bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adult age‐at‐death estimation using the first rib: A simple probabilistic approach
Authors: Luna Leandro H. , Aranda Claudia M. 
Published: August 2022

AbstractAge‐at‐death estimation is a difficult task in fragmented or incomplete contexts. The generation and testing of methods are needed to identify their potential application in different types of osteological samples. This paper proposes a new method for age estimation using the first rib. Four hundred and fifteen individuals from the Coimbra Identified Skeletal Collections (Portugal) were studied; the sample was divided in two groups (reference and test samples), used to develop the procedure and to evaluate its reliability. The three rib joints and two variables (surface texture and topography, and periarticular margins and subsidence of the surface) were recorded. Statistical analyses were performed to test the procedure reproducibility, and side and sex variations. Posterior probabilities were calculated following Bayesian statistics and used to estimate the most likely age‐at‐death in the test sample. The results show the reproducibility of the method is adequate, left and right ribs can be indistinctly recorded, and both sexes show similar change patterns. The costal face and the head epiphyseal region offered satisfactory results and the percentages of correct estimations are greater for younger individuals, decreasing as ages‐at‐death are higher. This method is more effective among individuals in the 20–50 years cohort; nevertheless, it also offered satisfactory estimates in older ones. Overestimations in individuals younger than 40 years and underestimations in individuals older than 75 years were identified. In conclusion, the first rib offer adequate estimates when a basic probabilistic approach is chosen. Whenever possible, this proposal must be included in a multifactorial perspective with other age markers.
Effects of alcohol on sleep and nocturnal heart rate: Relationships to intoxication and morning‐after effects
Authors: Pabon Elisa , Greenlund Ian M. , Carter Jason R. , de Wit Harriet 
Published: August 2022

AbstractBackgroundAlcohol consumption produces feelings of well‐being and stimulation, but also impairs psychomotor performance, disturbs cardiovascular function and sleep, and can disrupt next‐day mood and behavior. A deeper understanding of how the acute effects of alcohol relate to its sleep and morning‐after effects is needed to minimize harm resulting from its use. This study examined relationships between the effects of a high dose of alcohol on subjective and psychomotor measures, nocturnal heart rate, sleep quality, and morning‐after mood and behavior. We hypothesized that alcohol would produce disturbances in cardiovascular and sleep regulation during the night, which would predict morning‐after mood and behavioral performance.MethodsThirty‐one men and women participated in two overnight laboratory visits during which they consumed either alcohol (1.0 g/kg for men, 0.85 g/kg for women) or placebo (randomized, crossover design). They consumed the beverage from 8 to 9 pm, and remained in the laboratory overnight for polysomnographic sleep recording. Subjective and behavioral measures were obtained during consumption and at 7–8 amthe morning after.ResultsAlcohol increased both negative and positive arousal, urge to drink and sedation, and it impaired performance on behavioral tasks. During sleep, alcohol produced expected tachycardia and detriments in sleep quality including decreased total sleep time, sleep efficiency, and altered sleep architecture. Only modest effects on mood or performance were detected the following morning. The acute sedative‐like effects of alcohol were related to increases in N2 sleep, but not to other disruptions in sleep or nocturnal heart rate, and neither sleep impairments nor nocturnal heart rate were related to mood or task performance the morning after.ConclusionsThe effects of alcohol on sleep and nocturnal heart rate were not strongly related to either its acute or morning‐after effects. These findings do not provide strong support for the idea that alcohol‐induced sleep disruptions underlie morning‐after effects.
Betel quid: New insights into an ancient addiction
Authors: Stokes Clare , Pino Jose A. , Hagan D. Walker , Torres Gonzalo E. , Phelps Edward A. , Horenstein Nicole A. , Papke Roger L. 
Published: August 2022

