bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fe K-edge x-ray absorption spectroscopy of corrosion phases of archaeological iron: results, limitations, and the need for complementary techniques
Authors: Simon Hayley , Cibin Giannantonio , Freestone Ian , Schofield Eleanor 
Published: June 2021

Abstract
Data analysis methods for iron x-ray absorption spectroscopy can provide extensive information about the oxidation state and co-ordination of an Fe-species. However, the extent to which techniques developed using a single-phase iron sample may be applied to complex, mixed-phase samples formed under real-world conditions is not clear. This work uses a combination of pre-edge fitting and linear combination analysis to characterise the near edge region of the x-ray absorption spectrum (XANES) for a set of archaeological iron corrosion samples from a collection of cast iron cannon shot excavated from the Mary Rose shipwreck and compares the data with phase compositions determined by synchrotron x-ray powder diffraction (SXPD). Archaeological powder and cross-section samples were compared to a library of iron standards and diffraction data. The XANES are consistent with previous observations that generation of the chlorinated phase akaganeite, β-FeO(OH,Cl), occurs in those samples which have been removed form passive storage and subjected to active conservation. However, the results show that if any metallic species is present in the sample, the contribution from Fe(0) to the spectral region containing a pre-edge for oxidised iron—Fe(II) and Fe(III)—causes the analysis to be less effective and the conclusions unreliable. Consequently, while the pre-edge fitting methodology may be applied to a mixture of iron oxides or oxyhydroxides, the procedure is inappropriate for a mixed metal-oxide sample without the application of a complimentary technique, such as SXPD.
Bending bad—testing caramel wafer bars (#TestATunnocks)
Authors: Birch Stephen M I , Bates Joanna , Bell Michael , Christofidou Katerina A. , Corkhill Claire L , Hearnden Vanessa , Lopez Elisa Alonso , Mason Louise C , Southworth Amanda , Dean Julian S 
Published: June 2021

Abstract
During the coronavirus pandemic, there have been significant challenges in the remote teaching and demonstration of experiments, especially those that require laboratory testing equipment. With a desire to give students a feel for our materials laboratory on open days and allow them to gain a deeper understanding of what materials science and engineering is about, we have designed an experiment focused on composite materials that can be performed remotely and without specialist equipment. This enabled students to experience a bend test sensorily through seeing, hearing and feeling it, creating a strong link to then being able to relate it to the pre-prepared experimental data taken in the laboratory. This fun, easy-to-run and engaging experiment allowed a shared experience and encouraged a discussion about students’ observations, differences in results and implications of the bend strength of sandwich composites. We have found it not only works well universally by all ages but can be used with younger children to think about words such as ‘stronger’, ‘stiffer’ and ‘flexible’ and how materials can be different in different directions.
Identification of 27 abnormalities from multi-lead ECG signals: an ensembled SE_ResNet framework with Sign Loss function
Authors: Zhu Zhaowei , Lan Xiang , Zhao Tingting , Guo Yangming , Kojodjojo Pipin , Xu Zhuoyang , Liu Zhuo , Liu Siqi , Wang Han , Sun Xingzhi , Feng Mengling 
Published: June 2021

Abstract

Objective. Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. Our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG databases. Approach. Firstly, a series of pre-processing methods were proposed and applied on various data sources in order to mitigate the problem of data divergence. Secondly, we ensembled two SE_ResNet models and one rule-based model to enhance the performance of various ECG abnormalities’ classification. Thirdly, we introduce a Sign Loss to tackle the problem of class imbalance, and thus improve the model's generalizability. Main results. In the PhysioNet/Computing in Cardiology Challenge (2020), our proposed approach achieved a challenge validation score of 0.682, and a full test score of 0.514, placed us 3rd out of 40 in the official ranking. Significance. We proposed an accurate and robust predictive framework that combines deep neural networks and clinical knowledge to automatically classify multiple ECG abnormalities. Our framework is able to identify 27 ECG abnormalities from multi-lead ECG signals regardless of discrepancies in data sources and the imbalance of data labeling. We trained our framework on five datasets and validated it on six datasets from various countries. The outstanding performance demonstrate the effectiveness of our proposed framework.
Hydrogen Sulphide (H2S) Exposure Hazard Assessment: An Algorithm for Generating Exposure Index Based on Direct Instrument Readings
Authors: Austigard Åse Dalseth , Smedbold Hans Thore 
Published: June 2021

