bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reconstructing the growth index $$\gamma $$ with Gaussian processes
Authors: Oliveira Fernanda , Avila Felipe , Bernui Armando , Bonilla Alexander , Nunes Rafael C. 
Published: June 2024

AbstractAlternative cosmological models have been proposed to alleviate the tensions reported in the concordance cosmological model, or to explain the current accelerated phase of the universe. One way to distinguish between General Relativity and modified gravity models is using current astronomical data to measure the growth index $$\gamma $$
γ
, a parameter related to the growth of matter perturbations, which behaves differently in different metric theories. We propose a model independent methodology for determining $$\gamma $$
γ
, where our analyses combine diverse cosmological data sets, namely $$\{ f(z_i) \}$$

{
f

(

z
i

)

}

, $$\{ [f\sigma _8](z_i) \}$$

{

[
f

σ
8

]

(

z
i

)

}

, and $$\{ H(z_i) \}$$

{
H

(

z
i

)

}

, and use Gaussian Processes, a non-parametric approach suitable to reconstruct functions. This methodology is a new consistency test for $$\gamma $$
γ
constant. Our results show that, for the redshift interval $$0< z < 1$$

0
<
z
<
1

, $$\gamma $$
γ
is consistent with the constant value $$\gamma = 0.55$$

γ
=
0.55

, expected in General Relativity theory, within $$2 \sigma $$

2
σ

confidence level (CL). Moreover, we find $$\gamma (z=0)$$

γ
(
z
=
0
)

= 0.311 $$\pm 0.144 $$

±
0.144

and $$\gamma (z=0) = 0.609 \pm 0.200$$

γ
(
z
=
0
)
=
0.609
±
0.200

for the reconstructions using the $$\{ f(z_i) \}$$

{
f

(

z
i

)

}

and $$\{ [f\sigma _8](z_i) \}$$

{

[
f

σ
8

]

(

z
i

)

}

data sets, respectively, values that also agree at a 2$$\sigma $$
σ
CL with $$\gamma = 0.55$$

γ
=
0.55

. Our methodology and analyses can be considered as an alternative approach in light of the current discussion in the literature that suggests a possible evidence for the growth index evolution.
IceCube – Neutrinos in Deep Ice
Authors: Bukhari Habib , Chakraborty Dipam , Eller Philipp , Ito Takuya , Shugaev Maxim V. , Ørsøe Rasmus 
Published: June 2024

AbstractDuring the public Kaggle competition “IceCube – Neutrinos in Deep Ice”, thousands of reconstruction algorithms were created and submitted, aiming to estimate the direction of neutrino events recorded by the IceCube detector. Here we describe in detail the three ultimate best, award-winning solutions. The data handling, architecture, and training process of each of these machine learning models is laid out, followed up by an in-depth comparison of the performance on the Kaggle datatset. We show that on cascade events in IceCube above 10 TeV, the best Kaggle solution is able to achieve an angular resolution of better than 5$$^{\circ }$$

∘

, and for tracks correspondingly better than 0.5$$^{\circ }$$

∘

. These results indicate that the Kaggle solutions perform at a level comparable to the current state-of-the-art in the field, and that they may even be able to outperform existing reconstruction resolutions for certain types of events.
Late time decay of scalar and Dirac fields around an asymptotically de Sitter black hole in the Euler–Heisenberg electrodynamics
Authors: Bolokhov S. V. 
Published: June 2024

AbstractWe compute the quasinormal modes of massive scalar and Dirac fields within the framework of asymptotically de Sitter black holes in Euler–Heisenberg non-linear electrodynamics. We pay particular attention to the regime $$\mu M/m_{P}^2 \gg 1,$$

μ
M
/

m

P

2

≫
1
,

where $$\mu $$
μ
and M denote the masses of the field and the black hole, respectively, and $$m_{P}$$

m
P

represents the Planck mass, covering a range from primordial to large astrophysical black holes. Through time-domain integration, we demonstrate that, contrary to the asymptotically flat case, the quasinormal modes also dictate the asymptotic decay of fields. Employing the 6th order WKB formula, we derive a precise analytic approximation for quasinormal modes in the regime $$\mu M/m_{P}^2 \gg 1$$

μ
M
/

m

P

2

≫
1

without resorting to expansion in terms of the inverse multipole number. This analytic expression takes on a concise form in the limit of linear electrodynamics, represented by the Reissner–Nordström black holes. Our numerical analysis indicates the stability of the fields under consideration against linear perturbations.
Hybrid inflation from supersymmetry breaking
Authors: Aldabergenov Yermek , Antoniadis Ignatios , Chatrabhuti Auttakit , Isono Hiroshi 
Published: June 2024

