bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementation and evaluation of specialist heart failure pharmacist prescribing clinics
Authors: Campbell Gayle , Doherty Ciara , D’Silva Andrew , Carr-White Gerald , Webb Jessica , Ismail Tevfik F 
Published: October 2024

Abstract

Background
Medications form the basis of treatment for heart failure (HF) and adherence is crucial as untreated HF has a mortality of greater than 30%. As such, specialist HF pharmacists with expertise in prescribing and promoting adherence have become an integral part of the wider HF multidisciplinary team (MDT).

Aim
To implement specialist HF pharmacist prescribing clinics and evaluate their impact.

Setting
An integrated HF team at a tertiary London hospital.

Development
The clinic was initially developed to facilitate the introduction of sacubitril-valsartan evolving to 6 dedicated clinics/week.

Implementation
A dedicated electronic referral pathway was created to channel referrals to the specialist clinic, and referral criteria expanded to all patients requiring optimisation of medical therapy.

Evaluation
Data were retrospectively collected for patients with heart failure with reduced ejection fraction seen in the HF pharmacist clinic between September 2021 and July 2022. Overall, 114 patients were seen (mean age 66 years, 78 male). The mean time to medication optimisation was 3 months (averaging 1 appointment/month). The number on optimised doses of guideline-directed medical therapy, increased significantly from 8% at first appointment to 76% on discharge (p < 0.001). The HF pharmacists reviewed all medications and optimised non-HF medications for 17.5% (n = 20) of patients.

Conclusion
HF pharmacists can optimise patients’ HF and non-HF medical therapy typically within 3 months. By reviewing all prescribed medications, HF pharmacists provide a holistic review of all medications. They can play a vital role in addressing the underutilisation of HF medical therapy and thereby improving patient outcomes.
Infants with biliary atresia exhibit an altered amino acid profile in their newborn screening
Authors: Uecker Marie , Prehn Cornelia , Janzen Nils , Adamski Jerzy , Vieten Gertrud , Petersen Claus , Kuebler Joachim F. , Madadi-Sanjani Omid , Klemann Christian 
Published: October 2024

Abstract
Introduction
Biliary atresia (BA) is a rare progressive neonatal cholangiopathy with unknown pathophysiology and time of onset. Newborn Screening (NBS) in Germany is routinely performed in the first days of life to identify rare congenital diseases utilizing dried blood spot (DBS) card analyses. Infants with biliary atresia (BA) are known to have altered amino acid profiles (AAP) at the time point of diagnosis, but it is unclear whether these alterations are present at the time point of NBS.

Objectives
We aimed to analyze amino acid profiles in NBS–DBS of infants with Biliary Atresia.

Methods
Original NBS-DBS cards of 41 infants who were later on diagnosed with BA were retrospectively obtained. NBS–DBS cards from healthy newborns (n = 40) served as controls. In some BA infants (n = 14) a second DBS card was obtained at time of Kasai surgery. AAP in DBS cards were analyzed by targeted metabolomics.

Results
DBS metabolomics in the NBS of at that time point seemingly healthy infants later diagnosed with BA revealed significantly higher levels of Methionine (14.6 ± 8.6 μmol/l), Histidine (23.5 ± 50.3 μmol/l), Threonine (123.9 ± 72.8 μmol/l) and Arginine (14.1 ± 11.8 μmol/l) compared to healthy controls (Met: 8.1 ± 2.6 μmol/l, His: 18.6 ± 10.1 μmol/l, Thr: 98.1 ± 34.3 μmol/l, Arg: 9.3 ± 6.6 μmol/l). Methionine, Arginine and Histidine showed a further increase at time point of Kasai procedure. No correlation between amino acid levels and clinical course was observed.

