bluuebunny/crossref_metadata_2025_split huggingface dataset:
Changes in PAH and 3-MCPDE contents at the various stages of Camellia oleifera seed oil refining
Authors: Cai Chenggang , Chang Guoli , Zhang Nianhua , Wang Junlin , Wang Liyuan , Wu Pinggu , Yang Dajin 
Published: May 2022

Abstract

Objectives
Polycyclic aromatic hydrocarbons (PAHs) and 3-monochloropropane-1,2-diol ester (3-MCPDE) were studied in camellia oil. It is important to study the changes in the content of PAHs and 3-MCPDE at different refining stages (from crude oil to the final refined oil product) to elucidate the influence of the refining procedures on their change.

Materials and methods
The PAHs and 3-MCPDE in camellia oil from different refining stages (from crude oil to the product) of a plant were analyzed by gas chromatography–mass spectrometry and calculated by the internal standard method.

Results
The overall PAH content was (79.64±2.43) µg/kg in crude camellia oil. After refining treatment, the PAH content decreased to (18.75±0.55) µg/kg. The 3-MCPDE content increased during the refining process from 0 mg/kg in the crude oil to 4.62 mg/kg in the refined oil product.

Conclusions
This is the first study to simultaneously monitor changes in both PAH and 3-MCPDE contents during the production of camellia oil. These results confirmed the effectiveness of the refining method on PAH removal and the increase in 3-MCPDE at high temperature. It is suggested that novel processing methods or refining parameters need further optimization to decrease the overall concentrations of PAHs and 3-MCPDE in camellia oil.
Genome of a novel Sediminibacterium discovered in association with two species of freshwater cyanobacteria from streams in Southern California
Authors: Sethuraman Arun , Stancheva Rosalina , Sanders Ciara , Caceres Lakme , Castro David , Hausknecht-Buss Hannah , Henry Simone , Johansen Haven , Kasler Antolette , Lastor Sandy , Massaro Isabelle , Mekuria Immanuel , Moron-Solano Andrea , Read Niki , Vengerova Gretchen , Zhang Andrew , Zhang Xiaoyu , Read Betsy 
Published: May 2022

Abstract
Here, we report the discovery of a novel Sediminibacterium sequenced from laboratory cultures of freshwater stream cyanobacteria from sites in Southern California, grown in BG11 medium. Our genome-wide analyses reveal a highly contiguous and complete genome (97% BUSCO) that is placed within sediminibacterial clades in phylogenomic analyses. Functional annotation indicates the presence of genes that could be involved in mutualistic/commensal relationship with associated cyanobacterial hosts.
Longitudinal Associations Between Personal Growth and Cognitive Functioning in Adulthood
Authors: Toyama Masahiro 
Published: May 2022

Abstract

Objectives
While personal growth has been found to be associated with multiple aspects of health in adulthood, its associations with cognitive functioning have not been fully understood. The present study aimed to assess both directions of such longitudinal associations.

Method
Using data from the second wave (Time 1 [T1]) and third wave (Time 2 [T2]) of the Midlife in the United States (MIDUS) study (N = 4,206; mean age = 56.0 [standard deviation (SD) = 12.3]), a longitudinal measurement model containing latent variables of episodic memory and executive function was first constructed. Built on the measurement model, a cross-lagged panel model was analyzed to assess relationships between personal growth and the two areas of cognitive functioning, in which T1 personal growth predicted residualized changes in episodic memory and executive function, and T1 episodic memory and executive function predicted change in personal growth, controlling for covariates.

Results
T1 personal growth significantly predicted smaller decreases in episodic memory, whereas it did not predict change in executive function. T1 episodic memory, but not T1 executive function, significantly predicted smaller decreases in personal growth.

Discussion
The present findings were unique, particularly implying potential longitudinal reciprocity between personal growth and episodic memory. These findings and implications can inform future research aimed at exploring approaches to promoting personal growth and cognitive functioning among aging adults.
Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification
Authors: Kanduri Chakravarthi , Pavlović Milena , Scheffer Lonneke , Motwani Keshav , Chernigovskaya Maria , Greiff Victor , Sandve Geir K 
Published: May 2022

Abstract

Background
Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where method development of more sophisticated ML approaches may be required.