AbstractWe extend a recently proposed framework, dubbed inflation by supersymmetry breaking, to hybrid inflation by introducing a waterfall field that allows to decouple the supersymmetry breaking scale in the observable sector from the inflation scale, while keeping intact the inflation sector and its successful predictions: naturally small slow-roll parameters, small field initial conditions and absence of the pseudo-scalar companion of the inflaton, in terms of one free parameter which is the first order correction to the inflaton Kähler potential. During inflation, supersymmetry is spontaneously broken with the inflaton being the superpartner of the goldstino, together with a massive vector that gauges the R-symmetry. Inflation arises around the maximum of the scalar potential at the origin where R-symmetry is unbroken. Moreover, a nearby minimum with tuneable vacuum energy can be accommodated by introducing a second order correction to the Kähler potential. The inflaton sector can also play the role of the supersymmetry breaking ‘hidden’ sector when coupled to the (supersymmetric) Standard Model, predicting a superheavy superparticle spectrum near the inflation scale. Here we show that the introduction of a waterfall field provides a natural way to end inflation and allows for a scale separation between supersymmetry breaking and inflation. Moreover, the study of the global vacuum describing low energy Standard Model physics can be done in a perturbative way within a region of the parameter space of the model.
On the bias in iterative quantum amplitude estimation
Authors: Miyamoto Koichi 
Published: June 2024

AbstractQuantum amplitude estimation (QAE) is a pivotal quantum algorithm to estimate the squared amplitude a of the target basis state in a quantum state $|{\Phi}\rangle $
|
Φ
〉
. Various improvements on the original quantum phase estimation-based QAE have been proposed for resource reduction. One of such improved versions is iterative quantum amplitude estimation (IQAE), which outputs an estimate â of a through the iterated rounds of the measurements on the quantum states like $G^{k}|{\Phi}\rangle $

G
k

|
Φ
〉
, with the number k of operations of the Grover operator G (the Grover number) and the shot number determined adaptively. This paper investigates the bias in IQAE. Through the numerical experiments to simulate IQAE, we reveal that the estimate by IQAE is biased and the bias is enhanced for some specific values of a. We see that the termination criterion in IQAE that the estimated accuracy of â falls below the threshold is a source of the bias. Besides, we observe that $k_{\mathrm{fin}}$

k
fin

, the Grover number in the final round, and $f_{\mathrm{fin}}$

f
fin

, a quantity affecting the probability distribution of measurement outcomes in the final round, are the key factors to determine the bias, and the bias enhancement for specific values of a is due to the skewed distribution of $(k_{\mathrm{fin}},f_{\mathrm{fin}})$
(

k
fin

,

f
fin

)
. We also present a bias mitigation method: just re-executing the final round with the Grover number and the shot number fixed.
Production of Sulphur-Doped Graphene Oxide as an Anode Material for Na-Ion Batteries
Authors: Almarzoge MohammedMustafa , Gencten Metin , Ozsin Gamzenur 
Published: June 2024

Sodium-ion batteries have been the focus of interest in recent years due to abundance and cost-effectiveness of sodium resources globally as opposed to lithium. In this work, sulfur-doped graphene oxide (SGO) was synthesized using a straightforward, one-step, cost-effective, and eco-friendly chronoamperometric method at room temperature. The resulting powder was then utilized as active anode material for Na-ion batteries. The surface of the synthesized SGO powder, which consists of approximately three layers with 19 sp2 hybridized carbon rings and a domain size of about 50 nm, is covalently doped with –C-SOx-C- (x = 2,3) groups. The deduced diffusion coefficient from electrochemical impedance spectroscopy and galvanostatic intermittent titration technique measurements for SGO as anode in NIBs is in the range of 10−11–10−12 cm2.s−1. At 0.1 C rate, the initial discharge capacity recorded 256.7 mAh.g−1 at 0.1 C rate. In addition, the capacity retention for long-term cycling of 100 cycles at 2 C rate was 99.85%. The unique structure of SGO allows us to achieve satisfactory anode performance in capacity and rate capability, with potential for further enhancement.
Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients
Authors: Harvey Annabelle , Moreau Clara A. , Kumar Kuldeep , Huguet Guillaume , Urchs Sebastian G.W. , Sharmarke Hanad , Jizi Khadije , Martin Charles-Olivier , Younis Nadine , Tamer Petra , Martineau Jean-Louis , Orban Pierre , Silva Ana Isabel , Hall Jeremy , van den Bree Marianne B.M. , Owen Michael J. , Linden David E.J. , Lippé Sarah , Bearden Carrie E. , Dumas Guillaume , Jacquemont Sébastien , Bellec Pierre 
Published: June 2024

