bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prebiotic studies on the interaction of zirconia nanoparticles and ribose nucleotides and their role in chemical evolution
Authors: Arora Avnish Kumar , Kumar Pankaj 
Published: February 2021

AbstractStudies on the interaction of biomolecules with inorganic compounds, mainly mineral surfaces, are of great concern in identifying their role in chemical evolution and origins of life. Metal oxides are the major constituents of earth and earth-like planets. Hence, studies on the interaction of biomolecules with these minerals are the point of concern for the study of the emergence of life on different planets. Zirconium oxide is one of the metal oxides present in earth's crust as it is a part of several types of rocks found in sandy areas such as beaches and riverbeds, e.g. pebbles of baddeleyite. Different metal oxides have been studied for their role in chemical evolution but no studies have been reported about the role of zirconium oxide in chemical evolution and origins of life. Therefore, studies were carried out on the interaction of ribonucleic acid constituents, 5′-CMP (cytidine monophosphate), 5′-UMP (uridine monophosphate), 5′-GMP (guanosine monophosphate) and 5′-AMP (adenosine monophosphate), with zirconium oxide. Synthesized zirconium oxide particles were characterized by using vibrating sample magnetometer, X-Ray Diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy. Zirconia particles were in the nanometre range, from 14 to 27 nm. The interaction of zirconium oxide with ribonucleic acid constituents was performed in the concentration range of 5 × 10−5–300 × 10−5 M. Interaction studies were carried out in three mediums; acidic (pH 4.0), neutral (pH 7.0) and basic (pH 9.0). At neutral pH, maximum interaction was observed. The interaction of zirconium oxide with 5′-UMP was 49.45% and with 5′-CMP 67.98%, while with others it was in between. Interaction studies were Langmurian in nature. Xm and KL values were calculated. Infrared spectral studies of ribonucleotides, metal oxide and ribonucleotide–metal oxide adducts were carried out to find out the interactive sites. It was observed that the nitrogen base and phosphate moiety of ribonucleotides interact with the positive charge surface of metal oxide. SEM was also carried out to study the adsorption. The results of the present study favour the important role of zirconium oxide in concentrating the organic molecules from their dilute aqueous solutions in primeval seas.
Response to “Comment on the Paper “Characteristic Time Scales of Decadal to Centennial Changes in Global Surface Temperatures Over the Past 150 years” by Y. Cuypers, F. Codron, and M. Crepon”
Authors: Le Mouël J. L. , Lopes F. , Courtillot V. 
Published: February 2021

AbstractWe thank the authors (hereafter referred to as CCC) for providing us an opportunity to clarify some points of our original paper. CCC list in their abstract three “key points”that we respond to in this Reply. The first comment is the central one and the most developed. It deals mainly with discussion of features of methods of spectral analysis, mainly SSA. We have quoted the sub‐parts of that comment as items 1a to 1k. The replies to comments/key points 2 and 3 are shorter. We disagree with most of the comments by CCC and explain why. We conclude that we have successfully countered CCC's criticism and shown many of their points to be unsubstantiated. The main problem seems to reside in differences concerning the literature on Singular Spectral Analysis and our use of it. Much of our response to the comments can be found in textbooks and review papers on SSA and time series analysis; we quote extensively, both in our original paper and in this response to CCC, Golyandina and Zhigljavsky (2013).
Understanding communicative intentions in schizophrenia using an error analysis approach
Authors: Parola Alberto , Brasso Claudio , Morese Rosalba , Rocca Paola , Bosco Francesca M. 
Published: February 2021

AbstractPatients with schizophrenia (SCZ) have a core impairment in the communicative-pragmatic domain, characterized by severe difficulties in correctly inferring the speaker’s communicative intentions. While several studies have investigated pragmatic performance of patients with SCZ, little research has analyzed the errors committed in the comprehension of different communicative acts. The present research investigated error patterns in 24 patients with SCZ and 24 healthy controls (HC) during a task assessing the comprehension of different communicative acts, i.e., sincere, deceitful and ironic, and their relationship with the clinical features of SCZ. We used signal detection analysis to quantify participants’ ability to correctly detect the speakers’ communicative intention, i.e., sensitivity, and their tendency to wrongly perceive a communicative intention when not present, i.e., response bias. Further, we investigated the relationship between sensitivity and response bias, and the clinical features of the disorder, namely symptom severity, pharmacotherapy, and personal and social functioning. The results showed that the ability to infer the speaker’s communicative intention is impaired in SCZ, as patients exhibited lower sensitivity, compared to HC, for all the pragmatic phenomena evaluated, i.e., sincere, deceitful, and ironic communicative acts. Further, we found that the sensitivity measure for irony was related to disorganized/concrete symptoms. Moreover, patients with SCZ showed a stronger response bias for deceitful communicative acts compared to HC: when committing errors, they tended to misattribute deceitful intentions more often than sincere and ironic ones. This tendency to misattribute deceitful communicative intentions may be related to the attributional bias characterizing the disorder.
Avolition as the core negative symptom in schizophrenia: relevance to pharmacological treatment development
Authors: Strauss Gregory P. , Bartolomeo Lisa A. , Harvey Philip D. 
Published: February 2021

