bluuebunny/crossref_metadata_2025_split huggingface dataset:
Creation of an unexpected plane of enhanced covalency in cerium(III) and berkelium(III) terpyridyl complexes
Authors: Gaiser Alyssa N. , Celis-Barros Cristian , White Frankie D. , Beltran-Leiva Maria J. , Sperling Joseph M. , Salpage Sahan R. , Poe Todd N. , Gomez Martinez Daniela , Jian Tian , Wolford Nikki J. , Jones Nathaniel J. , Ritz Amanda J. , Lazenby Robert A. , Gibson John K. , Baumbach Ryan E. , Páez-Hernández Dayán , Neidig Michael L. , Albrecht-Schönzart Thomas E. 
Published: December 2021

AbstractControlling the properties of heavy element complexes, such as those containing berkelium, is challenging because relativistic effects, spin-orbit and ligand-field splitting, and complex metal-ligand bonding, all dictate the final electronic states of the molecules. While the first two of these are currently beyond experimental control, covalent M‒L interactions could theoretically be boosted through the employment of chelators with large polarizabilities that substantially shift the electron density in the molecules. This theory is tested by ligating BkIIIwith 4’-(4-nitrophenyl)-2,2’:6’,2”-terpyridine (terpy*), a ligand with a large dipole. The resultant complex, Bk(terpy*)(NO3)3(H2O)·THF, is benchmarked with its closest electrochemical analog, Ce(terpy*)(NO3)3(H2O)·THF. Here, we show that enhanced Bk‒N interactions with terpy* are observed as predicted. Unexpectedly, induced polarization by terpy* also creates a plane in the molecules wherein the M‒L bonds trans to terpy* are shorter than anticipated. Moreover, these molecules are highly anisotropic and rhombic EPR spectra for the CeIIIcomplex are reported.
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Authors: Seyyed-Kalantari Laleh , Zhang Haoran , McDermott Matthew B. A. , Chen Irene Y. , Ghassemi Marzyeh 
Published: December 2021

AbstractArtificial intelligence (AI) systems have increasingly achieved expert-level performance in medical imaging applications. However, there is growing concern that such AI systems may reflect and amplify human bias, and reduce the quality of their performance in historically under-served populations such as female patients, Black patients, or patients of low socioeconomic status. Such biases are especially troubling in the context of underdiagnosis, whereby the AI algorithm would inaccurately label an individual with a disease as healthy, potentially delaying access to care. Here, we examine algorithmic underdiagnosis in chest X-ray pathology classification across three large chest X-ray datasets, as well as one multi-source dataset. We find that classifiers produced using state-of-the-art computer vision techniques consistently and selectively underdiagnosed under-served patient populations and that the underdiagnosis rate was higher for intersectional under-served subpopulations, for example, Hispanic female patients. Deployment of AI systems using medical imaging for disease diagnosis with such biases risks exacerbation of existing care biases and can potentially lead to unequal access to medical treatment, thereby raising ethical concerns for the use of these models in the clinic.
Regulation-based probabilistic substance quality index and automated geo-spatial modeling for water quality assessment
Authors: Nikitin Artyom , Tregubova Polina , Shadrin Dmitrii , Matveev Sergey , Oseledets Ivan , Pukalchik Maria 
Published: December 2021

AbstractNatural environments are recognized as complex heterogeneous structures thus requiring numerous multi-scale observations to yield a comprehensive description. To monitor the current state and identify negative impacts of human activity, fast and precise instruments are in urgent need. This work provides an automated approach to the assessment of spatial variability of water quality using guideline values on the example of 1526 water samples comprising 21 parameters at 448 unique locations across the New Moscow region (Russia). We apply multi-task Gaussian process regression (GPR) to model the measured water properties across the territory, considering not only the spatial but inter-parameter correlations. GPR is enhanced with a Spectral Mixture Kernel to facilitate a hyper-parameter selection and optimization. We use a 5-fold cross-validation scheme along with$$R^2$$R2-score to validate the results and select the best model for simultaneous prediction of water properties across the area. Finally, we develop a novel Probabilistic Substance Quality Index (PSQI) that combines probabilistic model predictions with the regulatory standards on the example of the epidemiological rules and hygienic regulations established in Russia. Moreover, we provide an interactive map of experimental results at 100 m2resolution. The proposed approach contributes significantly to the development of flexible tools in environment quality monitoring, being scalable to different standard systems, number of observation points, and region of interest. It has a strong potential for adaption to environmental and policy changes and non-unified assessment conditions, and may be integrated into support-decision systems for the rapid estimation of water quality spatial distribution.
Bayesian analyses question the role of climate in Chulmun demography
Authors: Kim Habeom , Lee Gyoung-Ah , Crema Enrico R. 
Published: December 2021