AbstractObjectivesIncreased use of small affordable alarm sensors with logging or network capabilities has improved the ability to monitor exposure. The large datasets generated from these monitors calls for development of a computer algorithm to assess these data.MethodsWe examined 88 time series of hydrogen sulphide (H2S) from wastewater works previously used for developing the exposure index. The time series covered 331 h, where 16 h had readings different from zero.ResultsThe developed algorithm reproduced the manual assessed index almost perfectly (linear regression β = 1.02, R2 = 0.97, P &lt; 0.001). Time-weighted average (TWA) values of the 88 time series showed a mean value of 0.04 ppm (range 0.0–0.9). The mean index value was 18 (range 0–337), with a good linear fit (β = 0.002, R2 = 0.93, and P &lt; 0.001). The index gave us a better resolution and basis for risk assessment than the TWA, and managed to combine evaluation of TWA and exceedance of ceiling value in one number.ConclusionsAs long as peaks above ceiling value occur, we find alarm tools with an H2S sensor to be an essential personal protective equipment against H2S. The proposed method has been verified, and it removes some common human errors in graph evaluation. Use of the index is a possible way of quantifying risk level in exposure to H2S in one single number and provides better understanding of the risk of exposure, as it eases the analysis and evaluation of large numbers of time series.
Recent ecophysiological, biochemical and evolutional insights into plant carnivory
Authors: Adamec Lubomír , Matušíková Ildikó , Pavlovič Andrej 
Published: June 2021

AbstractBackgroundCarnivorous plants are an ecological group of approx. 810 vascular species which capture and digest animal prey, absorb prey-derived nutrients and utilize them to enhance their growth and development. Extant carnivorous plants have evolved in at least ten independent lineages, and their adaptive traits represent an example of structural and functional convergence. Plant carnivory is a result of complex adaptations to mostly nutrient-poor, wet and sunny habitats when the benefits of carnivory exceed the costs. With a boost in interest and extensive research in recent years, many aspects of these adaptations have been clarified (at least partly), but many remain unknown.ScopeWe provide some of the most recent insights into substantial ecophysiological, biochemical and evolutional particulars of plant carnivory from the functional viewpoint. We focus on those processes and traits in carnivorous plants associated with their ecological characterization, mineral nutrition, cost–benefit relationships, functioning of digestive enzymes and regulation of the hunting cycle in traps. We elucidate mechanisms by which uptake of prey-derived nutrients leads to stimulation of photosynthesis and root nutrient uptake.ConclusionsUtilization of prey-derived mineral (mainly N and P) and organic nutrients is highly beneficial for plants and increases the photosynthetic rate in leaves as a prerequisite for faster plant growth. Whole-genome and tandem gene duplications brought gene material for diversification into carnivorous functions and enabled recruitment of defence-related genes. Possible mechanisms for the evolution of digestive enzymes are summarized, and a comprehensive picture on the biochemistry and regulation of prey decomposition and prey-derived nutrient uptake is provided.
Optimizing blood–brain barrier permeation through deep reinforcement learning for de novo drug design
Authors: Pereira Tiago , Abbasi Maryam , Oliveira José Luis , Ribeiro Bernardete , Arrais Joel 
Published: June 2021

Abstract

Motivation
The process of placing new drugs into the market is time-consuming, expensive and complex. The application of computational methods for designing molecules with bespoke properties can contribute to saving resources throughout this process. However, the fundamental properties to be optimized are often not considered or conflicting with each other. In this work, we propose a novel approach to consider both the biological property and the bioavailability of compounds through a deep reinforcement learning framework for the targeted generation of compounds. We aim to obtain a promising set of selective compounds for the adenosine A2A receptor and, simultaneously, that have the necessary properties in terms of solubility and permeability across the blood–brain barrier to reach the site of action. The cornerstone of the framework is based on a recurrent neural network architecture, the Generator. It seeks to learn the building rules of valid molecules to sample new compounds further. Also, two Predictors are trained to estimate the properties of interest of the new molecules. Finally, the fine-tuning of the Generator was performed with reinforcement learning, integrated with multi-objective optimization and exploratory techniques to ensure that the Generator is adequately biased.

Results
The biased Generator can generate an interesting set of molecules, with approximately 85% having the two fundamental properties biased as desired. Thus, this approach has transformed a general molecule generator into a model focused on optimizing specific objectives. Furthermore, the molecules’ synthesizability and drug-likeness demonstrate the potential applicability of the de novo drug design in medicinal chemistry.

Availability and implementation
All code is publicly available in the https://github.com/larngroup/De-Novo-Drug-Design.

Supplementary information
Supplementary data are available at Bioinformatics online.
MNHN-Tree-Tools: a toolbox for tree inference using multi-scale clustering of a set of sequences
Authors: Haschka Thomas , Ponger Loic , Escudé Christophe , Mozziconacci Julien 
Published: June 2021

Abstract

Summary
Genomic sequences are widely used to infer the evolutionary history of a given group of individuals. Many methods have been developed for sequence clustering and tree building. In the early days of genome sequencing, these were often limited to hundreds of sequences but due to the surge of high throughput sequencing, it is now common to have millions of sampled sequences at hand. We introduce MNHN-Tree-Tools, a high performance set of algorithms that builds multi-scale, nested clusters of sequences found in a FASTA file. MNHN-Tree-Tools does not rely on multiple sequence alignment and can thus be used on large datasets to infer a sequence tree. Herein, we outline two applications: a human alpha-satellite repeats classification and a tree of life derivation from 16S/18S rDNA sequences.

