bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-family Living Arrangements Among Young Adults in the United States
Authors: Jeffers Kristen , Esteve Albert , Batyra Ewa 
Published: March 2024

Abstract
The share of young adults living in married-couple family households in the USA has declined in recent decades. Research on alternative living arrangements focuses on cohabitation among unmarried couples and parent–adult child coresidence. Less is known about trends in non-family living arrangements and the characteristics of young adults living with non-relatives. This study documents trends over time in non-family living arrangements among young adults in the USA and examines the sociodemographic profile of those living with non-relatives. Using pooled US Census and American Community Survey microdata from 1990 to 2019, the authors document age patterns in non-family living arrangements over time and use logistic regression to estimate the likelihood of living with non-family based on individual-level characteristics. Results indicate that non-family living among young adults has increased over time, and that the arrangement is associated with markers of both advantage and disadvantage. Differences across age groups explain some of these mixed results. Trends among younger groups are linked to socioeconomic patterns around family formation. Among older groups, the demographic and labor force characteristics of the foreign born and constraints of their kin availability may be driving trends. The exploratory analysis provides relevant evidence around an increasingly common living arrangement in the USA and also identifies several areas for future research on living arrangements among young adults and the implications of these trends.
Cusped Borel Anosov representations with positivity
Authors: Lee Gye-Seon , Zhang Tengren 
Published: March 2024

AbstractWe show that if a cusped Borel Anosov representation from a lattice $$\Gamma \subset \textsf{PGL}_2({{\,\mathrm{\mathbb {R}}\,}})$$

Γ
⊂

PGL
2

(

R

)

to $$\textsf{PGL}_d({{\,\mathrm{\mathbb {R}}\,}})$$

PGL
d

(

R

)

contains a unipotent element with a single Jordan block in its image, then it is necessarily a (cusped) Hitchin representation. We also show that the amalgamation of a Hitchin representation with a cusped Borel Anosov representation that is not Hitchin is never cusped Borel Anosov.
Morphometric analysis of wild potato leaves
Authors: Diaz-Garcia Gabriela , Lozoya-Saldaña Hector , Bamberg John , Diaz-Garcia Luis 
Published: March 2024

AbstractTo catalog and promote the conservation and use of crop wild relatives, comprehensive phenotypic and genotypic information must be available. Plant genotyping has the power to resolve the phylogenetic relationships between crop wild relatives, quantify genetic diversity, and identify marker-trait associations for expedited molecular breeding. However, access to cost-effective genotyping strategies is often limited in underutilized crops and crop wild relatives. Potato landraces and wild species, distributed throughout Central and South America, exhibit remarkable phenotypic diversity and are an invaluable source of resistance to pests and pathogens. Unfortunately, very limited information is available for these germplasm resources, particularly regarding phenotypic diversity and potential use as trait donors. In this work, more than 150 accessions corresponding to 12 species of wild and cultivated potatoes, collected from different sites across the American continent, were analyzed using computer vision and morphometric methods to evaluate leaf size and shape. In total, more than 1100 leaves and leaflets were processed and analyzed for nine traits related to size, shape, and color. The results produced in this study provided a visual depiction of the extensive variability among potato wild species and enabled a precise quantification of leaf phenotypic differences, including shape, color, area, perimeter, length, width, aspect ratio, convexity, and circularity. We also discussed the application and utility of inexpensive but comprehensive morphometric approaches to catalog and study the diversity of crop wild relatives. Finally, this study provided insights for further experimental research looking into the potential role of leaf size and shape variation in plant–insect interactions, agronomic productivity, and adaptation.
Intolerance of Uncertainty and Anxiety (but not Alexithymia) Mediate the Association Between Autistic Traits and Quality of Life
Authors: Lin Yeju , Mason David , Hirsch Colette , Happé Francesca 
Published: March 2024

AbstractPrevious research has indicated that autistic individuals report lower quality of life (QoL) than non-autistic people. It is unclear whether it is the autism traits themselves or co-occurring thinking styles or mental health difficulties that most impair QoL. This study tested a hypothesised model to explore how ‘intolerance of uncertainty’ (IU), alexithymia (difficulty in identifying and describing own emotions), and anxiety play into the association between autistic traits and QoL. Online survey data were analysed from 116 autistic and 51 non-autistic adults who completed six standardised questionnaires measuring autistic traits, alexithymia, IU, anxiety and QoL (physical health, psychological health, social relations, and environment domains). The autistic group reported higher scores for alexithymia, IU and anxiety, and lower scores for QoL across domains, compared to the non-autistic group. Across the entire sample, autistic traits, alexithymia, IU and anxiety were positively correlated with one another, and negatively related to the four domains of QoL. Finally, IU and anxiety partially serially mediated the pathways from autistic traits to physical health and environment domains of QoL, and fully mediated the pathways from autistic traits to psychological health and social relations domains of QoL, across the full sample. The lower QoL experienced by autistic people may be explained in part by the mediating effect of both IU and anxiety (but not alexithymia). This study highlights the need for evidence-based interventions to address both IU and anxiety to improve QoL for autistic people/those with high levels of autistic traits.
First Language Lexical Attrition in a First Language Setting: A Multi-Measure Approach Testing Teachers of English
Authors: Ma Yueqingzhou , Vanek Norbert 
Published: March 2024

