bluuebunny/crossref_metadata_2025_split huggingface dataset:
Entanglement-preserving measurement of the Bell parameter on a single entangled pair
Authors: Virzì Salvatore , Rebufello Enrico , Atzori Francesco , Avella Alessio , Piacentini Fabrizio , Lussana Rudi , Cusini Iris , Madonini Francesca , Villa Federica , Gramegna Marco , Cohen Eliahu , Pietro Degiovanni Ivo , Genovese Marco 
Published: August 2024

Abstract
Bell inequalities represent one of the cornerstones of quantum foundations, and a fundamental tool for quantum technologies. Although a lot of effort was put in exploring and generalizing them, because of the wave function collapse it was deemed impossible to estimate the entire Bell parameter from one entangled pair, since this would involve measuring incompatible observables on the same quantum state. Conversely, here it is reported the first implementation of a new generation of Bell inequality tests, able to extract a Bell parameter value from each entangled pair and, at the same time, preserve the pair entanglement instead of destroying it. This is obtained by exploiting sequences of weak measurements, allowing incompatible observable measurements on a quantum state without collapsing its wave function. On the fundamental side, by removing the need to choose between different measurement bases our approach stretches the concept of counterfactual definiteness, since it allows measuring the entangled pair in all the bases needed for the Bell inequality test, intrinsically eliminating the issues connected with the otherwise not-chosen bases. On the practical side, after our Bell parameter measurement the entanglement within the pair remains (basically) unaltered, hence exploitable for other quantum-technology-related or foundational purposes.
Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning
Authors: Yoo Jieun , Dickinson Jennet , Swartz Morris , Di Guglielmo Giuseppe , Bean Alice , Berry Douglas , Blanco Valentin Manuel , DiPetrillo Karri , Fahim Farah , Gray Lindsey , Hirschauer James , Kulkarni Shruti R , Lipton Ron , Maksimovic Petar , Mills Corrinne , Neubauer Mark S , Parpillon Benjamin , Pradhan Gauri , Syal Chinar , Tran Nhan , Wen Dahai , Young Aaron 
Published: August 2024

Abstract
Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High- Luminosity Large Hadron Collider. Signal processing that handles data incoming at a rate of

O

(40 MHz) and intelligently reduces the data within the pixelated region of the detector at rate will enhance physics performance at high luminosity and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first demonstration, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector’s data volume by 57.1%–75.7%. The network is designed and simulated as a custom readout integrated circuit with 28 nm CMOS technology and is expected to operate at less than 300

μ
W

with an area of less than 0.2 mm2. The temporal development of charge clusters is investigated to demonstrate possible future performance gains, and there is also a discussion of future algorithmic and technological improvements that could enhance efficiency, data reduction, and power per area.
Projections of windstorms damages under changing climate and demography for Norway
Authors: Jaison Ashbin , Michel Clio , Sorteberg Asgeir , Breivik Øyvind 
Published: August 2024

Abstract
Windstorms cause severe damages and are the dominant natural hazard responsible for insurance payouts in Norway. Using future wind speeds from twenty bias corrected regional climate model simulations from EURO-CORDEX and four damage functions, fitted on municipality level to observed insurance losses, we estimate storm related losses to increase in all Norwegian counties due to climate change. At national level, there is an increase in storm losses in the far future, but some counties and municipalities can still exhibit a decrease in losses. Under a changing climate, despite a decrease in the 98th percentile of the wind speed, the most extreme winds increase, and this drives an increase in the windstorm damages. We also show that the wind load design values may need to be re-evaluated to take into account the changing wind speeds. In addition to climate change, the impact of future exposure changes in the form of population changes using municipality-level population projections is shown to be important for determining the level of future windstorm damage risk. Overall, the loss projections show a large spread, highlighting their sensitivity to the choice of the storm-damage function and to the choice of the simulation.
Disproportionate energy disruptions afflicted rural Hispanic households during winter storm Uri
Authors: Kumar Ashwin , Ruggles Tyler H , Virgüez Edgar 
Published: August 2024

Abstract
This perspective investigates the impacts of disparate energy disruptions during Winter Storm Uri, which in 2021 caused severe power outages across the state of Texas leading to numerous deaths and billions of dollars of damage to property and infrastructure. The paper focuses on the potential injustice along ethnic lines in the distribution of power outages across the state. By analyzing data from the hourly rate of power outages aggregated by counties in Texas we find that among rural populations, Hispanic households faced substantially higher outage rates throughout the storm compared to other racial and ethnic groups. This reveals an inequitable distribution of energy disruptions. We propose that further nuances may be uncovered if more granular data were made available, delving into intra-county disparities. Only through comprehensive examination and proactive measures can we mitigate the adverse effects of future energy disruptions and safeguard the well-being and lives of all citizens, irrespective of their race, ethnicity, or socioeconomic status.
Antipoverty Interventions Across ‘Most Different’ Settings
Authors: Gubrium Erika 
Published: August 2024

