bluuebunny/crossref_metadata_2025_split huggingface dataset:
Standards of NGS Data Sharing and Analysis in Ataxias: Recommendations by the NGS Working Group of the Ataxia Global Initiative
Authors: Beijer Danique , Fogel Brent L. , Beltran Sergi , Danzi Matt C. , Németh Andrea H. , Züchner Stephan , Synofzik Matthis ,   AGI Ataxia NGS genomics, platforms Working Group, Adarmes Astrid , Alhusaini Saud , Ashrafi Mahmoud Reza , Bataller Luis , Bertini Enrico , Boesch Sylvia , Buijsen Ronald , Cassou Emanuel , Chan Edwin , Damásio Joana , Donis Karina , Elert-Dobkowska Ewelina , Elsayed Liena , Espinos Carmen , Hanağasi Haşmet , Heidari Morteza , Nachbauer Wolfgang , Oliveira Jorge , Opal Puneet , Paisan-Ruiz Coro , Puccio Hélène , Saccà Francesco , Saraiva-Pereira Maria Luiza , Schmidt Thorsten , Schüle Rebecca , Stevanin Giovanni , Wilke Carlo , Yoon Grace , Zach Neta , Zanni Ginevra 
Published: March 2023

Abstract
The Ataxia Global Initiative (AGI) is a worldwide multi-stakeholder research platform to systematically enhance trial-readiness in degenerative ataxias. The next-generation sequencing (NGS) working group of the AGI aims to improve methods, platforms, and international standards for ataxia NGS analysis and data sharing, ultimately allowing to increase the number of genetically ataxia patients amenable for natural history and treatment trials. Despite extensive implementation of NGS for ataxia patients in clinical and research settings, the diagnostic gap remains sizeable, as approximately 50% of patients with hereditary ataxia remain genetically undiagnosed. One current shortcoming is the fragmentation of patients and NGS datasets on different analysis platforms and databases around the world. The AGI NGS working group in collaboration with the AGI associated research platforms—CAGC, GENESIS, and RD-Connect GPAP—provides clinicians and scientists access to user-friendly and adaptable interfaces to analyze genome-scale patient data. These platforms also foster collaboration within the ataxia community. These efforts and tools have led to the diagnosis of > 500 ataxia patients and the discovery of > 30 novel ataxia genes. Here, the AGI NGS working group presents their consensus recommendations for NGS data sharing initiatives in the ataxia field, focusing on harmonized NGS variant analysis and standardized clinical and metadata collection, combined with collaborative data and analysis tool sharing across platforms.
Multimodal biomedical image retrieval and indexing system using handcrafted with deep convolution neural network features
Authors: Mansour Romany F. 
Published: March 2023

AbstractThe advances in biomedical imaging equipment have produced a massive amount of medical images that are generated by the different modalities. Consequently, a huge volume of data has been produced and caused a complex and time-consuming retrieving process of the relevant cases. To resolve this issue, the Content-Based Biomedical Image Retrieval (CBMIR) system is applied to retrieve the related images from the earlier patients’ databases. However, the previous handcrafted features methods that applied the CBMIR model have shown poor performance in many multimodal databases. In this paper, we focus on designing CBMIR technique using Deep Learning (DL) models. We present a new Multimodal Biomedical Image Retrieval and Classification (M-BMIRC) technique for retrieving and classifying the biomedical images from huge databases. The proposed M-BMIRC model involves three dissimilar processes as following: feature extraction, similarity measurement, and classification. It uses an ensemble of handcrafted features from Zernike Moments (ZM) and deep features from Deep Convolutional Neural Networks (DCNN) for feature extraction process. Additionally, the Hausdorff Distance based similarity measure is employed to identify the resemblance between the queried image and the images that exist in the database. Moreover, the classification process gets executed on the retrieval images using the Probabilistic Neural Network (PNN) model, which allocates the class labels of the tested images. Finally, the experimental studies are conducted using two benchmark medical datasets and the results ensure the superior performance of the proposed model in terms of different measures include Average Precision Rate (APR), Average Recall Rate (ARR), F-score, accuracy, and Computation Time (CT).
Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0
Authors: Fordal Jon Martin , Schjølberg Per , Helgetun Hallvard , Skjermo Tor Øistein , Wang Yi , Wang Chen 
Published: March 2023