AbstractResearch on first language (L1) attrition typically focuses on immigrant populations in their second language (L2) environment, yet we know comparably little about L1 attrition in the L1 setting. This study used two lexical tasks to test L1 attrition, a time-sensitive word decision task and a video retelling. Chinese teachers of English vs. Chinese teachers of other subjects (N = 25/group) were recruited at a secondary school in China. The aim was to provide an exploratory basis of the L2 influence on L1 lexical attrition in the L1 environment, both on the level of lexical comprehension and production. Mixed-effects models were used to analyse multiple measures including response accuracy and reaction times in comprehension, and lexical diversity, density, sophistication, and accuracy in production. The results showed Chinese teachers’ L1 lexical attrition in the form of longer response times to high-frequency Chinese words compared to non-English Chinese teachers, and the use of significantly fewer sophisticated words in their retellings. Also, teachers of English were faster and more accurate in decisions about Chinese borrowings from English, suggesting L2-driven influence on their mental lexicon. Considering participants’ background information, analyses showed that increased L2 exposure and frequency of use can predict L1 lexical attrition.
Inhibition of BMP signaling pathway induced senescence and calcification in anaplastic meningioma
Authors: Yokogami Kiyotaka , Watanabe Takashi , Yamashita Shinji , Mizuguchi Asako , Takeshima Hideo 
Published: March 2024

Abstract
Purpose
Meningiomas are the most common type of brain tumors and are generally benign, but malignant atypical meningiomas and anaplastic meningiomas frequently recur with poor prognosis. The metabolism of meningiomas is little known, so few effective treatment options other than surgery and radiation are available, and the targets for treatment of recurrence are not well defined. The Aim of this paper is to find the therapeutic target.

Methods
The effects of bone morphogenetic protein (BMP) signal inhibitor (K02288) and upstream regulator Gremlin2 (GREM2) on meningioma’s growth and senescence were examined. In brief, we examined as follows: 1) Proliferation assay by inhibiting BMP signaling. 2) Comprehensive analysis of forced expression GREM2.3) Correlation between GREM2 mRNA expression and proliferation marker in 87 of our clinical samples. 4) Enrichment analysis between GREM2 high/low expressed groups using RNA-seq data (42 cases) from the public database GREIN. 5) Changes in metabolites and senescence markers associated with BMP signal suppression.

Results
Inhibitors of BMP receptor (BMPR1A) and forced expression of GREM2 shifted tryptophan metabolism from kynurenine/quinolinic acid production to serotonin production in malignant meningiomas, reduced NAD + /NADH production, decreased gene cluster expression involved in oxidative phosphorylation, and caused decrease in ATP. Finally, malignant meningiomas underwent cellular senescence, decreased proliferation, and eventually formed psammoma bodies. Reanalyzed RNA-seq data of clinical samples obtained from GREIN showed that increased expression of GREM2 decreased the expression of genes involved in oxidative phosphorylation, similar to our experimental results.

Conclusions
The GREM2-BMPR1A-tryptophan metabolic pathway in meningiomas is a potential new therapeutic target.
FEASE: Feature Selection and Enhancement Networks for Action Recognition
Authors: Zhou Lu , Lu Yuanyao , Jiang Haiyang 
Published: March 2024

AbstractReinforcement of motor features is necessary in action recognition tasks. In this work, we propose an efficient feature reinforcement model, termed as Feature Selection and Enhancement Networks (FEASE-Net). The core of our FEASE-Net is the use of the FEASE module to adaptively capture input features at multi-scales and reinforce them globally. FEASE module is composed of two sub-module, Feature Selection (FS) and Feature Enhancement (FE). The FS focuses on adaptive attention and selection of input features through a multi-scale structure with an attention mechanism, and FE employs channel attention to enhance the global useful feature information. To assess the effectiveness of FEASE-Net, we undertake a series of extensive experiments on two benchmark datasets, namely Kinetics 400 and Something-Something V2. Our proposed FEASE-Net can achieve a competitive performance compared with previous state-of-the-art methods that use similar backbones.
CEEMDAN-Based Hybrid Machine Learning Models for Time Series Forecasting Using MARS Algorithm and PSO-Optimization
Authors: Garai Sandip , Paul Ranjit Kumar , Yeasin Md , Paul A. K. 
Published: March 2024