Abstract
There is a growing interest in using machine learning (ML) models to perform automatic diagnosis of psychiatric conditions; however, generalising the prediction of ML models to completely independent data can lead to sharp decrease in performance. Patients with different psychiatric diagnoses have traditionally been studied independently, yet there is a growing recognition of neuroimaging signatures shared across them as well as rare genetic copy number variants (CNVs). In this work, we assess the potential of multi-task learning (MTL) to improve accuracy by characterising multiple related conditions with a single model, making use of information shared across diagnostic categories and exposing the model to a larger and more diverse dataset. As a proof of concept, we first established the efficacy of MTL in a context where there is clearly information shared across tasks: the same target (age or sex) is predicted at different sites of data collection in a large functional magnetic resonance imaging (fMRI) dataset compiled from multiple studies. MTL generally led to substantial gains relative to independent prediction at each site. Performing scaling experiments on the UK Biobank, we observed that performance was highly dependent on sample size: for large sample sizes (N &gt; 6000) sex prediction was better using MTL across three sites (N = K per site) than prediction at a single site (N = 3K), but for small samples (N &lt; 500) MTL was actually detrimental for age prediction. We then used established machine-learning methods to benchmark the diagnostic accuracy of each of the 7 CNVs (N = 19–103) and 4 psychiatric conditions (N = 44–472) independently, replicating the accuracy previously reported in the literature on psychiatric conditions. We observed that MTL hurt performance when applied across the full set of diagnoses, and complementary analyses failed to identify pairs of conditions which would benefit from MTL. Taken together, our results show that if a successful multi-task diagnostic model of psychiatric conditions were to be developed with resting-state fMRI, it would likely require datasets with thousands of patients across different diagnoses.
Mental health and the ballot box: A correlational analysis of Google searches for mental health and national election periods in the United States and the United Kingdom from 2008 to 2020
Authors: Smith Alexander J. , Graña Juan , Alibudbud Rowalt , Ventriglio Antonio , Buadze Anna , Liebrenz Michael 
Published: June 2024

Background: In electoral contexts, mental health topics have recently attracted sociopolitical relevance, influenced by policy developments, election-related psychopathology and popular discourse about individual candidates. Yet, whether this reflects generalised trends or is contingent on election-specific and contextual factors remains difficult to ascertain. Aims: This study sought to examine correlations between Google Trends (GT) data on mental health and four national elections in the US and the UK from 2008 to 2020. This was intended to yield preliminary insights into the relevance of mental health topics amongst voters and the potential impact of electoral cycles on patterns of online engagement with these issues. Methods: Monthly and daily Search Volume Indexes (SVI) were gathered from the ‘Mental Health’ category on GT in the US and UK from 2008 to 2023. SVI were evaluated around the past four national ballots, comparing election-year intervals and baseline data from the preceding year. Statistical tests were conducted to assess SVI and short- and long-term dynamics. Findings: The results showed heterogeneous SVI patterns related to mental health during elections in the US and the UK. In the US, there were statistically significant SVI increases proximal to the majority of elections as compared to data in the same calendar year, but these did not typically exceed baseline SVI. However, interestingly, divisive US contests showed correlations with several elevated SVI measures in the same and previous years. Conversely, there was a lack of consistent longitudinal trends in UK elections, perhaps informed by its disparate sociopolitical landscape. Conclusions: These findings underline the composite relationship between electoral politics and popular engagement with mental health topics around national votes, suggesting that this is driven more by situational factors rather than a recurrent electoral effect or signs of burgeoning engagement. Detailed research is required to understand the nuances and causality behind these dynamics and their wider implications.
Sickle cell trait, APOL1 risk allele status and chronic kidney disease among ART-experienced adults living with HIV in northern Nigeria
Authors: Abdulhamid Abdurrahman , Shepherd Bryan E. , Wudil Usman J. , Van Wyk Chelsea , Dankishiya Faisal S. , Hussaini Nafiu , Wester C. William , Aliyu Muktar H. 
Published: June 2024