Abstract
A comparative, qualitative study in the Global North and South provided ground-breaking evidence concerning the psychosocial impact of antipoverty interventions. This chapter offers a new look at synthetic findings from a targeted policy analysis and an analysis of interview data from two country cases from the study—China and Norway. The concept of biographical circumstance is used to explore the mechanisms shaping varying emotional impacts of antipoverty interventions (social assistance) in the two sites. Specific focus is placed on how intersections between personal biography and changing socioeconomic, historical, and institutional contexts led to receipt of the intervention and how this variably shaped emotional impact. The findings, analysed through an analytical lens that incorporates a focus on change over time, add nuance to and complicate earlier findings. The findings are methodologically significant to the project of comparative analysis more broadly speaking. Attention to the participant’s changing experiences over time and the historical changes circumscribing the period in focus in such a comparison also has the potential to destabilize the essentialist tendencies that otherwise might prevail given a snapshot comparison.
Neurodevelopment in the Post-Pandemic World
Authors: Colvin Molly , Reesman Jennifer Linton , Glen Tannahill 
Published: August 2024

Abstract
The COVID-19 pandemic caused worldwide and sustained educational and psychosocial disruption for children and adolescents. Schools, when they were able to remain open, quickly shifted to adopt untested virtual instruction methods. Youth were exposed to increased stress at home, including adverse childhood experiences, and mental health conditions increased. Young children who needed support for developmental disabilities and learning delays missed critical interventions and/or entered school later. Older adolescents were lost entirely from the educational system. The significant developmental and psychosocial impact on this generation of young people will be felt for decades to come. The mental health system is presently unable to meet the demands of the population, prompting a declared state of emergency for youth mental health. There are significant implications of changes in academic achievement for the identification of disability using pre-pandemic methods, especially for the fields of forensic neuropsychology and special education law. Radical educational and psychosocial changes during the COVID-19 pandemic may impact neurodevelopment from birth through the transition to adulthood, with lasting impacts on psychological and social functioning, as well as academic achievement, especially for vulnerable youth. The lessons of the COVID-19 pandemic have the potential to be a catalyst for change. In a world that anticipates future sudden and calamitous disruption due to climate change or new pandemics, the information within this book is expected to be of use both in the immediate term and the future. This information has the potential to shape progress in the fields of psychology, developmental neuroscience, sociology, public policy, and the law.
Attenuated Learning Gains
Authors: Colvin Molly , Reesman Jennifer Linton , Glen Tannahill 
Published: August 2024

Abstract
Evidence for attenuation of learning gains has been clear and consistent, with statistically significant declines in achievement, erasing decades of progress in reading and math. A dose–response relationship has been established, with students in virtual education for longer periods demonstrating more significant declines in learning progress. Learning losses have been disproportionately greatest for at-risk groups, including students of color, emerging English speakers, those with disabilities, and students from economically disadvantaged households. Preexisting achievement gaps have increased. Measures of learning progress have not fully rebounded with school reopenings, and in some cases, there has been further widening of achievement gaps, especially for vulnerable students.
Delayed Evaluation, Diagnosis, and Treatment of Childhood Disorders
Authors: Colvin Molly , Reesman Jennifer Linton , Glen Tannahill 
Published: August 2024

Abstract
The early identification and treatment of neurodevelopmental disorders is consistently associated with better long-term outcomes. During the COVID-19 pandemic, children were less likely to be screened for developmental delays by pediatricians, teachers, and school evaluation teams. Diagnosis of learning disorders, which uniquely requires evidence from standardized academic achievement assessments, has become especially complex, given the changes in academic instruction and the school environment. Changes in the psychosocial environment have complicated diagnosis of autism, attention-deficit/hyperactivity disorder, and language disorders. Children who have significant developmental delays are presenting later for clinical evaluation, and needed interventions have been deferred. When interventions were initiated, there were often changes in evidence-based service delivery, either by reduced provider availability or by changes in service delivery to adhere to public health guidelines. Delayed diagnosis and treatment of neurodevelopmental disorders has significant implications for an individual child’s long-term functioning but also has broader sociological and economic consequences.
Psychosocial Stressors and Mental Health Concerns
Authors: Colvin Molly , Reesman Jennifer Linton , Glen Tannahill 
Published: August 2024