Availability and implementation
Open source with a Zlib License via the Git protocol: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools.

Manual
A detailed users guide and tutorial: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools-manual/-/raw/master/manual.pdf.

Website and FAQ
http://treetools.haschka.net.

Supplementary information
Supplementary data are available at Bioinformatics online.
Smart homes: potentials and challenges
Authors: El-Azab Rasha 
Published: June 2021

Abstract
Decentralized distributed clean-energy sources have become an essential need for smart grids to reduce the harmful effects of conventional power plants. Smart homes with a suitable sizing process and proper energy-management schemes can share in reducing the whole grid demand and even sell clean energy to the utility. Smart homes have been introduced recently as an alternative solution to classical power-system problems, such as the emissions of thermal plants and blackout hazards due to bulk plants/transmission outages. The appliances, sources and energy storage of smart homes should be coordinated with the requirements of homeowners via a suitable energy-management scheme. Energy-management systems are the main key to optimizing both home sources and the operation of loads to maximize home-economic benefits while keeping a comfortable lifestyle. The intermittent uncertain nature of smart homes may badly affect the whole grid performance. The prospective high penetration of smart homes on a smart power grid will introduce new, unusual scenarios in both generation and loading. In this paper, the main features and requirements of smart homes are defined. This review aims also to address recent proposed smart-home energy-management schemes. Moreover, smart-grid challenges with a high penetration of smart-home power are discussed.
Phase Resetting in the Anterior Cingulate Cortex Subserves Childhood Attention and Is Impaired by Epilepsy
Authors: Wong Simeon M , Arski Olivia N , Warsi Nebras M , Pang Elizabeth W , Kerr Elizabeth , Smith Mary Lou , Dunkley Benjamin T , Ochi Ayako , Otsubo Hiroshi , Sharma Roy , Jain Puneet , Donner Elizabeth , Snead O Carter , Ibrahim George M 
Published: June 2021

AbstractThe neural mechanisms that underlie selective attention in children are poorly understood. By administering a set-shifting task to children with intracranial electrodes stereotactically implanted within anterior cingulate cortex (ACC) for epilepsy monitoring, we demonstrate that selective attention in a set-shifting task is dependent upon theta-band phase resetting immediately following stimulus onset and that the preferred theta phase angle is predictive of reaction time during attentional shift. We also observe selective enhancement of oscillatory coupling between the ACC and the dorsal attention network and decoupling with the default mode network during task performance. When transient focal epileptic activity occurs around the time of stimulus onset, phase resetting is impaired, connectivity changes with attentional and default mode networks are abolished, and reaction times are prolonged. The results of the present work highlight the fundamental mechanistic role of oscillatory phase in ACC in supporting attentional circuitry and present novel opportunities to remediate attention deficits in children with epilepsy.
Importance of blood pressure control in Kawasaki disease with expanded multiple giant coronary aneurysms with a 32 mm maximum diameter: a case report
Authors: Moritoh Yuji , Kamada Masahiro , Matsumoto Shinsaku , Kido Koji 
Published: June 2021

Abstract

Background
Ruptured coronary artery aneurysm is rare, but the most serious complications of an acute phase of Kawasaki disease (KD) with giant coronary artery aneurysm (GCAA). Progressive or super GCAA, which rapidly dilates and continue to increase over a diameter of 10 mm, are more susceptible to rupture.

Case summary
We report the case of a 6-year-old boy with KD who had multiple super GCAAs with a high risk of GCAA rupture. On admission to our hospital, he presented with fever, chest pain, and Stage II hypertension. Echocardiographic Z-scores adjusted for body surface area were used for measurements. The coronary artery diameter of segment 1 was 24.3 mm with a Z-score of 20.8; the diameter of segment 3 was 24.4 mm; the diameter of the left anterior descending branch was 32.6 mm with a Z-score of 20.1. The super GCAAs showed a tendency to expand compared to the latest echocardiography, and thrombus formation was observed in the super GCAA of segment 3. The patient was treated with anti-inflammatory therapy, antithrombotic therapy, and antihypertensive therapy with continuous arterial pressure monitoring with the goal of not exceeding the 5th percentile of the normal standard during the period when there was a risk of progressive coronary aneurysm expansion. He was discharged without any neurological complications.