AbstractAccurate prediction of time series data is crucial for informed decision-making and economic development. However, predicting noisy time series data is a challenging task due to their irregularity and complex trends. In the past, several attempts have been made to model complex time series data using both stochastic and machine learning techniques. This study proposed a CEEMDAN-based hybrid machine learning algorithm combined with stochastic models to capture the volatility of weekly potato price in major markets of India. The smooth decomposed component is predicted using stochastic models, while the coarser components, selected using MARS, are fitted into two different machine learning algorithms. The final predictions for the original series are obtained using optimization techniques such as PSO. The performance of the proposed algorithm is measured using various metrics, and it is found that the optimization-based combination of models outperforms the individual counterparts. Overall, this study presents a promising approach to predict price series using a hybrid model combining stochastic and machine learning techniques, with feature selection and optimization techniques for improved performance.
HF-YOLO: Advanced Pedestrian Detection Model with Feature Fusion and Imbalance Resolution
Authors: Pan Lihu , Diao Jianzhong , Wang Zhengkui , Peng Shouxin , Zhao Cunhui 
Published: March 2024

AbstractPedestrian detection is crucial for various applications, including intelligent transportation and video surveillance systems. Although recent research has advanced pedestrian detection models like the YOLO series, they still face limitations in handling diverse pedestrian scales, leading to performance challenges. To address these issues, we propose HF-YOLO, an advanced pedestrian detection model. HF-YOLO tackles the complexities of pedestrian detection in complex scenes by addressing scale variations and occlusions among pedestrians. In the feature fusion stage, our algorithm leverages both shallow localization information and deep semantic information. This involves fusing P2 layer features and adding a high-resolution detection layer, significantly improving the detection of small-scale pedestrians and occluded instances. To enhance feature representation, HF-YOLO incorporates the HardSwish activation function, introducing more non-linear factors and strengthening the model’s ability to represent complex and discriminative features. Additionally, to address regression imbalance, a balance factor is introduced to the CIoU loss function. This modification effectively resolves the imbalance problem and enhances pedestrian localization accuracy. Experimental results demonstrate the effectiveness of our proposed algorithm. HF-YOLO achieves notable improvements, including a 3.52% increase in average precision, a 1.35% boost in accuracy, and a 4.83% enhancement in recall. Moreover, the algorithm maintains real-time performance with a detection time of 8.5ms, meeting the stringent requirements of real-time applications.
DE3TC: Detecting Events with Effective Event Type Information and Context
Authors: Liu Boyang , Rao Guozheng , Wang Xin , Zhang Li , Cong Qing 
Published: March 2024

AbstractEvent Detection (ED) is a crucial information extraction task that aims to identify the event triggers and classify them into predefined event types. However, most existing methods did not perform well when processing events with implicit triggers. And most methods considered ED as a sentence-level task, lacking effective context for event semantics. Moreover, how to maintain good performance under low resource conditions still needs further study. To address these problems, we propose a novel end-to-end ED model called DE3TC, which Detects Events with Effective Event Type Information and Context. We construct an event type-specific Clue to capture the interaction between event type name and trigger words, providing event type information for implicit triggers. For accessing the effective context of event semantics for sentence-level ED, we consider the correlations between types and select similar types’ descriptions as context. With contextualized representation from a contextual encoder, DE3TC learns the event type information for all events including implicit ones. And it performs sentence-level ED efficiently with effective contexts. The empirical results on ACE 2005 and MAVEN datasets show that: (i) DE3TC obtains state-of-the-art performance compared with previous methods. (ii) DE3TC is also excelled under low-resource conditions.
On a Problem of Conrad on Riesz Space Structures
Authors: Lenzi Giacomo 
Published: March 2024

AbstractThis paper is concerned with Riesz space structures on a lattice ordered abelian group, continuing a line of research conducted by the author and the collaborators Antonio Di Nola and Gaetano Vitale. First we prove a statement in a paper of Paul Conrad (given without proof) that every non-archimedean totally ordered abelian group has at least two Riesz space structures, if any. Then, as a main result, we prove that there is a non-archimedean lattice ordered abelian group with strong unit having only one Riesz space structure. This gives a solution to a problem posed in a paper of Conrad dating back to 1975. Then we combine these results and the categorial equivalence between lattice ordered abelian groups with strong unit and MV-algebras (due to Daniele Mundici) and the one between Riesz spaces with strong unit and Riesz MV-algebras (due to Di Nola and Ioana Leustean). By combining these tools, we prove that every non-semisimple totally ordered MV-algebra has at least two Riesz MV-algebra structures, if any, and that there is a non-semisimple MV-algebra with only one Riesz MV-algebra structure.
Temporary growth cessation of wheat roots following defoliation
Authors: Han Eusun , Kirkegaard John A. , Thorup-Kristensen Kristian 
Published: March 2024

Abstract
Background and aims
Defoliation triggers the remobilisation of root reserves to generate new leaves which can affect root growth until the shoot resumes net assimilation. However, the duration of root growth cessation and its impact on resource uptake potential is uncertain.