Conclusion
Our data demonstrate that BA patients exhibit an altered AAP within 72 h after birth, long before the infants become symptomatic. This supports the theory of a prenatal onset of the disease and, thus, the possibility of developing a sensitive and specific NBS. Methionine might be particularly relevant due to its involvement in glutathione metabolism. Further investigation of AAP in BA may help in understanding the underlying pathophysiology.
Asymmetric Laplace scale mixtures for the distribution of cryptocurrency returns
Authors: Punzo Antonio , Bagnato Luca 
Published: October 2024

AbstractRecent studies about cryptocurrency returns show that their distribution can be highly-peaked, skewed, and heavy-tailed, with a large excess kurtosis. To accommodate all these peculiarities, we propose the asymmetric Laplace scale mixture (ALSM) family of distributions. Each member of the family is obtained by dividing the scale parameter of the conditional asymmetric Laplace (AL) distribution by a convenient mixing random variable taking values on all or part of the positive real line and whose distribution depends on a parameter vector $$\varvec{\theta }$$

θ

providing greater flexibility to the resulting ALSM. Advantageously concerning the AL distribution, our family members allow for a wider range of values for skewness and kurtosis. For illustrative purposes, we consider different mixing distributions; they give rise to ALSMs having a closed-form probability density function where the AL distribution is obtained as a special case under a convenient choice of $$\varvec{\theta }$$

θ

. We examine some properties of our ALSMs such as hierarchical and stochastic representations and moments of practical interest. We describe an EM algorithm to obtain maximum likelihood estimates of the parameters for all the considered ALSMs. We fit these models to the returns of two cryptocurrencies, considering several classical distributions for comparison. The analysis shows how our models represent a valid alternative to the considered competitors in terms of AIC, BIC, and likelihood-ratio tests.
Hypoxia-Induced Inflammation in In Vitro Model of Human Blood–Brain Barrier: Modulatory Effects of the Olfactory Ensheathing Cell-Conditioned Medium
Authors: Agafonova Aleksandra , Cosentino Alessia , Musso Nicolò , Prinzi Chiara , Russo Cristina , Pellitteri Rosalia , Anfuso Carmelina Daniela , Lupo Gabriella 
Published: October 2024

AbstractHypoxia compromises the integrity of the blood–brain barrier (BBB) and increases its permeability, thereby inducing inflammation. Olfactory ensheathing cells (OECs) garnered considerable interest due to their neuroregenerative and anti-inflammatory properties. Here, we aimed to investigate the potential modulatory effects of OEC-conditioned medium (OEC-CM) on the response of human brain microvascular endothelial cells (HBMECs), constituting the BBB, when exposed to hypoxia. HBMECs were utilized to establish the in vitro BBB model. OECs were isolated from mouse olfactory bulbs, and OEC-CM was collected after 48 h of culture. The effect of OEC-CM treatment on the HBMEC viability was evaluated under both normoxic and hypoxic conditions at 6 h, 24 h, and 30 h. Western blot and immunostaining techniques were employed to assess NF-κB/phospho-NF-κB expression. HIF-1α, VEGF-A, and cPLA2 mRNA expression levels were quantified using digital PCR. ELISA assays were performed to measure PGE2, VEGF-A, IL-8 secretion, and cPLA2 specific activity. The in vitro formation of HBMEC capillary-like structures was examined using a three-dimensional matrix system. OEC-CM attenuated pro-inflammatory responses and mitigated the HIF-1α/VEGFA signaling pathway activation in HBMECs under hypoxic condition. Hypoxia-induced damage of the BBB can be mitigated by novel therapeutic strategies harnessing OEC potential.
Synchronous LoRa sensor nodes for modal identification in footbridge vibration monitoring
Authors: Qiao Huiyue , Guan Hong , Jabbour Andrei , Zhu Yong 
Published: October 2024

