bluuebunny/crossref_metadata_2025_split huggingface dataset:
Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus
Authors: Rojo-Sánchez Alejandra , Carmona-Martes Ada , Díaz-Olmos Yirys , Santamaría-Torres Mary , Cala Mónica P. , Orozco-Acosta Erick , Aroca-Martínez Gustavo , Pacheco-Londoño Leonardo , Navarro-Quiroz Elkin , Pacheco-Lugo Lisandro A. 
Published: April 2024

AbstractSystemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.
Identification of type VI secretion system effector-immunity pairs using structural bioinformatics
Authors: Geller Alexander M , Shalom Maor , Zlotkin David , Blum Noam , Levy Asaf 
Published: April 2024

AbstractThe type VI secretion system (T6SS) is an important mediator of microbe–microbe and microbe–host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity proteins that neutralize self-intoxication. Here, we applied novel structural bioinformatic tools to perform systematic discovery and functional annotation of T6Es and their cognate immunity proteins from a dataset of 17,920 T6SS-encoding bacterial genomes. Using structural clustering, we identified 517 putative T6E families, outperforming sequence-based clustering. We developed a logistic regression model to reliably quantify protein–protein interaction of new T6E-immunity pairs, yielding candidate immunity proteins for 231 out of the 517 T6E families. We used sensitive structure-based annotation which yielded functional annotations for 51% of the T6E families, again outperforming sequence-based annotation. Next, we validated four novel T6E-immunity pairs using basic experiments in E. coli. In particular, we showed that the Pfam domain DUF3289 is a homolog of Colicin M and that DUF943 acts as its cognate immunity protein. Furthermore, we discovered a novel T6E that is a structural homolog of SleB, a lytic transglycosylase, and identified a specific glutamate that acts as its putative catalytic residue. Overall, this study applies novel structural bioinformatic tools to T6E-immunity pair discovery, and provides an extensive database of annotated T6E-immunity pairs.
Molecular two-point recognition of fructosyl valine and fructosyl glycyl histidine in water by fluorescent Zn(ii)-terpyridine complexes bearing boronic acids
Authors: Salomón-Flores María K. , Valdes-García Josue , Viviano-Posadas Alejandro O. , Martínez-Otero Diego , Barroso-Flores Joaquín , Bazany-Rodríguez Iván J. , Dorazco-González Alejandro 
Published: April 2024

We report novel cationic Zn-terpyridine complexes bearing a N-quinolinium nucleus covalently linked to strongly acidic phenylboronic acids for the fluorescent recognition of fructosyl valine and fructosyl glycyl histidine in water.
An effective screening of COVID‐19 pneumonia by employing chest X‐ray segmentation and attention‐based ensembled classification
Authors: Sayeed Abu , Khansur Nasif Osman , Srizon Azmain Yakin , Faruk Md. Farukuzzaman , Alyami Salem A. , Azad AKM , Moni Mohammad Ali 
Published: April 2024

AbstractQuick and accurate diagnosis of COVID‐19 is crucial in preventing its transmission. Chest X‐ray (CXR) imaging is often used for diagnosis, however, even experienced radiologists may misinterpret the results, necessitating computer‐aided diagnosis. Deep learning has yielded favourable results previously, but overfitting, excessive variance, and generalization errors may occur due to noise and limited datasets. Ensemble learning can improve predictions by using robust techniques. Therefore, this study, proposes two‐fold strategy that combines advanced and robust algorithms, including DenseNet201, EfficientNetB7, and Xception, to achieve faster and more accurate COVID‐19 detection. Segmented lung images were generated from CXR images using the residual U‐Net model, and two attention‐based ensemble neural networks were used for classification. The COVID‐19 radiography dataset was used to evaluate the proposed approach, which achieved an accuracy of 98.21%, 93.4%, and 89.06% for two, three, and four classes respectively which outperformed previous studies by a significant margin considering COVID, viral pneumonia, and lung opacity simultaneously. Despite the similarity in CXR images of COVID, pneumonia, and lung opacity, the proposed approach achieved 89.06% accuracy, demonstrating its ability to recognize distinguishable features. The developed algorithm is expected to have applications in clinics for diagnosing different diseases using X‐ray images.
Joint waveform design and resource allocation strategy for cognitive radar target situation awareness
Authors: Song Yuxiao , Tian Biao , Wang Rongqing , Xu Shiyou , Chen Zengping 
Published: April 2024

AbstractTraditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value.
AMICO-COSMOS galaxy cluster and group catalogue up to z = 2: Sample properties and X-ray counterparts
Authors: Toni G. , Maturi M. , Finoguenov A. , Moscardini L. , Castignani G. 
Published: April 2024

Aims. We present the results of a new galaxy cluster search in the COSMOS field obtained using the Adaptive Matched Identifier of Clustered Objects (AMICO). Our aim was to produce a new cluster and group catalogue up to z = 2 by performing an innovative application of AMICO with respect to previous successful applications to wide-field surveys in terms of depth (down to r < 26.7), small area covered (∼1.69 deg2 of unmasked effective area), and redshift extent. This sample and the comparative analysis we performed with the X-rays, allowed for the calibration of mass-proxy scaling relations up to z = 2 and down to less than 1013 M⊙, and constitutes the basis for the refinement of the cluster model for future applications of AMICO, such as the analysis of upcoming Euclid data.
Methods. The AMICO algorithm is based on an optimal linear matched filter and detects clusters in photometric galaxy catalogues using galaxy location, photometric redshift and, in the simplest case, one galaxy property. We chose to use a single magnitude as the galaxy property, avoiding the explicit use of galaxy colour for the selection of clusters. We used three different magnitudes by performing three independent runs in the r-, Y-, and H-bands using both COSMOS2020 and COSMOS2015 galaxy catalogues. We created a composite visibility mask and cluster models for the signal to detect, and we estimated the noise directly from the data.
Results. We performed a matching of the catalogues resulting from the three runs and merged them to produce a final catalogue that contains 1269 and 666 candidate clusters and groups with S/N > 3.0 and > 3.5, respectively. A total of 490 candidates are detected in all three runs. Most of the detections unmatched between runs have S/N < 3.5, which can be set as a threshold for selecting a more robust sample. We assigned X-ray properties to our detections by matching the catalogue with a public X-ray selected group sample and by estimating, for unmatched detections, the X-ray properties at the location of AMICO candidates using Chandra+XMM-Newton data. There are in total 622 candidate clusters and groups with an X-ray flux estimate. This large sample of candidates with X-ray properties allowed the calibration of the scaling relations between two AMICO mass-proxies (richness and cluster amplitude) and X-ray mass and the study of their redshift dependence for the selection of the most stable photometric bands.
Retraction Note: Public regulation effectiveness assessment of Russian regional labor markets using hierarchical clustering method
Authors: Gurieva Lira , Dzhioev Aleksandr 
Published: April 2024

It has been commonly decided to retract this article on the grounds that one of the authors, Lira Gurieva, has used material without due knowledge of all parties involved in the results obtained within this research article. The author apologizes for the inadvertent misappropriation of the data.
The Authors, the Publisher and Editor approved to the retraction.
Mit Krafttraining zum Fußballstar – ein systematischer Aufbau für Kinder und
Jugendliche
Authors: Hartmann Patrick 
Published: April 2024

ZusammenfassungTimo ist 12 Jahre alt und begeisterter Fußballer. Kürzlich hat er eine Dokumentation über seinen
Lieblingsverein gesehen und dabei erfahren, dass seine Vorbilder weit mehr tun, als nur Fußball zu
spielen. Es wurde gezeigt, wie die Profis auch abseits des Fußballplatzes trainieren, unter anderem mit
Gewichten im Kraftraum. Und das alles, um die nächste Meisterschaft zu gewinnen. Er war fasziniert.
Neuausrichtung der Therapie für hochfunktionale Jugendliche mit
Zerebralparese
Authors: Minghetti Alice , Keller Martin , Gysin Ramon , Roth Ralf 
Published: April 2024

ZusammenfassungEin innovativer Ansatz für funktionelles Training bei Jugendlichen mit Zerebralparese: wie
wissenschaftliche Erkenntnisse umgesetzt in praktische Anwendungen erstaunliche Fortschritte
ermöglichen.
Sportverletzungen der Hand
Authors: Jourdan Joanna , Meyer Nora 
Published: April 2024