AbstractThe evidence is now clear that pandemic-era disruption of educational and psychosocial and health care environments negatively impacted mental health for children and adolescents, especially for those with neurologic or psychiatric vulnerabilities and those in at-risk groups. Simultaneously access to mental health treatment has been reduced, initially by closures and later by increased demand, economic factors, and a shortage of mental health providers. Risk factors for adverse childhood experiences (ACEs) also increased during school closures, in part due to reduced access to typical systems for monitoring and reporting concerns. Neurocognitive performance is influenced by psychological factors as well as physical conditions, so the increase in psychiatric diagnoses and exposure to ACE risk factors will be a long-term salient issue for clinicians.
Altered Neurodevelopmental Trajectories
Authors: Colvin Molly , Reesman Jennifer Linton , Glen Tannahill 
Published: August 2024

Abstract
The developing brain grows quickly and is remarkably responsive to the environment. Because of neuroplasticity, children learn quickly and can show incredible recovery from deprivation or injury. During sensitive periods, the brain is particularly attuned to certain inputs, fueling the rapid acquisition of language and social skills. These mechanisms allow the child to become highly adapted to their environment and may confer resilience and protection in the face of change or stress. At the same time, plasticity increases the likelihood of maladaptive responses to challenges that become deeply embedded in a child’s behavioral foundation. Similarly, a lack of expected input during sensitive periods may permanently alter a child’s developmental trajectory. Differences may compound over time, leading to a widening gap between current and expected functioning. This chapter discusses how psychosocial and environmental changes during the COVID-19 pandemic, including educational disruption and stress, may have impacted brain development.
Quantile regressions as a tool to evaluate how an exposure shifts and reshapes the outcome distribution: a primer for epidemiologists
Authors: Khadka Aayush , Hebert Jillian L , Glymour M Maria , Jiang Fei , Irish Amanda , Duchowny Kate A , Vable Anusha M 
Published: August 2024

Abstract
Quantifying how an exposure affects the entire outcome distribution is often important (eg, for outcomes such as blood pressure that have nonlinear effects on long-term morbidity and mortality). Quantile regressions offer a powerful way of estimating an exposure’s relationship with the outcome distribution, but they remain underused in epidemiology. We introduce quantile regressions with a focus on distinguishing estimators for quantiles of the conditional and unconditional outcome distributions. We also present an empirical example in which we fit mean and quantile regressions to investigate educational attainment’s association with later-life systolic blood pressure (SBP). We use data on 8875 US-born respondents aged 50 years or older from the US Health and Retirement Study. Having more education was negatively associated with mean SBP. Conditional and unconditional quantile regressions both suggested a negative association between education and SBP at all levels of SBP, but the absolute magnitudes of these associations were higher at higher SBP quantiles relative to lower quantiles. In addition to showing that educational attainment shifted the SBP distribution leftward, quantile regression results revealed that education may have reshaped the SBP distribution through larger protective associations in the right tail, thus benefiting those at highest risk of cardiovascular diseases.
Effects of atmospheric CO2 concentration on transpiration and leaf elongation responses to drought in Triticum aestivum, Lolium perenne and Festuca arundinacea
Authors: Acker Victoria , Durand Jean-Louis , Perrot Cédric , Roy Eric , Frak Elzbieta , Barillot Romain 
Published: August 2024

Abstract

Background and Aims
Leaf elongation is vital for productivity of Poaceae species, influenced by atmospheric CO2 concentration ([CO2]) and climate-induced water availability changes. Although [CO2] mitigates the effects of drought on reducing transpiration per unit leaf area, it also increases total leaf area and water use. These complex interactions associated with leaf growth pose challenges in anticipating climate change effects. This study aims to assess [CO2] effects on leaf growth response to drought in perennial ryegrass (Lolium perenne), tall fescue (Festuca arundinacea) and wheat (Triticum aestivum).

Methods
Plants were cultivated in growth chambers with [CO2] at 200 or 800 ppm. At leaf six to seven unfolding, half of the plants were subjected to severe drought treatment. Leaf elongation rate (LER) was measured daily, whereas plant transpiration was continuously recorded gravimetrically. Additionally, water-soluble carbohydrate (WSC) content along with water and osmotic potentials in the leaf growing zone were measured at drought onset, mid-drought and leaf growth cessation.