AbstractThe use of areca nuts (areca) in the form of betel quids constitutes the fourth most common addiction in the world, associated with high risk for oral disease and cancer. Areca is a complex natural product, making it difficult to identify specific components associated with the addictive and carcinogenic properties. It is commonly believed that the muscarinic agonist arecoline is at the core of the addiction. However, muscarinic receptor activation is not generally believed to support drug‐taking behaviour. Subjective accounts of areca use include descriptions of both sedative and stimulatory effects, consistent with the presence of multiple psychoactive agents. We have previously reported partial agonism of α4‐containing nicotinic acetylcholine receptors by arecoline and subsequent inhibition of those receptors by whole areca broth. In the present study, we report the inhibition of nicotinic acetylcholine receptors and other types of neurotransmitter receptors with compounds of high molecular weight in areca and the ability of low molecular weight areca extract to activate GABA and glutamate receptors. We confirm the presence of a high concentration of GABA and glutamate in areca. Additionally, data also indicate the presence of a dopamine and serotonin transporter blocking activity in areca that could account for the reported stimulant and antidepressant activity. Our data suggest that toxic elements of high molecular weight may contribute to the oral health liability of betel quid use, while two distinct low molecular weight components may provide elements of reward, and the nicotinic activity of arecoline contributes to the physical dependence of addiction.
Robotic‐arm assisted total knee arthroplasty has a learning curve of 16 cases and increased operative time of 12 min
Authors: Tay Mei Lin , Carter Matthew , Zeng Nina , Walker Matthew L. , Young Simon W. 
Published: August 2022

AbstractBackgroundRobotic‐arm assisted systems are increasingly used for knee arthroplasty, however introduction of new systems can involve a learning curve. We aimed to define the learning curve in terms of operative time and component placement/sizing of a robotic system for total knee arthroplasty (TKA) in a team of experienced surgeons, and to investigate mid‐term patient outcomes.MethodsA total of 101 consecutive patients underwent primary robotic‐arm assisted TKA by three surgeons (mean 2 year follow‐up). Operative times, component placement, implant sizing and reoperations were recorded. Cumulative Summation (CUSUM) was used to analyse learning curves. Patient outcomes were compared between learning and proficiency phases.ResultsThe learning curve was 16 cases, with a 12‐min increase in operative time (P < 0.01). Once proficiency was achieved, the greatest time reductions were seen for navigation registration (P = 0.003) and bone preparation (P < 0.0001). A learning curve was found with polyethylene (PE) insert sizing (P = 0.01). No differences were found between learning and proficiency groups in terms of implant survival (100% and 97%, respectively, NS) or patient‐reported outcome measures at 2 years (NS).ConclusionIntroduction of a robotic‐arm assisted system for TKA led to increased operative times for navigation registration and bone preparation, and a learning curve with PE insert sizing. No difference in patient outcomes between learning and proficiency groups at 2 years was found. These findings can inform surgeons' expectations when starting to use robotic‐assisted systems.
Action is needed to tackle the clinical, psychological and socioeconomic impact of perinatal COVID‐19
Authors: Briana Despina D. , Papaevangelou Vassiliki , Malamitsi‐Puchner Ariadne 
Published: August 2022

AbstractThe COVID‐19 pandemic has turned perinatal healthcare into a worldwide public health challenge. Although initial data did not demonstrate pregnancy as a more susceptible period to adverse outcomes of severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) infection, an increasing number of reports now certify maternal illness as a high‐risk condition for the development of maternal–fetal complications. Despite the rarity of SARS‐CoV‐2 vertical transmission, severe maternal illness might induce adverse perinatal and neonatal outcomes. Additionally, perinatal COVID‐19 data may raise concerns about long‐term harmful consequences to the offspring in the framework of non‐communicable diseases. The World Health Organisation, as well as scientific literature, consider the protection of the maternal–fetal dyad against COVID‐19 as a critical issue and, therefore, strongly promote and encourage the vaccination of pregnant and lactating women. Furthermore, the pandemic has triggered an unprecedented recession, leading to historic levels of unemployment and deprivation, while health, societal, economic and gender inequities particularly affecting low‐income and middle‐income countries, have increased. This mini‐review provides an updated brief report on historical, clinical, psychological and socioeconomic aspects of the COVID‐19 pandemic based on 10 lectures presented at the 9th Maria‐Delivoria‐Papadopoulos Perinatal Symposium, held virtually on 19 March 2022.
Systemic exposure to hydroxychloroquine and its relationship with outcome in severely ill COVID‐19 patients in New York City
Authors: Lyashchenko Alex K. , Yu Yifan , McMahon Donald J. , Bies Robert , Yin Michael T. , Cremers Serge 
Published: August 2022