Results
To identify those scenarios where a baseline ML method is able to perform well for AIRR classification, we generated a collection of synthetic AIRR benchmark data sets encompassing a wide range of data set architecture-associated and immune state–associated sequence patterns (signal) complexity. We trained ≈1,700 ML models with varying assumptions regarding immune signal on ≈1,000 data sets with a total of ≈250,000 AIRRs containing ≈46 billion TCRβ CDR3 amino acid sequences, thereby surpassing the sample sizes of current state-of-the-art AIRR-ML setups by two orders of magnitude. We found that L1-penalized logistic regression achieved high prediction accuracy even when the immune signal occurs only in 1 out of 50,000 AIR sequences.

Conclusions
We provide a reference benchmark to guide new AIRR-ML classification methodology by (i) identifying those scenarios characterized by immune signal and data set complexity, where baseline methods already achieve high prediction accuracy, and (ii) facilitating realistic expectations of the performance of AIRR-ML models given training data set properties and assumptions. Our study serves as a template for defining specialized AIRR benchmark data sets for comprehensive benchmarking of AIRR-ML methods.
Can machine learning reveal precursors of reversals of the geomagnetic axial dipole field?
Authors: Gwirtz K , Davis T , Morzfeld M , Constable C , Fournier A , Hulot G 
Published: May 2022

SUMMARY
It is well known that the axial dipole part of Earth’s magnetic field reverses polarity, so that the magnetic North Pole becomes the South Pole and vice versa. The timing of reversals is well documented for the past 160 Myr, but the conditions that lead to a reversal are still not well understood. It is not known if there are reliable ‘precursors’ of reversals (events that indicate that a reversal is upcoming) or what they might be. We investigate if machine learning (ML) techniques can reliably identify precursors of reversals based on time-series of the axial magnetic dipole field. The basic idea is to train a classifier using segments of time-series of the axial magnetic dipole. This training step requires modification of standard ML techniques to account for the fact that we are interested in rare events—a reversal is unusual, while a non-reversing field is the norm. Without our tweak, the ML classifiers lead to useless predictions. Perhaps even more importantly, the usable observational record is limited to 0–2 Ma and contains only five reversals, necessitating that we determine if the data are even sufficient to reliably train and validate an ML algorithm. To answer these questions we use several ML classifiers (linear/non-linear support vector machines and long short-term memory networks), invoke a hierarchy of numerical models (from simplified models to 3-D geodynamo simulations), and two palaeomagnetic reconstructions (PADM2M and Sint-2000). The performance of the ML classifiers varies across the models and the observational record and we provide evidence that this is not an artefact of the numerics, but rather reflects how ‘predictable’ a model or observational record is. Studying models of Earth’s magnetic field via ML classifiers thus can help with identifying shortcomings or advantages of the various models. For Earth’s magnetic field, we conclude that the ability of ML to identify precursors of reversals is limited, largely due to the small amount and low frequency resolution of data, which makes training and subsequent validation nearly impossible. Put simply: the ML techniques we tried are not currently capable of reliably identifying an axial dipole moment (ADM) precursor for geomagnetic reversals. This does not necessarily imply that such a precursor does not exist, and improvements in temporal resolution and length of ADM records may well offer better prospects in the future.
Modelling tippers on a sphere
Authors: Kruglyakov Mikhail , Kuvshinov Alexey 
Published: May 2022