AbstractNegative symptoms have long been considered a core component of schizophrenia. Modern conceptualizations of the structure of negative symptoms posit that there are at least two broad dimensions (motivation and pleasure and diminished expression) or perhaps five separable domains (avolition, anhedonia, asociality, blunted affect, alogia). The current review synthesizes a body of emerging research indicating that avolition may have a special place among these dimensions, as it is generally associated with poorer outcomes and may have distinct neurobiological mechanisms. Network analytic findings also indicate that avolition is highly central and interconnected with the other negative symptom domains in schizophrenia, and successfully remediating avolition results in global improvement in the entire constellation of negative symptoms. Avolition may therefore reflect the most critical treatment target within the negative symptom construct. Implications for targeted treatment development and clinical trial design are discussed.
Integrating resource recovery process and watershed modelling to facilitate decision-making regarding bio-fertilizer production and application
Authors: Vaneeckhaute Céline 
Published: February 2021

AbstractWaste management strategies such as anaerobic digestion and composting produce bio-based fertilizer products that could be applied to agricultural soil. Although multiple modelling software tools are available to simulate the environmental effect of fertilizer application to the soil, these models do not allow specification of emerging bio-based fertilizer types. Moreover, mathematical process models exist that allow optimizing the operational settings of waste management processes in order to produce an optimal bio-fertilizer quality adjusted to the local market needs. If an integrated tool would be available that couples process modelling to watershed modelling, the valorization chain could be simulated as a whole, i.e. the bio-fertilizer type and composition could be adjusted to the local watershed and environmental impacts of bio-based fertilizer production and application could more easily be assessed and controlled. The availability of such integrated tool may as such allow for improved decision and policy making regarding bio-fertilizer production and application with environmental benefits as a result.
Scalable distributed gate-model quantum computers
Authors: Gyongyosi Laszlo , Imre Sandor 
Published: February 2021

AbstractA scalable model for a distributed quantum computation is a challenging problem due to the complexity of the problem space provided by the diversity of possible quantum systems, from small-scale quantum devices to large-scale quantum computers. Here, we define a model of scalable distributed gate-model quantum computation in near-term quantum systems of the NISQ (noisy intermediate scale quantum) technology era. We prove that the proposed architecture can maximize an objective function of a computational problem in a distributed manner. We study the impacts of decoherence on distributed objective function evaluation.
Fluid–structure interactions (FSI) based study of low-density lipoproteins (LDL) uptake in the left coronary artery
Authors: Chen Xueping , Zhuang Jian , Huang Huanlei , Wu Yueheng 
Published: February 2021

AbstractThe purpose of this study is to compare the effect of the different physical factors on low-density lipoproteins (LDL) accumulation from flowing blood to the arterial wall of the left coronary arteries. The three-dimensional (3D) computational model of the left coronary arterial tree is reconstructed from a patient-specific computed tomography angiography (CTA) image. The endothelium of the coronary artery is represented by a shear stress dependent three-pore model. Fluid–structure interaction ($$FSI$$

FSI

) based numerical method is used to study the LDL transport from vascular lumen into the arterial wall. The results show that the high elastic property of the arterial wall decreases the complexity of the local flow field in the coronary bifurcation system. The places of high levels of LDL uptake coincide with the regions of low wall shear stress. In addition, hypertension promotes LDL uptake from flowing blood in the arterial wall, while the thickened arterial wall decreases this process. The present computer strategy combining the methods of coronary CTA image 3D reconstruction, $$FSI$$

FSI

simulation, and three-pore modeling was illustrated to be effective on the simulation of the distribution and the uptake of LDL. This may have great potential for the early prediction of the local atherosclerosis lesion in the human left coronary artery.
Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks
Authors: Sammani Arjan , Bagheri Ayoub , van der Heijden Peter G. M. , te Riele Anneline S. J. M. , Baas Annette F. , Oosters C. A. J. , Oberski Daniel , Asselbergs Folkert W. 
Published: February 2021