AbstractWe investigate the relationship between climatic and demographic events in Korea during the Chulmun period (10,000–3,500 cal. BP) by analyzing paleoenvironmental proxies and14C dates. We focus on testing whether a cooling climate, and its potential negative impact on millet productivity around the mid 5th-millennium cal. BP, triggered the population decline suggested by the archaeological record. We employ a Bayesian approach that estimates the temporal relationship between climatic events and change-points in the rate of growth in human population as inferred from radiocarbon time frequency data. Our results do not support the climate-induced population decline hypothesis for three reasons. First, our Bayesian analyses suggest that the cooling event occurred after the start of the population decline inferred from the radiocarbon time–frequency record. Second, we did not find evidence showing a significant reduction of millet-associated dates occurring during the cooling climate. Third, we detected different magnitudes of decline in the radiocarbon time–frequency data in the inland and coastal regions, indicating that the even if cooling episodes were ultimately responsible of these population ‘busts’, their impact was most likely distinct between these regions. We discuss our results highlighting the long tradition of mobility-based subsistence strategy in coastal regions as a potential factor contributing to the regional differences we were able to detect.
Design, synthesis, and biological evaluation of novel urolithins derivatives as potential phosphodiesterase II inhibitors
Authors: Tang Long , Jiang Jianchun , Song Guoqiang , Wang Yajing , Zhuang Ziheng , Tan Ying , Xia Yan , Huang Xianfeng , Feng Xiaoqing 
Published: December 2021

AbstractA series of urolithins derivatives were designed and synthesized, and their structures have been confirmed by 1H NMR, 13C NMR, and HR-MS. The inhibitory activity of these derivatives on phosphodiesterase II (PDE2) was thoroughly studied with 3-hydroxy-8-methyl-6H-benzo[C]chromen-6-one and 3-hydroxy-7,8,9,10-tetrahydro-6H-benzo[C] chromen-6-one as the lead compounds. The biological activity test showed that compound 2e had the best inhibitory activity on PDE2 with an IC50 of 33.95 μM. This study provides a foundation for further structural modification and transformation of urolithins to obtain PDE2 inhibitor small molecules with better inhibitory activity.
C-type lectin receptor DCIR contributes to hippocampal injury in acute neurotropic virus infection
Authors: Stoff Melanie , Ebbecke Tim , Ciurkiewicz Malgorzata , Pavasutthipaisit Suvarin , Mayer-Lambertz Sabine , Störk Theresa , Pavelko Kevin D. , Baumgärtner Wolfgang , Jung Klaus , Lepenies Bernd , Beineke Andreas 
Published: December 2021

AbstractNeurotropic viruses target the brain and contribute to neurologic diseases. C-type lectin receptors (CLRs) are pattern recognition receptors that recognize carbohydrate structures on endogenous molecules and pathogens. The myeloid CLR dendritic cell immunoreceptor (DCIR) is expressed by antigen presenting cells and mediates inhibitory intracellular signalling. To investigate the effect of DCIR on neurotropic virus infection, mice were infected experimentally with Theiler’s murine encephalomyelitis virus (TMEV). Brain tissue of TMEV-infected C57BL/6 mice and DCIR−/−mice were analysed by histology, immunohistochemistry and RT-qPCR, and spleen tissue by flow cytometry. To determine the impact of DCIR deficiency on T cell responses upon TMEV infection in vitro,antigen presentation assays were utilised. Genetic DCIR ablation in C57BL/6 mice was associated with an ameliorated hippocampal integrity together with reduced cerebral cytokine responses and reduced TMEV loads in the brain. Additionally, absence of DCIR favoured increased peripheral cytotoxic CD8+T cell responses following TMEV infection. Co-culture experiments revealed that DCIR deficiency enhances the activation of antigen-specific CD8+T cells by virus-exposed dendritic cells (DCs), indicated by increased release of interleukin-2 and interferon-γ. Results suggest that DCIR deficiency has a supportive influence on antiviral immune mechanisms, facilitating virus control in the brain and ameliorates neuropathology during acute neurotropic virus infection.
Assessment of leaf morphological, physiological, chemical and stoichiometry functional traits for understanding the functioning of Himalayan temperate forest ecosystem
Authors: Rawat Monika , Arunachalam Kusum , Arunachalam Ayyandar , Alatalo Juha M. , Pandey Rajiv 
Published: December 2021