SUMMARY
In the past three decades, an immense amount of magnetic field data (with periods of measurements from months to years) has been collected worldwide, either inland or at the sea bottom. This makes it tempting to estimate from these data magnetotelluric (MT) vertical transfer functions—tippers—and further probe with them the 3-D distribution of electrical conductivity on a continental or even global scale. Such a problem setup requires modelling tippers in spherical geometry. It is known that MT impedances in spherical coordinates can be modelled using different polarizations of a uniform external magnetic field. As for tippers, one needs another type of excitation because the uniform external magnetic field of any polarization contains a non-zero radial component. In the paper, we elaborate a source model, which leads to valid tippers on a whole sphere. We also present a novel, accurate and computationally efficient solver to calculate the electromagnetic field in a spherical shell or a part thereof. To illustrate the workability of both the introduced source model and the developed new solver, we present the results of modelling the ocean induction effect in tippers on a global scale. We performed computations in the models with and without oceanic sediments and show that the sediments substantially influence the results in the coastal regions where the thickness of sediments is comparable with or even exceeds the ocean’s bathymetry.
P- andS-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California
Authors: Bas Elif Ecem , Seylabi Elnaz , Yong Alan , Tehrani Hesam , Asimaki Domniki 
Published: May 2022

SUMMARYThis study uses an ensemble Kalman method for near-surface seismic site characterization of 154 network earthquake monitoring stations in California to improve the resolution of S-wave velocity (VS) and P-wave velocity (VP) profiles—up to the resolution depth—coupled with better quantification of uncertainties compared to previous site characterization studies at this network. These stations were part of the Yong et al. site characterization project, with selected stations based on future recordings of ground motions that are expected to exceed 10 per cent peak ground acceleration in 50 yr. To estimate VS and VP from experimental dispersion data, Yong et al. investigated these stations using linearized (local search and iteration) routines, and Yong et al. later studied a subset of these stations using nonlinear (global search and optimization) routines. In both studies, the selection of model parameters—that is, discretization of the VS and VP profiles with only five fixed thickness layers—was mainly based on trial and error. In contrast, this paper uses an approximate Bayesian method to assimilate experimental dispersion data and sequentially update an ensemble of particle estimates that span the VS and VP parameter spaces. Doing so, we systematically determine the most probable profiles conditioned on the experimental dispersion data, the introduced noise levels, and a priori knowledge in the form of physical constraints. We consider two configurations to discretize the soil depth from the surface to half of the maximum discernible wavelength obtained from the experimental dispersion data, namely refined and coarse models, and two initial models for each configuration to study solution multiplicity. Our results suggest that using the refined model for the top surface layers improves the resolution of near-surface site characteristics and the model’s success rate in capturing dispersion data at high frequencies. All models result in similar VS but distinct VP profiles, with increasing uncertainty at deeper layers, suggesting that the fundamental mode of Rayleigh wave dispersion data is not adequate to constrain the P-wave velocity profile and the S-wave velocity close to the resolution depth.
Regulation of tomato fruit elongation by transcription factor BZR1.7 through promotion of SUN gene expression
Authors: Yu Ting , Ai Guo , Xie Qingmin , Wang Wenqian , Song Jianwen , Wang Jiaying , Tao Jingbao , Zhang Xingyu , Hong Zonglie , Lu Yongen , Ye Jie , Zhang Yuyang , Zhang Junhong , Ye Zhibiao 
Published: May 2022

Abstract
Fruit shape is an important biological trait that is also of special commercial value in tomato. The SUN gene has been known as a key regulator of tomato fruit elongation for years, but the molecular mechanisms underlying its transcriptional regulation remain little understood. Here, a unique BZR1-like transcription factor, BZR1.7, was identified as a trans-acting factor of the SUN gene promoter that bound to the conserved E-box of the promoter to promote SUN gene expression. Overexpression of BZR1.7 in tomato led to elevated SUN gene expression and formation of elongated fruits. Plants of the BZR1.7 knockout mutant created by gene editing did not exhibit an observable fruit shape phenotype, suggesting possible functional redundancy of BZR1-like genes in tomato. There were seven BZR1-like genes in the tomato genome and overexpression of BZR1.5 and BZR1.6 led to elongated fruit phenotypes similar to those observed in the BZR1.7 overexpression lines, further supporting the notion of functional redundancy of BZR1-like genes in tomato fruit shape specification. Microscopic analysis revealed that there was a decreased number of cell layers in the fruit pericarp in the BZR1.7 overexpression lines. These findings offer new insights into the regulatory mechanism by which BZR1.7 promotes SUN gene expression and regulates fruit elongation in tomato.
Social Workers’ Self-Care Practices: Buffering the Influence of Work–Family Interferences on Burnout and Engagement
Authors: Lombardero-Posada Xoán M , Méndez-Fernández Ana B , Aguiar-Fernández Francisco X , Murcia-Álvarez Evelia , González-Fernández Antonio 
Published: May 2022