AbstractThis article aims at presenting a preliminary investigation of using long-range and low-cost LoRa (Long Range) technology and two synchronous LoRa sensor nodes for cost-effective footbridge structural health monitoring (SHM). Two sensor nodes with LoRa modules and accelerometers were employed for vibration monitoring and a new attempt was made to use synchronous LoRa nodes for modal identification. In this article, a modal identification method based on a lightweight synchronization concept was proposed. This method is able to identify the fundamental mode of vibrating beam structures. Meanwhile, maximum accelerations can also be tracked periodically from the simultaneously recorded acceleration data by the synchronous LoRa nodes. Specifically, synchronization was achieved through the wireless peer-to-peer (P2P) communication between the two LoRa nodes, with the aim of initializing simultaneous acceleration recordings. The fundamental frequency and the phase information derived from the on-board calculation of the synchronized nodes can then be used to effectively identify the vertical bending mode and torsion mode. A series of laboratory tests were also conducted on a beam structure for the purpose of validation. The test results showed that the fundamental mode of the vibrating beam can be obtained rapidly and accurately using the synchronized LoRa nodes, with an average synchronization accuracy of 4.45 ms. The maximum acceleration data recorded by the LoRa nodes also showed high accuracy when compared with the raw acceleration data collected from a commercial Bluetooth accelerometer node. The fundamental frequencies obtained from both types of nodes also compare reasonably. The proposed modal identification method using two synchronous LoRa sensor nodes provides a basis for the development of a low-cost footbridge SHM system with the integration of IoT techniques. An attempt has been made to perform a preliminary field test on a cable-stayed footbridge, where the fundamental frequency and the mode shape type of the footbridge were successfully identified and its serviceability condition was also found to satisfy the code requirements.
BwMMV-pred: a novel ensemble learning approach using blood smear images for malaria prediction
Authors: Tayyab Muhammad Arabi , Alim Affan , Alam Mansoor , Su’ud Mazliham Mohd 
Published: October 2024

AbstractThe use of machine learning in healthcare has become widespread, enhancing the capabilities of doctors and clinicians. This study introduces a novel ensemble learning approach named Blending with Meta Majority Voting (BwMMV) for malaria prediction using blood smear images. The BwMMV technique combines the strengths of eight base classifiers to form an intermediate dataset, which is subsequently used to train five distinct meta-models using different machine learning algorithms. A Local Binary Pattern Histogram (LBPH) method is employed to extract texture features from blood smear images, effectively capturing the underlying patterns necessary for classification. The final classification decision is determined through a majority voting mechanism, selecting the outcome with the most votes as the final prediction. Our results indicate that the BwMMV approach significantly outperforms traditional hard voting and blending techniques, achieving superior accuracy, robustness, and resilience in performance. This innovative method demonstrates promising potential as a powerful tool for automated diagnosis systems, with the ability to be expanded to analyze various datasets efficiently.
New averaged type algorithms for solving split common fixed point problems for demicontractive mappings
Authors: Berinde Vasile , Saleh Khairul 
Published: October 2024

AbstractIn this paper we propose new averaged iterative algorithms designed for solving a split common fixed point problem in the class of demicontractive mappings. The algorithms are obtained by inserting an averaged term into the algorithms used in [Li, R. and He, Z., A new iterative algorithm for split solution problems of quasi-nonexpansive mappings J. Inequal. Appl.131 (2015), 1–12.] for solving the same problem but in the class of quasi-nonexpansive mappings, which is a subclass of demicontractive mappings. Basically, our investigation is based on the embedding of demicontractive operators in the class of quasi-nonexpansive operators by means of averaged mappings. For the considered algorithms we prove weak and strong convergence theorems in the setting of a real Hilbert space. A numerical example is given to illustrate the results.
Efficient random walks for generating random fuzzy measures in Möbius representation in large universe
Authors: Beliakov Gleb , Baz Juan , Wu Jian-Zhang 
Published: October 2024

AbstractRandom generation of fuzzy measures plays a pivotal role in large-scale decision-making and optimization. Random walks ensure uniform generation and adequate coverage. The Möbius representation of set functions is a valuable tool for establishing the sparse structure of fuzzy measures, with its non-negativity closely linked to monotonicity and convexity/supermodularity checking. We propose three efficient methods for monotonicity verification and convexity/supermodularity verification applicable to random walks in Möbius representations, specifically tailored for universal sets larger than ten inputs and k-order fuzzy measures. We first present the baseline methods by directly inspecting monotonicity and convexity constraints. Building on the observation that the majority of initially generated values exhibit non-negativity, we intentionally track negative Möbius values to enhance the computational performance of these baseline approaches. Further, we introduce the more agile methods that employ insertion and merge sorting techniques for both monotonicity and convexity checks in random walks that involve small perturbations of fuzzy measures. To address sparsity in large-scale scenarios, we focus on two major types of measures: k-additive and k-interactive measures, demonstrating their effectiveness through theoretical analysis and experimental results.
Flexible Graphene Field-Effect Transistors and Their Application in Flexible Biomedical Sensing
Authors: Sun Mingyuan , Wang Shuai , Liang Yanbo , Wang Chao , Zhang Yunhong , Liu Hong , Zhang Yu , Han Lin 
Published: October 2024