Abstract
Possessing an efficient production line relies heavily on the availability of the production equipment. Thus, to ensure that the required function for critical equipment is in compliance, and unplanned downtime is minimized, succeeding with the field of maintenance is essential for industrialists. With the emergence of advanced manufacturing processes, incorporating predictive maintenance capabilities is seen as a necessity. Another field of interest is how modern value chains can support the maintenance function in a company. Accessibility to data from processes, equipment and products have increased significantly with the introduction of sensors and Industry 4.0 technologies. However, how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a challenge. Thus, the aim of this paper is to investigate on how maintenance and value chain data can collectively be used to improve value chain performance through prediction. The research approach includes both theoretical testing and industrial testing. The paper presents a novel concept for a predictive maintenance platform, and an artificial neural network (ANN) model with sensor data input. Further, a case of a company that has chosen to apply the platform, with the implications and determinants of this decision, is also provided. Results show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.
Assessing cognitive impairment in an ethnically diverse cohort of oldest-old: the life after 90 study
Authors: Petrosyan Diana , Corrada Maria M. , Kawas Claudia H. , Demoz Asmeret , Ganzon Czarina , Popovich Oksana , Gaied Reham , Mungas Dan , Gilsanz Paola , Vieira Katharine E. , Whitmer Rachel A. , DeCarli Charles 
Published: March 2023

Abstract
Background
Though dementia rates vary by racial or ethnic groups, it is unknown if these disparities remain among those aged 90 or older.

Aims
To test this hypothesis, we used baseline clinical evaluation of 541 ethnically and racially diverse individuals participating in the LifeAfter90 Study to assess how associations between core demographic characteristics and measures of physical and cognitive performance differ across the racial/ethnic groups.

Methods
Participants in this study were long-term non-demented members of Kaiser Permanente Northern California. They were clinically evaluated and diagnosed with normal or impaired cognition (mild cognitive impairment and dementia) through an in-person comprehensive clinical assessment consisting of a detailed medical history, physical and neurological examination, functional, and cognitive tests.

Results
The average age at enrollment was 93.0 ± 2.6 years, 62.4% female and 34.2% non-Hispanic White. At initial evaluation 301 participants had normal cognition and 165 had mild cognitive impairment (MCI) and despite screening, 69 participants were determined to have dementia. Age, education, 3MS, FAQ and CDR scores were significantly associated with cognitive impairment (normal versus MCI and dementia), but not gender. There was a significant univariate association between race/ethnicity and cognitive impairment (p < 0.02) being highest among Black (57.4%) and lowest among Asian (32.7%) individuals. After adjustment for age, gender, and education, however, prevalence of cognitive impairment was not influenced by race or ethnicity.

Conclusion
Our results confirm the ability to reliably assess clinical diagnosis in a diverse sample of very old individuals.
Retinal sensitivity and gaze fixation evaluated by microperimetry in subjects with type 2 diabetes: two independent parameters that explore different neuronal circuits
Authors: Ortiz-Zuñiga A. M. , Rojano Toimil A. , Rahnama K. , Lainez E. , Raguer N. , Simó-Servat O. , Hernández C. , Simó R. , Ciudin A. 
Published: March 2023

Abstract
Background and aims
Retinal sensitivity (RS) and gaze fixation (GF) assessed by retinal microperimetry are useful and complementary tools for identifying mild cognitive impairment (MCI) in patients with type 2 diabetes (T2D). The hypothesis is that RS and GF examine different neural circuits: RS depends only on the visual pathway while GF reflects white matter complex connectivity networks. The aim of the study is to shed light to this issue by examining the relationship of these two parameters with visual evoked potentials (VEP), the current gold standard to examine the visual pathway.

Materials and methods
Consecutive T2D patients > 65 years were recruited from the outpatient clinic. Retinal microperimetry (MAIA 3rd generation) and visual evoked potentials (VEP) (Nicolet Viking ED). RS (dB), GF (BCEA63%, BCEA95%) (MAIA) and VEP (Latency P100ms, Amplitude75–100 uV) were analyzed.

Results
Thirty three patients (45% women, 72.1 ± 4.6 years) were included. VEP parameters significantly correlated with RS but not with GF.

