
Motor learning by selection in visual working memory
Authors: Wagner Ilja , Wolf Christian , Schütz Alexander C. 
Published: April 2021

AbstractMotor adaptation maintains movement accuracy over the lifetime. Saccadic eye movements have been used successfully to study the mechanisms and neural basis of adaptation. Using behaviorally irrelevant targets, it has been shown that saccade adaptation is driven by errors only in a brief temporal interval after movement completion. However, under natural conditions, eye movements are used to extract information from behaviorally relevant objects and to guide actions manipulating these objects. In this case, the action outcome often becomes apparent only long after movement completion, outside the supposed temporal window of error evaluation. Here, we show that saccade adaptation can be driven by error signals long after the movement when using behaviorally relevant targets. Adaptation occurred when a task-relevant target appeared two seconds after the saccade, or when a retro-cue indicated which of two targets, stored in visual working memory, was task-relevant. Our results emphasize the important role of visual working memory for optimal movement control.
Landscape change patterns at three stages of the construction and operation of the TGP
Authors: Li Ruikang , Li Yangbing , Li Bo , Fu Dianji 
Published: April 2021

AbstractAnalyses of landscape change patterns that are based on elevation and slope can not only provide reasonable interpretations of landscape patterns but can also help to reveal evolutionary laws. However, landscape change patterns and their model in different landforms of the typical watershed in the Three Gorges Reservoir Area (TGRA) has not been quantified and assessed effectively. As a complex geographical unit, the ecological environment in the middle reach of the Yangtze River has experienced great changes due to the construction of the Three Gorges Project (TGP) and its associated human activities. Here, based mainly on a digital elevation model (DEM) and remotely sensed images from 1986, 2000, 2010, and 2017 and by using GIS technology, speeds/ trends of landscape change, the index of landscape type change intensity, landscape pattern indices, and landscape ecological security index, the spatial and temporal evolution characteristics of different elevations, slopes, and buffer landscape types were analyzed in typical watersheds, as well as an evolutionary model of the landscape pattern. The results indicated that (1) the landscape types along with the land classification and buffer zone that were influenced by the TGR construction have undergone a phased change, with the period 2000–2010 being the most dramatic period of landscape evolution during the impoundment period; (2) landscape type shifts from human-dominated farmland to nature-driven forestland and shrub-land as elevations, slopes and buffer distances increased. The landscape has shifted from diversity to relative homogeneity; (3) land types and buffer zones played essential roles in the landscape pattern index, which is reflected in the differences in landscape type indices for spatial extension and temporal characteristics. The results of this paper illustrate the spatial–temporal characteristics of various landscape types at three distinct stages in the construction of the TGR. These findings indicate that the landscape ecological security of the watershed is improving year by year. The follow-up development of the TGRA needs to consider the landscape change patterns of different landforms.
Alantolactone inhibits cervical cancer progression by downregulating BMI1
Authors: Sun Xiaodong , Xu Hongxia , Dai Tianyu , Xie Lixia , Zhao Qiang , Hao Xincai , Sun Yan , Wang Xuanbin , Jiang Nan , Sang Ming 
Published: April 2021

AbstractCervical cancer is the second most common cancer in women. Despite advances in cervical cancer therapy, tumor recurrence and metastasis remain the leading causes of mortality. High expression of BMI1 is significantly associated with poor tumor differentiation, high clinical grade, and poor prognosis of cervical cancer, and is an independent prognostic factor in cervical carcinoma. Alantolactone (AL), a sesquiterpene lactone, exhibits potent anti-inflammatory and anticancer activities. In this paper, we investigated the mechanism of AL in reducing the proliferation, migration, and invasion of HeLa and SiHa cervical cancer cells as well as its promotion of mitochondrial damage and autophagy. BMI1 silencing decreased epithelial-mesenchymal transformation-associated proteins and increased autophagy-associated proteins in HeLa cells. These effects were reversed by overexpression of BMI1 in HeLa cells. Thus, BMI1 expression is positively correlated with invasion and negatively correlated with autophagy in HeLa cells. Importantly, AL decreased the weight, volume, and BMI1 expression in HeLa xenograft tumors. Furthermore, the structure of BMI1 and target interaction of AL were virtually screened using the molecular docking program Autodock Vina; AL decreased the expression of N-cadherin, vimentin, and P62 and increased the expression of LC3B and Beclin-1 in xenograft tumors. Finally, expression of BMI1 increased the phosphorylation of STAT3, which is important for cell proliferation, survival, migration, and invasion. Therefore, we suggest that AL plays a pivotal role in inhibiting BMI1 in the tumorigenesis of cervical cancer and is a potential therapeutic agent for cervical cancer.
Synchronized resistive-pulse analysis with flow visualization for single micro- and nanoscale objects driven by optical vortex in double orifice
Authors: Nakajima Kichitaro , Nakatsuka Ryoji , Tsuji Tetsuro , Doi Kentaro , Kawano Satoyuki 
Published: April 2021