Background We sought to determine the prevalence of sickle cell trait (SCT) and apolipoprotein-1 ( APOL1) risk variants in people living with HIV (PLWH) in Nigeria, and to establish if SCT and APOL1 high-risk status correlate with estimated glomerular filtration rate (eGFR) and/or prevalent chronic kidney disease (CKD). Methods Baseline demographic and clinical data were obtained during three cross-sectional visits. CKD was defined as having an eGFR<60 mL/min/1.73 m2. We collected urine specimens to determine urine albumin-creatine ratio and blood samples for sickle cell genotyping, APOL1 testing, and for creatinine/cystatin C assessment. The associations between SCT, APOL1 genotype, and eGFR/CKD stages/CKD were investigated using linear/ordinal logistic/logistic regression models, respectively. Results Of 2443 participants, 599 (24.5%) had SCT, and 2291 (93.8%) had a low-risk APOL1 genotype (0 or 1 risk variant), while 152 (6.2%) had high-risk genotype (2 allele copies). In total, 108 participants (4.4%) were diagnosed with CKD. In adjusted analyses, SCT was associated with lower eGFR (adjusted mean difference [aMD]= −2.33, 95% CI -4.25, −0.42), but not with worse CKD stages, or increased odds of developing CKD. Participants with the APOL1 high risk genotype were more likely to have lower eGFR (aMD= −5.45, 95% CI -8.87, −2.03), to develop CKD (adjusted odds ratio [aOR] = 1.97, 95% CI: 1.03, 3.75), and to be in worse CKD stages (aOR = 1.60, 95% CI: 1.12, 2.29) than those with the low-risk genotype. There was no evidence of interaction between SCT and APOL1 genotype on eGFR or risk of CKD. Conclusion Our findings highlight the multifaceted interplay of genetic factors in the pathogenesis of CKD in PLWH.
Pediatric Uropathogens and their Antimicrobial Susceptibility Pattern: Experience from an Impoverished District of Karachi, Pakistan
Authors: Khan Moiz Ahmed , Shakeel Nosheen 
Published: June 2024

Introduction: Urinary tract infection (UTI) is the most common infection of the pediatric age group. Several factors linked to higher prevalence include poor personal hygiene, improper sanitation, lower socioeconomic status, and malnourishment. In addition to having a worse quality of life, the 1.8 million children who live in Karachi’s Korangi district are routinely exposed to such factors. Objectives: The study aims to evaluate the frequency of UTI and distribution of uropathogens along with their antimicrobial susceptibility pattern in patients presenting to a pediatric tertiary care center in the Korangi district of Karachi, Pakistan. Design: The study employed an observational cross-sectional design. Methods: The study was conducted at the Microbiology laboratory of Sindh Institute of Child Health and Neonatology, Karachi, Pakistan from 1st January to 15th August 2023. Urine samples of patients 1 to 16 years of age were collected via midstream clean catch method and of patients from birth up to 1 year were collected in urine collection bags. The samples were cultured on Cystine Lactose Electrolyte Deficient (CLED) agar and antibiotic susceptibility testing was performed using the Kirby-Bauer Disc Diffusion method. Results: A total of 457 urine samples were collected, of which 90 (19.7%) were positive for significant uropathogens. With a mean age of 4.6 years, majority of the culture-positive patients were female (n = 72; 80%). Enterobacterales were the most frequently isolated (n = 88; 95.6%), of which Escherichia coli was the most common (73.9%; n = 68). Citrobacter (n = 7; 7.6%), Klebsiella (n = 6; 6.5%), Serratia (n = 4; 4.3%), Proteus (n = 2; 2.2%), Salmonella (n = 2; 2.2%), and Enterobacter (n = 1; 1.1%) were among the other Enterobacterales isolated. Meropenem and imipenem were the most effective in isolates from Enterobacterales (n = 88) followed by amikacin (n = 84), ciprofloxacin (n = 75), and piperacillin-tazobactam (n = 70). Ceftriaxone and cefixime exhibited moderate susceptibility (n = 69 and 52) whereas, amoxicillin-clavulanate was the least susceptible (n = 3). Conclusion: We report high frequency of UTI in our pediatric population with uropathogens and associated antimicrobial susceptibility pattern confirming to the existing trends of pediatric UTIs in Pakistan. In addition to valuable insights for treating patients under similar conditions, our study serves as a catalyst for further multi-center research in this area.
Cerebrospinal fluid distribution and pharmacokinetics of ponatinib in Ph1+ acute lymphoblastic leukemia
Authors: Walczak Pierre , Fodil Sofiane , Vignal Nicolas , Cabannes-Hamy Aurélie , Boissel Nicolas , Raffoux Emmanuel , Cayuela Jean-Michel , Goldwirt Lauriane , Lengliné Etienne 
Published: June 2024