AbstractStandard reference terminology of diagnoses and risk factors is crucial for billing, epidemiological studies, and inter/intranational comparisons of diseases. The International Classification of Disease (ICD) is a standardized and widely used method, but the manual classification is an enormously time-consuming endeavor. Natural language processing together with machine learning allows automated structuring of diagnoses using ICD-10 codes, but the limited performance of machine learning models, the necessity of gigantic datasets, and poor reliability of terminal parts of these codes restricted clinical usability. We aimed to create a high performing pipeline for automated classification of reliable ICD-10 codes in the free medical text in cardiology. We focussed on frequently used and well-defined three- and four-digit ICD-10 codes that still have enough granularity to be clinically relevant such as atrial fibrillation (I48), acute myocardial infarction (I21), or dilated cardiomyopathy (I42.0). Our pipeline uses a deep neural network known as a Bidirectional Gated Recurrent Unit Neural Network and was trained and tested with 5548 discharge letters and validated in 5089 discharge and procedural letters. As in clinical practice discharge letters may be labeled with more than one code, we assessed the single- and multilabel performance of main diagnoses and cardiovascular risk factors. We investigated using both the entire body of text and only the summary paragraph, supplemented by age and sex. Given the privacy-sensitive information included in discharge letters, we added a de-identification step. The performance was high, with F1 scores of 0.76–0.99 for three-character and 0.87–0.98 for four-character ICD-10 codes, and was best when using complete discharge letters. Adding variables age/sex did not affect results. For model interpretability, word coefficients were provided and qualitative assessment of classification was manually performed. Because of its high performance, this pipeline can be useful to decrease the administrative burden of classifying discharge diagnoses and may serve as a scaffold for reimbursement and research applications.
Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies
Authors: Gunasekeran Dinesh Visva , Tseng Rachel Marjorie Wei Wen , Tham Yih-Chung , Wong Tien Yin 
Published: February 2021

AbstractThe coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n = 111/247) and big data analytics (36.0%, n = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n = 4/247), and digital structural screening (DS) (8.9%, n = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.
Specific PIP2 binding promotes calcium activation of TMEM16A chloride channels
Authors: Jia Zhiguang , Chen Jianhan 
Published: February 2021

AbstractTMEM16A is a widely expressed Ca2+-activated Cl−channel that regulates crucial physiological functions including fluid secretion, neuronal excitability, and smooth muscle contraction. There is a critical need to understand the molecular mechanisms of TMEM16A gating and regulation. However, high-resolution TMEM16A structures have failed to reveal an activated state with an unobstructed permeation pathway even with saturating Ca2+. This has been attributed to the requirement of PIP2for preventing TMEM16A desensitization. Here, atomistic simulations show that specific binding of PIP2to TMEM16A can lead to spontaneous opening of the permeation pathway in the Ca2+-bound state. The predicted activated state is highly consistent with a wide range of mutagenesis and functional data. It yields a maximal Cl−conductance of ~1 pS, similar to experimental estimates, and recapitulates the selectivity of larger SCN−over Cl−. The resulting molecular mechanism of activation provides a basis for understanding the interplay of multiple signals in controlling TMEM16A channel function.
Photo-induced weak base-catalyzed synthesis of α-haloboronates from vinylboronates and polyfluoroalkyl halides
Authors: Fang Tongchang , Qiu Jian , Yang Kai , Song Qiuling 
Published: February 2021

An unprecedented strategy for the synthesis of sp2-α-haloboronates has been developed. Unique KOAc catalytic system, high synthetic application value of the product and no participation of metal constitute the notable features of this reaction.
Ensemble learning‐based classification of microarray cancer data on tree‐based features
Authors: Dagnew Guesh , Shekar B.H. 
Published: February 2021

AbstractCancer is a group of related diseases with high mortality rate characterized by abnormal cell growth which attacks the body tissues. Microarray cancer data is a prominent research topic across many disciplines focused to address problems related to the higher curse of dimensionality, a small number of samples, noisy data and imbalance class. A random forest (RF) tree‐based feature selection and ensemble learning based on hard voting and soft voting is proposed to classify microarray cancer data using six different base classifiers. The selected features due to RF tree are submitted to the base classifiers as the training set. Then, an ensemble learning method is applied to the base classifiers in which case each base classifier predicts class label individually. The final prediction is carried out hard and soft voting techniques that use majority voting and weighted probability on the test set. The proposed ensemble learning method is validated on eight different standard microarray cancer datasets, of which three of the datasets are binary class and the remaining five datasets are multi‐class datasets. Experimental results of the proposed method show 1.00 classification accuracy on six of the datasets and 0.96 on two of the datasets.
Relay selection for spatially random full‐duplex cooperative non‐orthogonal multiple access networks
Authors: Wang Xinyu , Jia Min , Ho Ivan Wang‐Hei , Guo Qing , Lau Francis Chung‐Ming 
Published: February 2021