Conclusions
These results confirm that RS but not GF depends on the visual pathway, reinforcing the concept that they are complementary diagnostic tools. Used together can further increase the value of microperimetry as screening test for identifying T2D population with cognitive impairment.
Compatibility and complementarity between institutional and post-Keynesian economics: a literature review with a particular focus on methodology
Authors: Fernández-Huerga Eduardo , Pardo Ana , Salvador Ana 
Published: March 2023

AbstractThe objective of this article is to carry out a review of the links between institutional and post-Keynesian economics, in order to raise the possible compatibility and even complementarity between the two branches of economic thought. To this end, we first review the question of compatibility from the perspective of institutional economics and then from the post-keynesian standpoint. Next, methodological issues are addressed by presenting the essential elements that characterize post-keynesian and institutional methodology (and internal debates in this regard) and then by describing the areas of compatibility between the two. The final section presents the main conclusions and summarizes the basic elements that could constitute a theoretical framework of synthesis between institutional and post-keynesian economics.
Laser beam heat treatment in large-scale additive manufacturing
Authors: Layher Michel , Eckhardt Lukas , Linke Daniel , Hopf Andreas , Bliedtner Jens 
Published: March 2023

AbstractLarge-scale additive manufacturing (LSAM) has been developing a huge potential to address certain tasks in industrial applications over the last years. Particularly granule extrusion technologies enable the processing of an enormous variety of materials but also introduce new challenges in printing large-scale parts. Compared to fused layer modelling and due to larger nozzle diameters as well as higher extrusion rates strand geometry and consequently, process-related voids are enlarged. A promising approach to improve part quality is the integration of carbon dioxide laser (CO2) radiation into the additive manufacturing process to weld deposited strands by increasing the interface temperature. Experiments are conducted for polymethyl methacrylate (PMMA) and styrene-acrylonitrile (SAN) which are very good absorbers of the wavelength 10.6 µm. Due to the locally defined heat treatment, merely certain areas of the strands are heated to a desired temperature. This leads to a more complete diffusion. At the same time, temperature gradients in the overall part are avoided. By means of a thermographic camera, the temperatures at the re-melting process of deposited strands can be precisely monitored. Therefore, the relation between laser intensity and resulting temperature can be transferred into a repeatable process window. The interaction between laser and deposited material leads to a wider contact area between stacked strands. While flexural strength is not significantly affected, compared to specimens manufactured without any heat treatment bending force is increased by 66% (PMMA) and 48% (SAN), respectively. In addition, voids between adjacent strands are reduced by up to 57%.
Integrating Different Data Modalities for the Classification of Alzheimer’s Disease Stages
Authors: Maddalena Lucia , Granata Ilaria , Giordano Maurizio , Manzo Mario , Guarracino Mario Rosario 
Published: March 2023

AbstractAlzheimer’s disease (AD) is the most common form of dementia with physical, psychological, social, and economic impacts on patients, their carers, and society. Its early diagnosis allows clinicians to initiate the treatment as early as possible to arrest or slow down the disease progression more effectively. We consider the problem of classifying AD patients through a machine learning approach using different data modalities acquired by non-invasive techniques. We perform an extensive evaluation of a machine learning classification procedure using omics, imaging, and clinical features, extracted by the ANMerge dataset, taken alone or combined together. Experimental results suggest that integrating omics and imaging features leads to better performance than any of them taken separately. Moreover, clinical features consisting of just two cognitive test scores always lead to better performance than any of the other types of data or their combinations. Since these features are usually involved in the clinician diagnosis process, our results show how their adoption as classification features positively biases the results.
A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories
Authors: Demetriou Andreas , Alfsvåg Henrik , Rahrovani Sadegh , Haghir Chehreghani Morteza 
Published: March 2023

AbstractWe propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and generate scenarios of trajectories with different lengths, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us the flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of autonomous driving. Second, we develop an architecture based on Recurrent Autoencoder with GANs to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, to obtain further insights into the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection.
Identification of Clinical Features Associated with Mortality in COVID-19 Patients
Authors: Eskandarian Rahimeh , Alizadehsani Roohallah , Behjati Mohaddeseh , Zahmatkesh Mehrdad , Sani Zahra Alizadeh , Haddadi Azadeh , Kakhi Kourosh , Roshanzamir Mohamad , Shoeibi Afshin , Hussain Sadiq , Khozeimeh Fahime , Darbandy Mohammad Tayarani , Joloudari Javad Hassannataj , Lashgari Reza , Khosravi Abbas , Nahavandi Saeid , Islam Sheikh Mohammed Shariful 
Published: March 2023

AbstractUnderstanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings.
EMA-FDA Parallel Scientific Advice: Optimizing Development of Medicines in the Global Age
Authors: Thor Shannon , Vetter Thorsten , Marcal Anabela , Kweder Sandra 
Published: March 2023