Abstract
As a reaction to specific job stressors, social workers can experience job burnout. The job demands-resources theory posits that personal characteristics would mediate the influence of job stressors on either burnout or engagement. Within this framework, this cross-sectional research aimed to analyze the relationships between work–family interferences (as predictors), self-care practices (as mediators), and burnout and engagement (as outcomes). The sample included 437 graduate social workers from Spain. Structural equation modeling showed that family–work and work–family conflicts negatively predicted self-care practices and positively predicted burnout. Professional and personal self-care practices positively predicted engagement, negatively predicted burnout, and attenuated the impact of work–family interferences on burnout and engagement. To the authors’ knowledge, the present article is the first to test the job demands-resources theory with these variables on social workers. The findings support interventions for social work students and professionals enhancing self-care practices to promote engagement and to reduce burnout, and highlight the need to decrease job stressors and enhance job resources for social workers.
Should any use of artificial cycle regimen for frozen-thawed embryo transfer in women capable of ovulation be abandoned: yes, but what’s next for FET cycle practice and research?
Authors: von Versen-Höynck Frauke , Griesinger Georg 
Published: May 2022

Abstract
Over the past decade, the use of frozen-thawed embryo transfer (FET) treatment cycles has increased substantially. The artificial (‘programmed’) cycle regimen, which suppresses ovulation, is widely used for that purpose, also in ovulatory women or women capable of ovulation, under the assumption of equivalent efficacy in terms of pregnancy achievement as compared to a natural cycle or modified natural cycle. The advantage of the artificial cycle is the easy alignment of the time point of thawing and transferring embryos with organizational necessities of the IVF laboratory, the treating doctors and the patient. However, recent data indicate that pregnancy establishment under absence of a corpus luteum as a consequence of anovulation may cause relevant maternal and fetal risks. Herein, we argue that randomized controlled trials (RCTs) are not needed to aid in the clinical decision for or against routine artificial cycle regimen use in ovulatory women. We also argue that RCTs are unlikely to answer the most burning questions of interest in that context, mostly because of lack of power and precision in detecting rare but decisive adverse outcomes (e.g. pre-eclampsia risk or long-term neonatal health outcomes). We pinpoint that, instead, large-scale observational data are better suited for that purpose. Eventually, we propose that the existing understanding and evidence is sufficient already to discourage the use of artificial cycle regimens for FET in ovulatory women or women capable of ovulation, as these may cause a strong deviation from physiology, thereby putting patient and fetus at avoidable health risk, without any apparent health benefit.
A comprehensive review on recent advancements in cooling of solar photovoltaic systems using phase change materials
Authors: Sheik Mohammed Anees , Aravindan M K , Cuce Erdem , Dasore Abhishek , Rajak Upendra , Shaik Saboor , Manokar A Muthu , Riffat Saffa 
Published: May 2022