AbstractLeaf functional traits support plant survival and growth in different stress and disturbed conditions and respond according to leaf habit. The present study examined 13 leaf traits (3 morphological, 3 chemical, 5 physiological, and 2 stoichiometry) of nine dominant forest tree species (3 coniferous, 3 deciduous broad-leaved, 3 evergreen broad-leafed) to understand the varied response of leaf habits. The hypothesis was to test if functional traits of the conifers, deciduous and evergreen differ significantly in the temperate forest and to determine the applicability of leaf economic theoryi.e., conservative vs. acquisitive resource investment, in the temperate Himalayan region. The attributes of the functional traitsi.e.,leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), leaf water content (LWC), stomatal conductance (Gs), and transpiration (E) followed the order deciduous > evergreen > coniferous. Leaf carbon and leaf C/N ratio showed the opposite pattern, coniferous > evergreen > deciduous. Chlorophyll (Chl) and photosynthetic rate (A) were highest for evergreen species, followed by deciduous and coniferous species. Also, structural equation modelling determined that morphological factors were negatively related to physiological and positively with chemical factors. Nevertheless, physiological and chemical factors were positively related to each other. The physiological traits were mainly regulated by stomatal conductance (Gs) however the morphological traits were determined by LDMC. Stoichiometry traits, such as leaf C/N, were found to be positively related to leaf carbon, and leaf N/P was found to be positively related to leaf nitrogen. The result of the leaf functional traits relationship would lead to precise prediction for the functionality of the temperate forest ecosystem at the regional scale.
Indicators of Communication Limitation in Dysarthria and Their Relation to Auditory-Perceptual Speech Symptoms: Construct Validity of the KommPaS Web App
Authors: Lehner Katharina , Ziegler Wolfram ,   KommPaS Study Group
Published: December 2021

Purpose:
Despite extensive research into communication-related parameters in dysarthria, such as intelligibility, naturalness, and perceived listener effort, the existing evidence has not been translated into a clinically applicable, comprehensive, and valid diagnostic tool so far. This study addresses Communication-Related Parameters in Speech Disorders (KommPaS), a new web-based diagnostic instrument for measuring indices of communication limitation in individuals with dysarthria through online crowdsourcing. More specifically, it answers questions about the construct validity of KommPaS.

In the first part, the interrelationship of the KommPaS variables
intelligibility, naturalness, perceived listener effort, and speech rate
were explored in order to draw a comprehensive picture of a patient's limitations and avoid the collection of redundant information. Second, the influences of motor speech symptoms on the KommPaS variables were studied in order to delineate the structural relationships between two complementary diagnostic perspectives.

Method:
One hundred persons with dysarthria of different etiologies and varying degrees of severity were examined with KommPaS to obtain layperson-based data on communication-level parameters, and with the Bogenhausen Dysarthria Scale (BoDyS) to obtain expert-based, function-level data on dysarthria symptoms. The internal structure of the KommPaS variables and their dependence on the BoDyS variables were analyzed using structural equation modeling.

Results:
Despite a high multicollinearity, all KommPaS variables were shown to provide complementary diagnostic information and their mutual interconnections were delineated in a path graph model. Regarding the influence of the BoDyS scales on the KommPaS variables, separate linear regression models revealed plausible predictor sets. A complete path model of KommPaS and BoDyS variables was developed to map the complex interplay between variables at the functional and the communication levels of dysarthria assessment.