AbstractFlexible electronics are transforming our lives by making daily activities more convenient. Central to this innovation are field-effect transistors (FETs), valued for their efficient signal processing, nanoscale fabrication, low-power consumption, fast response times, and versatility. Graphene, known for its exceptional mechanical properties, high electron mobility, and biocompatibility, is an ideal material for FET channels and sensors. The combination of graphene and FETs has given rise to flexible graphene field-effect transistors (FGFETs), driving significant advances in flexible electronics and sparked a strong interest in flexible biomedical sensors. Here, we first provide a brief overview of the basic structure, operating mechanism, and evaluation parameters of FGFETs, and delve into their material selection and patterning techniques. The ability of FGFETs to sense strains and biomolecular charges opens up diverse application possibilities. We specifically analyze the latest strategies for integrating FGFETs into wearable and implantable flexible biomedical sensors, focusing on the key aspects of constructing high-quality flexible biomedical sensors. Finally, we discuss the current challenges and prospects of FGFETs and their applications in biomedical sensors. This review will provide valuable insights and inspiration for ongoing research to improve the quality of FGFETs and broaden their application prospects in flexible biomedical sensing.
Convergence Rates for Identification of Robin Coefficient from Terminal Observations
Authors: Mondal Subhankar 
Published: October 2024

AbstractThis paper deals with the problem of identification of a Robin coefficient (also known as impedance coefficient) in a parabolic PDE from terminal observations of the temperature distributions. The problem is ill-posed in the sense that small perturbation in the observation may lead to a large deviation in the solution. Thus, in order to obtain stable approximations, we employ the Tikhonov-regularization. We propose a weak source condition motivated by the work of Engl and Zou (2000) and obtain a convergence rate of $$O(\delta ^\frac{1}{2})$$

O
(

δ

1
2

)

, where $$\delta $$
δ
is the noise level of the observed data. The obtained rate is better than some of the previous known rates. Moreover, the advantage of the proposed source condition is that the above mentioned convergence rate is obtained without the need for characterizing the range space of modelling operator and its Fréchet derivative, which is in contrast to the general convergence theory of Tikhonov-regularization for non linear operators.
Predictive modeling of sustainable recycled materials for stone column construction
Authors: Foda Tasneem , Hassan Hassan M. , Abdelkader Ahmed , el-Hassan Khaled Abu 
Published: October 2024

AbstractClimate change due to carbon dioxide emissions is one of the most significant global challenges. Sustainable recycled materials are recognized as a critical factor in mitigating climate change, representing a fundamental strategy for reducing gas emissions and preserving the environment. Promising sustainable recycled materials in construction, such as crushed asphalt, crushed concrete, crushed ceramic, and treated concrete offer numerous advantages, including reduced carbon dioxide $${\text{(CO}}_{2} )$$

(CO
2

)

emissions, cost-effectiveness, increased strength, and enhanced mechanical properties. In this study, various machine learning techniques, including the support vector machine (SVM), multiple linear regression (MLR), gaussian process regression (GPR), and artificial neural network (ANN), are used to assess the effectiveness of sustainable recycled materials within stone columns. The MLR model was specifically utilized to predict the ultimate stress equation for these materials within stone columns. Forty-five constructed samples were used in developing the models. The effectiveness of each model was evaluated using various statistical assessment measures, including mean absolute error (MAE), absolute fraction of variation ($${\text{R}}^{2}$$

R

2

), and root mean square error (RMSE). The predictive model’s performance was validated using the k-fold cross-validation method. The ANN model with an $${\text{R}}^{2}$$

R

2

value of 0.962 and an RMSE value of 7.268 kPa, performed better compared to GPR, SVM and MLR models. In summary, the results of the study indicate that the MLR model, utilizing the identified input parameters, can accurately predict the ultimate stress for different sustainable recycled materials. Implementing such technologies within the construction sector can expedite and reduce the cost of assessing material characteristics and the influence of input parameters.
Combating corruption on the frontlines: analyzing penalties for street-level bureaucrats in China
Authors: Wang Rui , Ni Xing 
Published: October 2024