Abstract
Due to the increasing demand for energy worldwide, photovoltaic (PV) cooling systems have become an important field of research in recent years. The most important factor affecting the performance of a solar PV cell is its operating temperature. For harvesting heat from solar PV systems, phase change material (PCM) is regarded as the most effective material. As a result, this study discusses and describes the effect of using PCM and nanoPCM (NPCM) in cooling PV cells. This research reviews the various feasible hybrid photovoltaic thermal (PVT)–PCM and PVT–NPCM methods used for cooling PV. The concept focusing on PV cooling technology is discussed where air, water and nanofluid are used as the working medium in combination with PCM and NPCM. It is observed that when high performance heat transfer and improved cooling rate are needed, active cooling methods are favoured, whereas passive cooling methods rely on themselves and don’t require extra power. It is also found that the effectiveness of applying PCM or NPCM for thermal control is heavily influenced by atmospheric air temperatures as well as the precise PCM or NPCM used. It is envisaged that this review will help new researchers better understand the qualities and capabilities of each cooling strategy. They are offered to help investigators quickly identify the basic science that led to the development of the thermal performance system and also improve the overall performance of the PV system.
Plasma exchange for severe immune-related adverse events from checkpoint inhibitors: an early window of opportunity?
Authors: Katsumoto Tamiko R , Wilson Kalin L , Giri Vinay K , Zhu Han , Anand Shuchi , Ramchandran Kavitha J , Martin Beth A , Yunce Muharrem , Muppidi Srikanth 
Published: May 2022

AbstractImmune checkpoint inhibitors (ICIs) have revolutionized the treatment of several advanced malignancies leading to durable remission in a subset of patients. Their rapidly expanding use has led to an increased frequency of immune-related adverse events (irAEs). The pathogenesis of irAEs is poorly understood but may involve aberrant activation of T cells leading to inflammatory cytokine release or production of pathogenic antibodies leading to organ damage. Severe irAEs can be extremely debilitating and, in some cases, life threatening. IrAEs may not always be corticosteroid responsive or may require excessively high, often toxic, corticosteroid doses. Therapeutic plasma exchange (PLEX) is a treatment modality that has shown promising results for the management of certain severe irAEs, including irAEs that are not mentioned in current treatment guidelines. PLEX may attenuate ongoing irAEs and prevent delayed irAEs by accelerating clearance of the ICI, or by acutely removing pathogenic antibodies, cytokines, and chemokines. Here, we summarize examples from the literature in which PLEX was successfully used for the treatment of irAEs. We posit that timing may be a critical factor and that earlier utilization of PLEX for life-threatening irAEs may result in more favorable outcomes. In individuals at high risk for irAEs, the availability of PLEX as a potential therapeutic mitigation strategy may encourage life-saving ICI use or rechallenge. Future research will be critical to better define which indications are most amenable to PLEX, particularly to establish the optimal place in the sequence of irAE therapies and to assess the ramifications of ICI removal on cancer outcomes.
When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional–structural model of oil palm
Authors: Perez Raphaël P A , Vezy Rémi , Brancheriau Loïc , Boudon Frédéric , Grand François , Ramel Merlin , Artanto Raharjo Doni , Caliman Jean-Pierre , Dauzat Jean 
Published: May 2022

Abstract
Functional–structural plant modelling approaches (FSPM) explore the relationships between the 3D structure and the physiological functioning of plants in relation to environmental conditions. In this study, we present a methodological approach that integrated architectural responses to planting design in an oil palm FSPM, and test the impact of planting design and architectural plasticity on physiological responses such as light interception and carbon assimilation. LiDAR-derived and direct measurements were performed on five planting designs to assess the phenotypic plasticity of architectural traits, and allowed evaluating the variations of the main parameters of an existing 3D plant model. Accordingly, we proposed a neighbourhood index (NI) as a simple explanatory variable of architectural plasticity, and used NI-based allometries to simulate architectural variations in 3D virtual plants. Light interception and carbon assimilation were then simulated on virtual plots reproducing the five studied designs. We found that the main traits affected by plant proximity were leaf dimensions, leaf weight and leaf erectness, whereas other structural traits like the frequency of leaflets along the rachis or biomechanical properties of leaves remained unchanged. Our simulation study highlighted model compliance to reproduce architectural plasticity and illustrated how architectural plasticity improved light interception via leaf area expansion, but how the competition for light imposed by the design can counterbalance this benefit in terms of carbon assimilation at stand scale. We conclude on the importance of planting patterns for plants with low architectural plasticity such as oil palm, and how in silico experiments can help in designing innovative planting patterns.