Conclusion:
In validating a new clinical tool for the diagnostics of communication limitations in dysarthria, this study is the first to draw a comprehensive picture of how auditory-perceptual characteristics of dysarthria interact at the levels of expert-based functional and layperson-based communicative assessments.
Dynamic Norming for Systematic Analysis of Language Transcripts
Authors: Tucci Alexander , Plante Elena , Heilmann John J. , Miller Jon F. 
Published: December 2021

Purpose:
This exploratory study sought to establish the psychometric stability of a dynamic norming system using the Systematic Analysis of Language Transcripts (SALT) databases. Dynamic norming is the process by which clinicians select a subset of the normative database sample matched to their individual client's demographic characteristics.

Method:
The English Conversation and Student-Selected Story (SSS) Narrative databases from SALT were used to conduct the analyses in two phases. Phase 1 was an exploratory examination of the standard error of measure (SEM) of six clinically relevant transcript metrics at predetermined sampling intervals to determine (a) whether the dynamic norming process resulted in samples with adequate stability and (b) the minimum sample size required for stable results. Phase 2 was confirmatory, as random samples were taken from the SALT databases to simulate clinical comparison samples. These samples were examined (a) for stability of SEM estimations and (b) to confirm the sample size findings from Phase 1.

Results:
Results of Phase 1 indicated that the SEMs for the six transcript metrics across both databases were low relative to each metric's scale. Samples as small as 40–50 children in the Conversation database and 20–30 children in the SSS Narrative database resulted in stable SEM estimations. Phase 2 confirmed these findings, indicating that age bands as small as ±4 months from a given center-point resulted in stable estimations provided there were approximately 35 children or more in the comparison sample.

Conclusion:
Psychometrically stable comparison samples can be achieved using SALT's dynamic norming system that are much smaller than the standard sample size recommended in most tests of children's language.
Providing Telepractice in Schools During a Pandemic: The Experiences and Perspectives of Speech-Language Pathologists
Authors: Hall-Mills Shannon , Johnson Lakeisha , Gross Melissa , Latham Don , Everhart Nancy 
Published: December 2021

Purpose:
The main objective of this study was to gain insight into school-based speech-language pathologists' (SLPs') perspectives on and experiences with telepractice as a service delivery model at the onset of the COVID-19 global pandemic. A better understanding of the facilitating and challenging factors that belie telepractice-based services will guide the creation of training and resource development to further support remote speech-language services in schools.

Method:
Four focus group sessions using a semistructured format were conducted with 22 school-based SLPs from 14 states in the United States. The focus groups provided an opportunity for SLPs to reflect on their telepractice experiences, including pros and cons, necessary knowledge and skills, factors impacting telepractice service delivery, and student and family participation. Inductive thematic analysis was used to describe the collective experiences and perspectives of the participants.

Results:
Three themes regarding telepractice emerged: technology use, locus of control for decision making, and student and family engagement. Participants reported experiencing a range of successes and challenges when attempting to meet students' needs and identified gaps in resources and school capacity for telepractice.

Conclusions:
The findings revealed how school SLPs experienced telepractice service delivery during the remote learning portion of the 2019–2020 school year due to the onset of the COVID-19 pandemic. The study increases our understanding of factors that facilitated and challenged the effective delivery of speech-language services via telepractice. To assist SLPs in future telepractice efforts, implications for professional development and further research are provided.
Modelling an oscillator phase noise whose spectral density contains both 1/f2 and 1/f3 regions
Authors: Aqamolaei Masoumeh , Ahmadi Mohammad Mahdi 
Published: December 2021

AbstractThis paper introduces a new equation for modelling the phase noise spectrum of an oscillator. The new equation can model both of 1/f2 and 1/f3 regions in a phase noise spectrum. To verify the model and show its effectiveness, a mathematical analysis is performed on the effect of the oscillator phase noise on the performance of a continuous‐time delta‐sigma modulator. By comparing the results from the mathematical analysis with those from the Simulink simulation results, it is shown that using the proposed equation can properly predict the effect of oscillator phase noise on the modulator performance.
Non‐existence of (pm,k,1)$(p^m, k, 1)$ difference sets
Authors: Daza David , Martos C. , Trujillo C. 
Published: December 2021