AbstractResistive-pulse analysis is a powerful tool for identifying micro- and nanoscale objects. For low-concentration specimens, the pulse responses are rare, and it is difficult to obtain a sufficient number of electrical waveforms to clearly characterize the targets and reduce noise. In this study, we conducted a periodic resistive-pulse analysis using an optical vortex and a double orifice, which repetitively senses a single micro- or nanoscale target particle with a diameter ranging from 700 nm to 2 $$\mu$$
μ
m. The periodic motion results in the accumulation of a sufficient number of waveforms within a short period. Acquired pulses show periodic ionic-current drops associated with the translocation events through each orifice. Furthermore, a transparent fluidic device allows us to synchronously average the waveforms by the microscopic observation of the translocation events and improve the signal-to-noise ratio. By this method, we succeed in distinguishing single particle diameters. Additionally, the results of measured signals and the simultaneous high-speed observations are used to quantitatively and systematically discuss the effect of the complex fluid flow in the orifices on the amplitude of the resistive pulse. The synchronized resistive-pulse analysis by the optical vortex with the flow visualization improves the pulse-acquisition rate for a single specific particle and accuracy of the analysis, refining the micro- and nanoscale object identification.
Analyzing the impact of induced magnetic flux and Fourier’s and Fick’s theories on the Carreau-Yasuda nanofluid flow
Authors: Bashir Seemab , Ramzan Muhammad , Chung Jae Dong , Chu Yu-Ming , Kadry Seifedine 
Published: April 2021

AbstractThe current study analyzes the effects of modified Fourier and Fick's theories on the Carreau-Yasuda nanofluid flow over a stretched surface accompanying activation energy with binary chemical reaction. Mechanism of heat transfer is observed in the occurrence of heat source/sink and Newtonian heating. The induced magnetic field is incorporated to boost the electric conductivity of nanofluid. The formulation of the model consists of nonlinear coupled partial differential equations that are transmuted into coupled ordinary differential equations with high nonlinearity by applying boundary layer approximation. The numerical solution of this coupled system is carried out by implementing the MATLAB solver bvp4c package. Also, to verify the accuracy of the numerical scheme grid-free analysis for the Nusselt number is presented. The influence of different parameters, for example, reciprocal magnetic Prandtl number, stretching ratio parameter, Brownian motion, thermophoresis, and Schmidt number on the physical quantities like velocity, temperature distribution, and concentration distribution are addressed with graphs. The Skin friction coefficient and local Nusselt number for different parameters are estimated through Tables. The analysis shows that the concentration of nanoparticles increases on increasing the chemical reaction with activation energy and also Brownian motion efficiency and thermophoresis parameter increases the nanoparticle concentration. Opposite behavior of velocity profile and the Skin friction coefficient is observed for increasing the stretching ratio parameter. In order to validate the present results, a comparison with previously published results is presented. Also, Factors of thermal and solutal relaxation time effectively contribute to optimizing the process of stretchable surface chilling, which is important in many industrial applications.
Using supraglottic airways by paramedics for airway management in analogue microgravity increases speed and success of ventilation
Authors: Hinkelbein Jochen , Ahlbäck Anton , Antwerber Christine , Dauth Lisa , DuCanto James , Fleischhammer Elisabeth , Glatz Carlos , Kerkhoff Steffen , Mathes Alexander , Russomano Thais , Schmitz Jan , Starck Clement , Thierry Seamus , Warnecke Tobias 
Published: April 2021

AbstractIn the next few years, the number of long-term space missions will significantly increase. Providing safe concepts for emergencies including airway management will be a highly challenging task. The aim of the present trial is to compare different airway management devices in simulated microgravity using a free-floating underwater scenario. Five different devices for airway management [laryngeal mask (LM), laryngeal tube (LT), I-GEL, direct laryngoscopy (DL), and video laryngoscopy (VL)] were compared by n = 20 paramedics holding a diving certificate in a randomized cross-over setting both under free-floating conditions in a submerged setting (pool, microgravity) and on ground (normogravity). The primary endpoint was the successful placement of the airway device. The secondary endpoints were the number of attempts and the time to ventilation. A total of 20 paramedics (3 female, 17 male) participated in this study. Success rate was highest for LM and LT and was 100% both during simulated microgravity and normogravity followed by the I-GEL (90% during microgravity and 95% during normogravity). However, the success rate was less for both DL (60% vs. 95%) and VL (20% vs. 60%). Fastest ventilation was performed with the LT both in normogravity (13.7 ± 5.3 s; n = 20) and microgravity (19.5 ± 6.1 s; n = 20). For the comparison of normogravity and microgravity, time to ventilation was shorter for all devices on the ground (normogravity) as compared underwater (microgravity). In the present study, airway management with supraglottic airways and laryngoscopy was shown to be feasible. Concerning the success rate and time to ventilation, the optimum were supraglottic airways (LT, LM, I-GEL) as their placement was faster and associated with a higher success rate. For future space missions, the use of supraglottic airways for airway management seems to be more promising as compared to tracheal intubation by DL or VL.
Fleeing lockdown and its impact on the size of epidemic outbreaks in the source and target regions – a COVID-19 lesson
Authors: Barbarossa Maria Vittoria , Bogya Norbert , Dénes Attila , Röst Gergely , Varma Hridya Vinod , Vizi Zsolt 
Published: April 2021

AbstractThe COVID-19 pandemic forced authorities worldwide to implement moderate to severe restrictions in order to slow down or suppress the spread of the disease. It has been observed in several countries that a significant number of people fled a city or a region just before strict lockdown measures were implemented. This behavior carries the risk of seeding a large number of infections all at once in regions with otherwise small number of cases. In this work, we investigate the effect of fleeing on the size of an epidemic outbreak in the region under lockdown, and also in the region of destination. We propose a mathematical model that is suitable to describe the spread of an infectious disease over multiple geographic regions. Our approach is flexible to characterize the transmission of different viruses. As an example, we consider the COVID-19 outbreak in Italy. Projection of different scenarios shows that (i) timely and stricter intervention could have significantly lowered the number of cumulative cases in Italy, and (ii) fleeing at the time of lockdown possibly played a minor role in the spread of the disease in the country.