Key Results
Elevated [CO2] mitigated drought impacts on LER and delayed growth cessation across species. A positive correlation between LER and soil relative water content (SRWC) was observed. At the same SRWC, perennial grasses exhibited a higher LER with elevated [CO2], probably due to enhanced stomatal regulation. Despite stomatal closure and WSC accumulation, CO2 did not influence nighttime water potential or osmotic potential. The marked increase in leaf area across species resulted in similar (wheat and tall fescue) or higher (ryegrass) total water use by the end of the experiment, under both watered and unwatered conditions.

Conclusions
Elevated [CO2] mitigates the adverse effects of drought on leaf elongation in three Poaceae species, due to its impact on plant transpiration. Overall, these findings provide valuable insights into CO2 and drought interactions that may help anticipate plant responses to climate change.
MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering
Authors: Zhu Keyun , Huang Mengting , Wang Yimeng , Gu Yaxin , Li Weihua , Liu Guixia , Tang Yun 
Published: August 2024

Abstract
Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.
A pan-cancer interrogation of intronic polyadenylation and its association with cancer characteristics
Authors: Liu Liang , Sun Peiqing , Zhang Wei 
Published: August 2024

Abstract
3′UTR-APAs have been extensively studied, but intronic polyadenylations (IPAs) remain largely unexplored. We characterized the profiles of 22 260 IPAs in 9679 patient samples across 32 cancer types from the Cancer Genome Atlas cohort. By comparing tumor and paired normal tissues, we identified 180 ~ 4645 dysregulated IPAs in 132 ~ 2249 genes in each of 690 patient tumors from 22 cancer types that showed consistent patterns within individual cancer types. We selected 2741 genes that showed consistently patterns across cancer types, including 1834 pan-cancer tumor-enriched and 907 tumor-depleted IPA genes; the former were amply represented in the functional pathways such as deoxyribonucleic acid damage repair. Expression of IPA isoforms was associated with tumor mutation burden and patient characteristics (e.g. sex, race, cancer stages, and subtypes) in cancer-specific and feature-specific manners, and could be a more accurate prognostic marker than gene expression (summary of all isoforms). In summary, our study reveals the roles and the clinical relevance of tumor-associated IPAs.
New GO-based measures in multiple network alignment
Authors: Yazdani Kimia , Mousapour Reza , Hayes Wayne B 
Published: August 2024

Abstract

Motivation
Protein–protein interaction (PPI) networks provide valuable insights into the function of biological systems. Aligning multiple PPI networks may expose relationships beyond those observable by pairwise comparisons. However, assessing the biological quality of multiple network alignments is a challenging problem.

Results
We propose two new measures to evaluate the quality of multiple network alignments using functional information from Gene Ontology (GO) terms. When aligning multiple real PPI networks across species, we observe that both measures are highly correlated with objective quality indicators, such as common orthologs. Additionally, our measures strongly correlate with an alignment’s ability to predict novel GO annotations, which is a unique advantage over existing GO-based measures.

Availability and implementation
The scripts and the links to the raw and alignment data can be accessed at https://github.com/kimiayazdani/GO_Measures.git
Toward a set of essential biodiversity variables for assessing change in mountains globally
Authors: Schmeller Dirk S , Thornton James M , Urbach Davnah , Alexander Jake , Jetz Walter , Kulonen Aino , Mills Robert T E , Notornicola Claudia , Palazzi Elisa , Pauli Harald , Randin Christophe , Rosbakh Sergey , Sayre Roger , Tehrani Nasrin Amini , Verbiest William W M , Walker Tom W N , Wipf Sonja , Adler Carolina 
Published: August 2024

Abstract
Mountain regions harbor unique and rich biodiversity, forming an important part of our global life support system. This rich biodiversity underpins the ecological intactness and functioning of mountain ecosystems, which are imperative for the provision of key ecosystem services. A considerable amount of data are required to assess ecological intactness and ecosystem functioning and, given the profound anthropogenic pressures many mountain regions are being subjected to, are urgently needed. However, data on mountain biodiversity remain lacking. The essential biodiversity variables (EBVs) framework can help focus efforts related to detecting, investigating, predicting, and managing global biodiversity change, but has not yet been considered in the context of mountains. Here, we review key biological processes and physical phenomena that strongly influence mountain biodiversity and ecosystems and elucidate their associations with potential mountain EBVs. We identify seven EBVs of highest relevance for tracking and understanding the most critical drivers and responses of mountain biodiversity change. If they are implemented, the selected EBVs will contribute useful information to inform management and policy interventions seeking to halt mountain biodiversity loss and maintain functional mountain ecosystems.