AbstractAs medicines development continues towards a globalized approach, both the pharmaceutical industry and regulatory agencies increasingly seek opportunities to proactively engage early in product development. The parallel scientific advice program shared by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) provides a mechanism for experts to concurrently engage in scientific discourse with sponsors on key issues during the development phase of new medicinal products (drugs, biologicals, vaccines, and advanced therapies).
A new approach to controlling a hydraulic indirect elevator with a variable-speed pump
Authors: Stawiński Łukasz , Kosucki Andrzej , Morawiec Adrian , Skowrońska Justyna 
Published: March 2023

Abstract
The paper describes a new approach to the issue of controlling an indirect elevator with a bidirectional variable-speed pump and a simple controller based on the position sensor. The aim of this paper is to present a method of controlling the speed of the elevator to ensure smooth movement and proper positioning of the car on the floor, regardless of its load and ropes rigidity. The main feature of the proposed solution is the use of a frequency inverter in vector mode to control the speed of the car in both directions. The control function is based on virtual cams comparing actual measurements from the car position sensor. The proposed control strategy has been experimentally verified on the existing indirect elevator drive, and the obtained results indicate a very high accuracy in maintaining and shaping the speed and positioning of the car. The conducted research confirms the possibility of using a new method of controlling hydraulic and indirect elevators. The benefits of this method include a less complex hydraulic system, the control of overloads in the car and vibrations in the rope system, and the possibility of energy recovery.
Shifts in Above‐ Versus Below‐Ground Carbon Gains to Terrestrial Ecosystems Carbon Sinks Under Excess Nitrogen Inputs
Authors: Lu Xiaofei , Gilliam Frank S. , Yue Xu , Wang Bin , Kuang Yuanwen 
Published: March 2023

AbstractAlthough elevated atmospheric nitrogen (N) deposition can increase terrestrial carbon (C) sinks, the persistence of this C acquisition depends partly on the responses of plant biomass and soil organic carbon (SOC) to long‐term exogenous N input. Experiments with N‐additions show increases in above‐ground biomass (AGB), although C dynamics between above‐ and below‐ground under N enrichment are unconnected in many studies. Here, we meta‐analyzed data from 362 N‐addition experiments globally and found that both AGB (+34%) and below‐ground biomass (BGB, +13%) positively but root‐to‐shoot ratios negatively (−15%) responded to N addition. These responses were reversed, however, when the experimental duration was over 20 years, wherein BGB increased and AGB was unchanged or even declined, during which time plants invested more C into roots. The effects of increased AGB were weak and negative, whereas those of increased BGB were robust and positive on SOC. Under chronic N deposition, the significant increase in SOC (5%) was associated with the increased root C inputs and decreased soil C losses rather than with increased above‐ground C inputs. The results suggest that the sequestration of below‐ground C exceeds that of above‐ground C to sustain terrestrial C gain during long‐term N deposition.
Influence of Seasonal Post‐Depositional Processes on the Remanent Magnetization in Varved Sediments From Glacial Lake Ojibway (Canada)
Authors: Philippe Édouard G. H. , St‐Onge Guillaume , Valet Jean‐Pierre , Godbout Pierre‐Marc , Egli Ramon , Francus Pierre , Roy Martin 
Published: March 2023

AbstractThe natural remanent magnetization (NRM) of high sedimentation rate sediments provides significant information about paleomagnetic secular variation of the Earth's magnetic field and can also potentially be used for stratigraphy. However, NRM acquisition depends on conditions inherent to the depositional environment. In addition to recording a precise annual chronology, varved sediments reflect marked annual sedimentary changes. The Earth's magnetic field does not vary significantly over such a short period, so magnetic changes recorded by varves are expected to reflect the influence of depositional parameters on the recording process. We focus here on a sequence of 27 ± 1 varves from the former proglacial Lake Ojibway (∼8.5 ka cal BP) from which individual cm‐thick summer and winter beds were sampled. Paleomagnetic, granulometric and geochemical analyses were conducted on each bed. A mean inclination shallowing of 24.3° is observed in winter beds, along with an 11.3° shallowing in summer beds. Magnetic declinations follow, on average, the expected field direction, but differences of up to 20° occur between successive beds. Summer beds are thicker than winter beds and have stronger magnetic susceptibility, higher Ca/Fe ratios and coarser sedimentary and magnetic grains. This grain size pattern reflects the input of coarser detrital particles during summer, while the finer fraction remained in suspension until it was deposited in winter. A combination of differential compaction between the winter and summer beds, seasonally varying physical and magnetic properties of sediments, and delayed NRM acquisition explains the variable and coercivity‐dependent inclination shallowing.