Methods
Winter wheat was established in a 4 m high outdoor rhizobox facility equipped with imaging panels, sensors, and access points for tracer-labelling. The wheat was defoliated in autumn at early tillering and roots were imaged at a high-time resolution and analyzed by deep learning segmentation. The water and nitrogen (N) uptake were measured using time-domain reflectometer (TDR) sensors and 2H and 15N isotopes.

Results
Root penetration of wheat paused for 269 °C days (20 days) following defoliation after which it resumed at a similar rate to un-defoliated plants (1.8 mm °C days−1). This caused a substantial decrease in root density with an associated reduction in water and N uptake at maturity, especially from deeper soil layers (>2 m).

Conclusions
Our results have significant implications for managing the grazing of dual-purpose crops to balance the interplay between canopy removal and the capacity of deep roots to provide water and N for yield recovery.
Variations on the Kripke Trick
Authors: Rybakov Mikhail , Shkatov Dmitry 
Published: March 2024

Abstract
In the early 1960s, to prove undecidability of monadic fragments of sublogics of the predicate modal logic

$$\textbf{QS5}$$

QS
5

that include the classical predicate logic

$$\textbf{QCl}$$

QCl

, Saul Kripke showed how a classical atomic formula with a binary predicate letter can be simulated by a monadic modal formula. We consider adaptations of Kripke’s simulation, which we call the Kripke trick, to various modal and superintuitionistic predicate logics not considered by Kripke. We also discuss settings where the Kripke trick does not work and where, as a result, decidability of monadic modal predicate logics can be obtained.
Accelerating the detection of DNA differentially methylated regions using multiple GPUs
Authors: Reaño Carlos , Olanda Ricardo , Baydal Elvira , Pérez Mariano , Orduña Juan M. 
Published: March 2024

AbstractDNA methylation analysis has become an important topic in the study of human health. In previous work, we developed a suite of tools to perform this analysis. It includes HPG-Dhunter, a web-based tool for automatic detection of differentially methylated regions (DMRs) between different samples. The back-end of that tool receives an undefined number of simultaneous requests to detect DMRs on different datasets. Currently, simultaneous requests are queued and processed one at a time. This paper proposes a parallel architecture where multiple daemons serve requests simultaneously. Daemons can also share the same physical GPUs. A scheduler manages requests and forwards them to daemons. The number of daemons per GPU is configurable, thus adapting the architecture to the available hardware. Results show that the proposed parallel architecture hugely reduces the execution time. Furthermore, the speedup increases proportionally to the number of available GPUs (up to 7.47x in our experimental setup).
MIB Guides: Measuring the Immunoreactivity of Radioimmunoconjugates
Authors: Delaney Samantha , Grimaldi Camilla , Houghton Jacob L. , Zeglis Brian M. 
Published: March 2024

AbstractImmunoglobulins, both full-length antibodies and smaller antibody fragments, have long been regarded as effective platforms for diagnostic and therapeutic radiopharmaceuticals. The construction of radiolabeled immunoglobulins (i.e., radioimmunoconjugates) requires the manipulation of the biomolecule through the attachment of a radiohalogen or the bioconjugation of a chelator that is subsequently used to coordinate a radiometal. Both synthetic approaches have historically relied upon the stochastic modification of amino acids within the immunoglobulin, a process which poses a risk to the structural and functional integrity of the biomolecule itself. Not surprisingly, radioimmunoconjugates with impaired antigen binding capacity will inevitably exhibit suboptimal in vivo performance. As a result, the biological characterization of any newly synthesized radioimmunoconjugate must include an assessment of whether it has retained its ability to bind its antigen. Herein, we provide straightforward and concise protocols for three assays that can be used to determine the immunoreactivity of a radioimmunoconjugate: (1) a cell-based linear extrapolation assay; (2) a cell-based antigen saturation assay; and (3) a resin- or bead-based assay. In addition, we will provide a critical analysis of the relative merits of each assay, an examination of the inherent limitations of immunoreactivity assays in general, and a discussion of other approaches that may be used to interrogate the biological behavior of radioimmunoconjugates.