AimTo investigate the relationship between systemic exposure to hydroxychloroquine (HCQ) and its metabolite desethylhydroxychloroquine (DHCQ) and clinical outcome in severely ill patients treated with a standard oral dose regimen of HCQ during the first wave of COVID‐19 in New York City.MethodsWe correlated retrospective clinical data with drug exposure prospectively assessed from convenience samples using population pharmacokinetics and Bayesian estimation. Systemic exposure was assessed in 215 patients admitted to ICU or COVID‐ward for whom an interleukin‐6 level was requested and who were still alive 24 hours after the last dose of HCQ. Patients received oral HCQ 600 mg twice daily on day 1 followed by 4 days of 400 mg daily.ResultsFifty‐three precent of the patients were intubated at 5.4 ± 6.4 days after admission and 26.5% died at an average of 32.2 ± 19.1 days. QTc at admission was 448 ± 34 ms. Systemic exposure to HCQ and DHCQ demonstrated substantial variability. Cumulative area under the serum concentration–time curve up to infinity for HCQ was 71.4 ± 19.3 h mg/L and for DHCQ 56.5 ± 28.3 h mg/L. Variability in systemic exposure was not clearly explained by renal function, liver function or inflammatory state. In turn, systemic exposure did not correlate with intubation status, survival or QTc prolongation.ConclusionThis study in severely ill patients was not able to find any relationship between systemic exposure to HCQ and DHCQ and clinical outcome at a routine dose regimen and adds to the growing body of evidence that oral HCQ does not alter the course of disease in COVID‐19 patients.
A song of volumes, surfaces and fluxes: The case study of the Central Mallorca Depression (Balearic Promontory) during the Messinian Salinity Crisis
Authors: Raad Fadl , Ebner Ronja , Heida Hanneke , Meijer Paul , Lofi Johanna , Maillard Agnès , Garcia‐Castellanos Daniel 
Published: August 2022

AbstractThe Central Mallorca Depression (CMD) located in the Balearic Promontory (Western Mediterranean) contains a well‐preserved evaporitic sequence belonging to the Messinian Salinity Crisis (MSC) salt giant, densely covered by high‐ and low‐resolution seismic reflection data. It has been proposed recently that the MSC evaporitic sequence in the CMD could be a non‐deformed analogue of the key MSC area represented by the Caltanissetta Basin in Sicily. This presumed similarity makes the CMD an interesting system to better understand the MSC events. Physics‐based box models of the water mixing between sub‐basins, built on conservation of mass of water and salt, help constrain the hydrological conditions under which evaporites formed during the MSC. Those models have been widely used in the literature of the MSC in the past two decades. They have been mostly applied to the Mediterranean Sea as a whole focusing on the Mediterranean–Atlantic connection, or focusing on the influence of the Sicily Sill connecting the Western and Eastern Mediterranean Sea. In this study, we apply a downscaled version of such modelling technique to the CMD. First, we quantify the present‐day volumes of the MSC units. We then use a reconstructed pre‐MSC paleo‐bathymetry to model salinity changes as a function of flux exchanges between the CMD and the Mediterranean. We show that a persistent connection between the CMD and the Mediterranean brine near gypsum saturation can explain volume of Primary Lower Gypsum under a sea level similar to the present. For the halite, on the contrary, we show that the observed halite volume cannot be deposited from a connected CMD‐Mediterranean scenario, suggesting a drawdown of at least 850 m (sill depth) is necessary. Comparison between the deep basin halite volume and that of the CMD shows that it is possible to obtain the observed halite volume in both basins from a disconnected Mediterranean basin undergoing drawdown, although determining the average salinity of the Western Mediterranean basin at the onset of drawdown requires further investigation.
Development and multi‐institutional validation of an artificial intelligence‐based diagnostic system for gastric biopsy
Authors: Abe Hiroyuki , Kurose Yusuke , Takahama Shusuke , Kume Ayako , Nishida Shu , Fukasawa Miyako , Yasunaga Yoichi , Ushiku Tetsuo , Ninomiya Youichiro , Yoshizawa Akihiko , Murao Kohei , Sato Shin’ichi , Kitsuregawa Masaru , Harada Tatsuya , Kitagawa Masanobu , Fukayama Masashi ,   Japan Pathology AI Diagnostics/National Institution of Informatics (JP‐AID/NII) Study Group for Gastric Biopsy Pathology
Published: August 2022