Abstract
Tyrosine kinase inhibitors efficacy in central nervous system (CNS) disease remains uncertain. Ponatinib was studied for CNS distribution in 16 patients with Philadelphia-positive acute lymphoblastic leukemia. Cerebrospinal fluid concentrations fell below the 40 nM threshold, suggesting suboptimal CNS exposure.
Cuproptosis in cancer: biological implications and therapeutic opportunities
Authors: Li Liping , Zhou Houfeng , Zhang Chenliang 
Published: June 2024

AbstractCuproptosis, a newly identified copper (Cu)-dependent form of cell death, stands out due to its distinct mechanism that sets it apart from other known cell death pathways. The molecular underpinnings of cuproptosis involve the binding of Cu to lipoylated enzymes in the tricarboxylic acid cycle. This interaction triggers enzyme aggregation and proteotoxic stress, culminating in cell death. The specific mechanism of cuproptosis has yet to be fully elucidated. This newly recognized form of cell death has sparked numerous investigations into its role in tumorigenesis and cancer therapy. In this review, we summarized the current knowledge on Cu metabolism and its link to cancer. Furthermore, we delineated the molecular mechanisms of cuproptosis and summarized the roles of cuproptosis-related genes in cancer. Finally, we offered a comprehensive discussion of the most recent advancements in Cu ionophores and nanoparticle delivery systems that utilize cuproptosis as a cutting-edge strategy for cancer treatment.
Corneal ulceration following periocular scorpion sting: a case report
Authors: Shiravani Mohammad , Nejabat Mahmood , Attar Alireza 
Published: June 2024

Abstract
Background
Scorpion envenomation, a prevalent medical emergency in rural areas, demands immediate attention due to its potential severity. While ocular manifestations are uncommon, they can lead to significant complications such as corneal ulceration. We present a unique case of corneal ulceration subsequent to a yellow scorpion (Hemiscorpius lepturus) sting near the eye, a scenario not previously documented.

Case presentation
A 34-year-old male sought medical care following a scorpion sting despite prior anti-venom treatment. Clinical examination revealed pronounced ocular inflammation, corneal stromal melting, and anterior chamber inflammation, with microbiological confirmation of Pseudomonas spp infection. Treatment comprised fortified ceftazidime and vancomycin eye drops, alongside topical corticosteroids, leading to visual and corneal healing.

Conclusion
This case highlights the urgency of addressing scorpion envenomation and its potential for severe ocular complications, including corneal ulceration. Prompt diagnosis and targeted therapy with antibiotics and corticosteroids are crucial for favorable outcomes. A comprehensive understanding and timely intervention in scorpion sting-induced ocular manifestations are essential for optimal patient management and outcomes in such cases.
The Impact of Blood and Urine Biomarkers on Age-Related Macular Degeneration: Insights from Mendelian Randomization and Cross-sectional Study from NHANES
Authors: Jingzhi Wang , Cui Xuehao 
Published: June 2024

Abstract
Introduction
Age-related macular degeneration (AMD) is a leading cause of blindness, affecting millions worldwide. Its complex pathogenesis involves a variety of risk factors, including lipid metabolism and inflammation. This study aims to elucidate the causal relationships between biomarkers related to these processes and AMD, leveraging Mendelian randomization (MR) and cross-sectional analysis from the National Health and Nutrition Examination Survey (NHANES).

Method
We conducted a two-phase study, initially using MR to explore the causality between 35 biomarkers and various AMD subtypes, followed by observational analysis with NHANES data to validate these findings.

Results
MR analysis identified a protective role of TG and a risk factor role of HDL-C and CRP in AMD development. NHANES data corroborated these findings, highlighting a nuanced relationship between these biomarkers and AMD. Notably, lipid metabolism-related biomarkers showed stronger associations with early AMD, whereas CRP’s significance was pronounced in late AMD.

Conclusion
This comprehensive analysis, combining MR with NHANES data, reinforces the importance of lipid metabolism and inflammation in AMD’s etiology. Future research should further investigate these biomarkers’ mechanisms and their potential as therapeutic targets for AMD prevention and treatment.