AbstractThis paper investigates the relay selection problem and proposes a three‐stage relay selection strategy with power allocation (TRSPA) for a spectrum‐sensing‐based full‐duplex (FD) user relaying cooperative non‐orthogonal multiple access (CNOMA) scheme. Uniformly‐distributed strong user relays in the investigated scheme help a weak user communicates with the base station in an efficient and reliable way. The proposed TRSPA strategy maximizes the transmission data rate of the selected relay while ensuring successful transmissions for the weak user by precisely narrowing down relay candidates step‐by‐step and dynamically allocating optimal power coefficients. Exact and asymptotic outage probabilities and ergodic rates are worked out. Accordingly, diversity orders and spatial multiplexing gains are derived. We further exploit the impact of self‐interference (SI) on TRSPA for FD‐CNOMA and then compare its performance with TRSPA applied in other relaying modes, that is half‐duplex and orthogonal multiple access. Finally, simulation results reveal that: (i) theoretical derivation results are correct; (ii) TRSPA always outperforms other relay selection strategies in terms of outage probability and ergodic rate; and (iii) TRSPA for FD‐CNOMA in a real‐world scenario achieves better performance than other relaying modes in spite of the adverse effect of SI in FD mode.
A novel denoising algorithm for medical images based on the non‐convex non‐local similar adaptive regularization
Authors: Tian Lin , Miao Jiaqing , Zhou Xiaobing , Wang Chao 
Published: February 2021

AbstractSparse representation is a powerful statistical image modelling technique and has been successfully applied to image denoising. For a given patch, a non‐convex non‐local similarity adaptive method is adopted for sparse representation of images. First, it uses the autoregressive model to perform dictionary learning from sample patch datasets. Second, the sparse representation of an image introduces non‐convex non‐local self‐similarity as the regularization term. In order to make better use of the sparse regularization method for image denoising, the parameters used in this study are estimated using adaptive methods. This model is more efficient and accurate, Compared with K‐means singular value decomposition (KSVD) algorithm, a generalized K‐means clustering method, total variation of population sparsity (GSTV) algorithm, adaptive sparse domain selection (ASDS) algorithm, forward denoising convolutional neural network (DnCNNs), a fast and flexible Convolutional Neural Network image denoising method (FFNNet) and operator‐splitting algorithm to minimize the Euler elastica functional (OSEEF). Image noise‐reduction experiments confirmed that using the adaptive regularization method, the results in peak signal to noise ratio (PSNR) and visual opinion are better than other algorithms.
Quasi‐3D magnetic field simulation of superconducting devices with translational symmetry
Authors: D'Angelo Laura A. M. , De Gersem Herbert 
Published: February 2021

AbstractThis work presents a quasi‐three‐dimensional (Q3D) approach for the magnetic field simulation in superconducting devices. First‐order two‐dimensional finite‐element edge functions in the model's cross‐section are combined with one‐dimensional orthogonal polynomials along the longitudinal direction. The interfilament coupling currents arising in superconducting multi‐filament materials are modelled by taking the associated magnetization into account. For this formulation, the Q3D ansatz is elaborated, verificated and applied to a superconducting cable model. In the end, the approach is compared to a conventional three‐dimensional finite‐element method against which the proposed Q3D method demonstrates a superior computational efficiency.
L’imagerie numérique : outil de la gestion thérapeutique des dents incluses
Authors: Le Gall Michel , Dubernard Charles , Philip-Alliez Camille 
Published: February 2021

Le traitement avec dégagement chirurgical et mise en place orthodontique de canines incluses étant complexe, la décision thérapeutique finale doit s’appuyer sur une concertation pluridisciplinaire impliquant l’orthodontiste et le praticien responsable de l’acte chirurgical. Pour améliorer le pronostic et réduire les échecs et ré-interventions, une appréciation initiale précise de la localisation de la dent, de la présence de résorption initiale, de la direction de traction et de l’ancrage nécessaire, est indispensable.
Lorsque l’imagerie conventionnelle ne fournit pas toutes les informations souhaitées, une analyse tridimensionnelle peut être recommandée afin de mieux localiser une dent incluse et mieux visualiser les structures associées. La sensibilité diagnostique de la radiologie conventionnelle étant peu élevée pour la détection de résorptions des dents adjacentes, le scanner augmente celle-ci de 50% en moyenne. Le cone beam peut être proposéafin d’améliorer la prise en charge du patient en se substituant au scanner plus irradiant.
L’imagerie médicale en reconstruction 3D est non seulement une aide pour l’orthodontiste de façon à visualiser et mettre en place la mécanique la plus adaptée compte tenu du contexte mais sert aussi au praticien qui va réaliser l’acte de façon à optimiser sa procédure de dégagement (respect des structures environnantes) pour le bien du patient.