AbstractLet be an additive abelian group of size . A ‐subset of is called a ‐difference set if every non‐identity element in can be written in ways as the difference of two elements in . This letter proves the non‐existence of ‐difference sets, for all prime and .
A 50nV/√Hz 2‐channel time multiplexed bio‐chopper amplifier with current‐attenuated feedback resistor
Authors: Yuk Beomjin , Kim Byeongseol , Bae Joonsung 
Published: December 2021

AbstractThis paper presents 2‐channel time‐multiplexed chopper instrument amplifier for better gain accuracy and mismatch. Mostly, the current‐attenuated feedback resistor with a duty‐cycled resistor is adopted for boosting the RC time constant. It can achieve the high pass corner frequencies between 10 Hz and 320 Hz by a 3‐bit digitally controlled resistor bank of the attenuator. As a result, the instrument amplifier achieves 0.6% gain accuracy between two time‐multiplexed channels, 100 dB CMRR, ‐40 dB crosstalk, and noise densities of 50nV/√Hz. It is implemented in a 180‐nm CMOS technology. It occupies a 0.6 mm*0.6 mm chip area and consumes 1.5μA current consumption from a 1.8 V supply for each channel.
Hybrid clustering‐based bad data detection of PMU measurements
Authors: Zhu Yanming , Xu Xiaoyuan , Yan Zheng 
Published: December 2021

AbstractPhasor measurement units (PMUs) have been widely deployed in power grids, while the bad PMU data problem threatens power system monitoring and control. This paper first gives the objective of the bad PMU data detection and gives an illustrative bad data instance. Then, the time‐series PMU data of neighbouring buses are cast as a two‐dimensional diagram, of which the spatio‐temporal correlation analysis is performed to design the normal and outlier data detection problem. Three clustering methods, including linear regression, density‐based spatial clustering of applications with noise (DBSCAN), and Gaussian mixture models (GMM) are ensembled for bad PMU data detection. Moreover, the statistical analysis and bound modification of data clustering are developed to further improve the detection accuracy. Finally, the procedure of the two‐stage bad PMU data detection is given, which consists of ensemble learning and modification. The proposed hybrid clustering‐based bad data detection is unsupervised and is applied to online bad PMU data detection with a short computation time. Visible and numerical case study results validate the outperformance of the proposed method.
Integrated control and monitoring of a smart charging station with a proposed data exchange protocol
Authors: Monadi Mehdi , Farzin Hossein , Salehizadeh Mohammad Reza , Rouzbehi Kumars 
Published: December 2021

AbstractExpansion of electric vehicles’ (EVs’) charging stations is an unavoidable requirement in sustainable cities. Provision of safe protection and control infrastructure is one of the most important requirements for a reliable charging station. In this direction, a centralized protection, control and monitoring unit (PCMU) for smart charging stations is proposed here. PCMU communicates with chargers of the station, protective devices (e.g. relays and circuit breakers), local generating units and all other devices installed at the charging station to determine the station status and to detect/locate faults that may occur in the supply grid of the station. The PCMU includes a self‐healing technique which is one of the specialties of smart systems that assists network reliability to increase. One of the major contributions of this research is to propose a protocol for data exchange between the PCMU and the other components similar to the logic used in standard of IEC 61850. Also, potential impacts of implementation of the proposed unit on the most‐widely used reliability indices are discussed. The proposed protection strategy is examined via two case studies.
Resilient control of hybrid microgrid with harmonic‐unbalanced loads under outages and faults
Authors: Hemmati Reza , Faraji Hossien 
Published: December 2021

AbstractA resilient control system in the island microgrid including AC and DC buses is designed here. The DC bus is supported by fuel cell, solar cell and battery and the AC bus is equipped with diesel generator and wind turbine. The AC bus is sectionalized into three sub‐buses that enables to continue operation when one section is not functioning. The connection between DC and AC buses is made by two parallel three‐phase lines each line made by three single‐phase inverters. The objectives are to eliminate the harmonics, the unbalanced load management, dealing with outage of resources and short circuits, providing backup strategies and supplying critical loads under all events. The simulations are performed by MATLAB/Simulink. It is demonstrated that the resilient control technique can achieve all the defined purposes at the same time. The harmonics are eliminated, the unbalanced load issues are dealt with and the microgrid has sufficient resilience against outages and faults.