Discussion
We speculated that the patient's hypertension was the cause of an expanding coronary artery aneurysm. In conclusion, KD patients with super GCAA may benefit from aggressive blood pressure control with continuous arterial pressure monitoring.
Cyclical Government Spending: Theory and Empirics
Authors: Roulleau-Pasdeloup Jordan 
Published: June 2021

AbstractThis paper shows that part of what is usually labelled discretionary government spending actually varies systematically over the cycle. I exploit the pervasive gap between ordinary least squares and two-stage least squares local government spending multipliers to estimate how cyclical the systematic part of government spending is. Estimating a structural open-economy New Keynesian model on United States state-level data, I find that when employment decreases by $1\%$, the systematic component of government spending decreases by $0.23\%$. I also find that the empirical specification in Nakamura and Steinsson, ‘Fiscal Stimulus in a monetary union’ (American Economic Review, 2014) does a good job in recovering the true impact multiplier effect, but that it overestimates the long-run cumulative effect.
‘You Felt Disembodied’: Reconfiguring Vulnerabilities Through Metalepsis in Harry Parker’s Anatomy of A Soldier
Authors: Marcellin Katia 
Published: June 2021

Abstract
Harry Parker’s first novel, Anatomy of a Soldier (2016), is a trauma narrative intermingling several timelines and told from the perspectives of forty-four object-narrators. This produces temporal and focal shifts in the narrative, which occur through stylistic metalepsis, that is, through discrepancies that blur the boundaries between causes and effects or between entities. This article proposes to examine how the use of metalepsis in this novel reconfigures the representation of trauma. Through metalepsis, Parker’s novel seeks to come as close as possible to the reality of the traumatized psyche, to materialize the space left blank by the shattering force of the traumatic event. Yet, beyond that representational issue, metalepsis also fulfils an ethical function in the novel: by performing rather than showing the workings of trauma, the narrative produces, and includes the reader in, a fragile yet tangible community of shared vulnerabilities.
Factors influencing health-related quality of life of patients with a left ventricular assist device: a systematic review and thematic synthesis
Authors: Levelink Michael , Brütt Anna Levke 
Published: June 2021

Abstract

Aims
Left ventricular assist devices (LVADs) are increasingly being used in the treatment of advanced heart failure. Left ventricular assist device patients frequently report improvements in health-related quality of life (HRQOL) post-implantation. However, HRQOL varies over time and between patients, which can be explained by patient-related and environmental factors, as individual studies suggest. This review aims to synthesize qualitative evidence on factors influencing HRQOL of LVAD patients.

Methods and results
We systematically searched the databases PubMed, Scopus, PsycINFO, and CINAHL for qualitative studies on factors influencing HRQOL of patients with continuous flow LVADs. The included papers were synthesized using a thematic synthesis. The results were validated in discussion with a patient and practical implications were jointly developed. We included 11 papers and developed 7 themes that represent influencing factors for HRQOL of LVAD patients: disease experiences, emotional reactivity, heart failure and mechanical circulatory support symptoms, medical care, self-care, self-concept, and social environment.

Conclusion
The identified themes highlight influencing factors on HRQOL. These help to explain variation in patient outcomes and to better consider the individual situation in rehabilitation. Based on the results, four strategies are proposed for promoting psychosocial outcomes: facilitate self-care, ensure social support, offer psychosocial support, and manage patient expectations.
Simulation of broad-band ground motions with consistent long-period and short-period components using the Wasserstein interpolation of acceleration envelopes
Authors: Okazaki Tomohisa , Hachiya Hirotaka , Iwaki Asako , Maeda Takahiro , Fujiwara Hiroyuki , Ueda Naonori 
Published: June 2021

SUMMARYPractical hybrid approaches for the simulation of broad-band ground motions often combine long-period and short-period waveforms synthesized by independent methods under different assumptions for different period ranges, which at times can lead to incompatible time histories and frequency properties. This study explores an approach that generates consistent broad-band waveforms using past observation records, under the assumption that long-period waveforms can be obtained from physics-based simulations. Specifically, acceleration envelopes and Fourier amplitude spectra are transformed from long-period to short-period using machine learning methods, and they are combined to produce a broad-band waveform. To effectively obtain the relationship of high-dimensional envelopes from limited amount of data, we (1) formulate the problem as the conversion of probability distributions, which enables the introduction of a metric known as the Wasserstein distance, and (2) embed pairs of long-period and short-period envelopes into a common latent space to improve the consistency of the entire waveform. An experimental application to a past earthquake demonstrates that the proposed method exhibits superior performance compared to existing methods as well as neural network approaches. In particular, the proposed method reproduces global properties in the time domain, which confirms the effectiveness of the embedding approach as well as the advantage of the Wasserstein distance as a measure of dissimilarity of the envelopes. This method serves as a novel machine learning approach that maintains consistency both in the time-domain and frequency-domain properties of waveforms.