AbstractPenalties for corrupt acts are a vital component of integrity management systems within bureaucratic organizations, yet systematic inquiry into specific interventions is lacking. Drawing from the principal–supervisor–agent model, this article explores the criteria for imposing penalties on corrupt bureaucrats at the street level. We examine the impact of various factors on the severity of penalties for corruption, highlighting the influence of top-down anticorruption reform and local enforcement capacity. Utilizing text mining techniques, we build a comprehensive dataset containing 4025 cases of punishing corruption among grassroots officials in China from 2015 to 2023 and use multilevel analysis to explore these dynamics. Findings suggest that anticorruption reform directly led to an increase in the severity of penalties, but with substantial variation across practices. In particular, the analysis reveals a positive correlation between enforcement capacity and the severity of penalties. Moreover, while the relationship between harm and penalty severity represents a generalizable observation, significant disparities exist in the penalties imposed across various types of corruption. This study contributes to understanding the dynamics of multiple punitive measures and the factors leading to penalties imposed on corrupt street-level bureaucrats.
Ecological indicators of water quality and marshland impact area (MARia) index of Ligawasan Marsh: a critically important wetland in the Southern Mindanao, Philippines
Authors: Tanalgo Krizler Cejuela , Manampan-Rubio Meriam , Alvaro-Ele Renee Jane , Hilario-Husain Bona Abigail , Murray Sedra A. , Delos Reyes Jamaica L. , Pangato Nasrodin M. , Magkidong Noril S. , Angcaco Kayle Lou D. , Catulos Angelie J. , Dimacaling Ace D. , Ruiz Julius O. , Abdulkasan Rallyessa Mohann A. , Murray-Buday Melanie , Lidasan Asraf K. , Dela Cruz Kier Celestial. , Respicio Jeaneth Magelen V. , Abdullah Sumaira S. , Agduma Angelo Rellama 
Published: October 2024

AbstractWetland ecosystems are vital for both biodiversity and communities that depend on them. The Ligawasan Marsh in the southern part of the Philippines is one of the most threatened wetlands in the country. Apart from increasing anthropogenic development, wetlands have faced warfare-driven threats that have hindered research and conservation efforts in the area for many decades. Our study is the first to investigate the patterns of physicochemical parameters, patterns of land use cover, and the relationship between land use change and marshland environmental status. We also developed the Marshland Impact Area (MARia) index to assess the potential impact contributions of the surrounding towns. We found a strong negative correlation between dissolved oxygen (DO) and indicators of pollution such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and heavy metals. Interestingly, we observed elevated levels of phosphate and mercury at all sampling sites in the Ligawasan Marsh. Our landscape-level modelling showed that these elevated levels are associated with expanding croplands and urbanisation surrounding the marsh. We found that the potential impact contribution of cropland and urbanisation from surrounding towns of the Ligawasan Marsh varied significantly, suggesting the importance of implementing local policies to reduce land use change impacts. With the current yet remaims limited knowledge and beyond safe pollution levels in the Ligawasan Marsh, it is crucial to implement collaborative and science-based governance to integrate conservation initiatives with the priorities of global targets, such as the Kunming-Montreal Global Biodiversity Framework, for a comprehensive and sustainable approach to Ligawasan Marsh conservation.
Impact of a remdesivir formulary restriction by antimicrobial stewardship on drug utilization and cost at a safety-net community hospital during the COVID-19 pandemic
Authors: Mena Lora Alfredo Jose , Huber Dylan , Li Ella , Lavani Romeen , Ali Mirza , Takhsh Eden , Burgos Rodrigo 
Published: October 2024

Abstract
Deploying novel COVID-19 therapies proved challenging amid rapidly evolving data, drug shortages, and conflicting guidelines. We established a remdesivir formulary restriction remdesivir to promote its evidence-based use. This intervention led to changes in remdesivir utilization patterns and cost savings. Formulary restrictions can play an important role in pandemic preparedness and response.