AbstractTo overcome the increasing burden on pathologists in diagnosing gastric biopsies, we developed an artificial intelligence‐based system for the pathological diagnosis of gastric biopsies (AI‐G), which is expected to work well in daily clinical practice in multiple institutes. The multistage semantic segmentation for pathology (MSP) method utilizes the distribution of feature values extracted from patches of whole‐slide images (WSI) like pathologists’ “low‐power view” information of microscopy. The training dataset included WSIs of 4511 gastric biopsy tissues from 984 patients. In tissue‐level validation, MSP AI‐G showed better accuracy (91.0%) than that of conventional patch‐based AI‐G (PB AI‐G) (89.8%). Importantly, MSP AI‐G unanimously achieved higher accuracy rates (0.946 ± 0.023) than PB AI‐G (0.861 ± 0.078) in tissue‐level analysis, when applied to the cohorts of 10 different institutes (3450 samples of 1772 patients in all institutes, 198–555 samples of 143–206 patients in each institute). MSP AI‐G had high diagnostic accuracy and robustness in multi‐institutions. When pathologists selectively review specimens in which pathologist’s diagnosis and AI prediction are discordant, the requirement of a secondary review process is significantly less compared with reviewing all specimens by another pathologist.
Seeing Through Sounds: Mapping Auditory Dimensions to Data and Charts for People with Visual Impairments
Authors: Wang R. , Jung C. , Kim Y. 
Published: August 2022

AbstractSonification can be an effective medium for people with visual impairments to understand data in visualizations. However, there are no universal design principles that apply to various charts that encode different data types. Towards generalizable principles, we conducted an exploratory experiment to assess how different auditory channels (e.g., pitch, volume) impact the data and visualization perception among people with visual impairments. In our experiment, participants evaluated the intuitiveness and accuracy of the mapping of auditory channels on different data and chart types. We found that participants rated pitch to be the most intuitive, while the number of tappings and the length of sounds yielded the most accurate perception in decoding data. We study how audio channels can intuitively represent different charts and demonstrate that data‐level perception might not directly transfer to chart‐level perception as participants reflect on visual aspects of the charts while listening to audio. We conclude by how future experiments can be designed to establish a robust ranking for creating audio charts.
Reusing Interactive Analysis Workflows
Authors: Gadhave K. , Cutler Z. , Lex A. 
Published: August 2022

AbstractInteractive visual analysis has many advantages, but an important disadvantage is that analysis processes and workflows cannot be easily stored and reused. This is in contrast to code‐based analysis workflows, which can simply be run on updated datasets, and adapted when necessary. In this paper, we introduce methods to capture workflows in interactive visualization systems for different interactions such as selections, filters, categorizing/grouping, labeling, and aggregation. These workflows can then be applied to updated datasets, making interactive visualization sessions reusable. We demonstrate this specification using an interactive visualization system that tracks interaction provenance, and allows generating workflows from the recorded actions. The system can then be used to compare different versions of datasets and apply workflows to them. Finally, we introduce a Python library that can load workflows and apply it to updated datasets directly in a computational notebook, providing a seamless bridge between computational workflows and interactive visualization tools.
Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures
Authors: Troidl Jakob , Cali Corrado , Gröller Eduard , Pfister Hanspeter , Hadwiger Markus , Beyer Johanna 
Published: August 2022

AbstractHigh‐resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell substructures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. In‐depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, and supported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparative visualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges them in linked and juxtaposed views. To adapt to new domain‐specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. We present an in‐depth case study for mitochondria analysis in neuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists.
LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning
Authors: Hong Jiayi , Trubuil Alain , Isenberg Tobias 
Published: August 2022

AbstractWe describe LineageD—a hybrid web‐based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision‐making process, however, is time‐consuming, tedious, and error‐prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom‐up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top‐down fashion for a few steps, improving the ML‐based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top‐down building approach can reduce assignments errors in real practice.
SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio‐temporal Ensembles
Authors: Huesmann Karim , Linsen Lars 
Published: August 2022

AbstractLatent feature spaces of deep neural networks are frequently used to effectively capture semantic characteristics of a given dataset. In the context of spatio‐temporal ensemble data, the latent space represents a similarity space without the need of an explicit definition of a field similarity measure. Commonly, these networks are trained for specific data within a targeted application. We instead propose a general training strategy in conjunction with a deep neural network architecture, which is readily applicable to any spatio‐temporal ensemble data without re‐training. The latent‐space visualization allows for a comprehensive visual analysis of patterns and temporal evolution within the ensemble. With the use of SimilarityNet, we are able to perform similarity analyses on large‐scale spatio‐temporal ensembles in less than a second on commodity consumer hardware. We qualitatively compare our results to visualizations with established field similarity measures to document the interpretability of our latent space visualizations and show that they are feasible for an in‐depth basic understanding of the underlying temporal evolution of a given ensemble.