ZusammenfassungAktive Nachbehandlung, Ruhigstellung oder OP: Sportverletzungen können auf verschiedenen Wegen behandelt
werden. Der Artikel stellt Behandlungen typischer Sportverletzungen der Finger vor. Die Autoren zeigen,
wie Tapes helfen, wann eine thermoplastische Schiene das Mittel der Wahl ist und ob Sportler*innen
präventiv tätig werden können.
Clinical, Microbiological Profile, and Treatment Response to Intraventricular Antibiotics in the Management of Postneurosurgical Meningitis: A Single-Center Experience
Authors: Datta Debajyoti , Sekar Arunkumar , Pasha Amir , Patnaik Ashis , Sahu Rabi Narayan , Bansal Sumit 
Published: April 2024

Abstract
Background Postneurosurgical meningitis (PNM) is a serious medical condition with high mortality and morbidity caused by Gram positive organisms like Staphylococcus aureus and Gram-negative organisms like Acinetobacter baumannii. Optimum concentration of antibiotics in the cerebrospinal fluid (CSF) to treat these infections is difficult to achieve. Intraventricular antibiotic administration bypasses the blood–brain barrier and can achieve high CSF concentration without causing systemic toxicity.
Methods Retrospective review of all patient records were done to identify patients who developed postneurosurgical meningitis and received intraventricular antibiotic therapy during the period of July 2017 to December 2022. Demographic and clinical data along with the type of antibiotic, route, dose, and duration of administration were collected. CSF parameters before and after intraventricular antibiotic administration were collected and analyzed.
Results Twenty-six patients with postneurosurgical meningitis received intraventricular antibiotic therapy. Intracranial tumors were the most common underlying pathology followed by aneurysms. In all, 17/26 patients had received vancomycin and 9/26 patients had received colistin. External ventricular drain was used in 17/26 cases and Ommaya reservoir was used in 9/26 cases. Six patients showed growth of organism in CSF before starting intraventricular antibiotics, while one patient remained culture positive despite treatment. Of the 26 patients, 3 died despite treatment. There were significant changes in the CSF parameters after intraventricular antibiotic therapy.
Conclusion Intraventricular administration of antibiotic provides an alternative therapeutic option in the management of patients who are not responding or poorly responding to systemic antibiotics.
Don’t call it a comeback! Revictimization and the cycle of violence at micro-places
Authors: Schnell Cory 
Published: April 2024

AbstractThis study presents a new perspective on the influence of time and the reoccurrence of crime problems at micro-places. I examined 342,690 aggravated assault incidents reported to the Chicago Police Department from 2001 to 2020 using a longitudinal repeat and near-repeat research design combined with cumulative incidence graphs across different temporal windows. There are two distinct periods to observe the revictimization of violence at micro-places. There is immediate risk after an incident within a week followed by a longer period with lower risk across 2 to 4 years when crime often routinely circles back to the same locations. Future research should continue to refine understanding of cyclical patterns or the “life course” of crime at micro-places to enhance the efficacy of place-based crime preventions strategies.
Exploring the optimal design space of transparent perovskite solar cells for four-terminal tandem applications through Pareto front optimization
Authors: Tan Hu Quee , Zhao Xinhai , Ambardekar Akhil , Birgersson Erik , Xue Hansong 
Published: April 2024

Machine learning algorithms can enhance the design and experimental processing of solar cells, resulting in increased conversion efficiency. In this study, we introduce a novel machine learning-based methodology for optimizing the Pareto front of four-terminal (4T) perovskite-copper indium selenide (CIS) tandem solar cells (TSCs). By training a neural network using the Bayesian regularization-backpropagation algorithm via Hammersley sampling, we achieve high prediction accuracy when testing with unseen data through random sampling. This surrogate model not only reduces computational costs but also potentially enhances device performance, increasing from 29.4% to 30.4% while simultaneously reducing material costs for fabrication by 50%. Comparing experimentally fabricated cells with the predicted optimal cells, the latter show a thinner front contact electrode, charge-carrier transport layer, and back contact electrode. Highly efficient perovskite cells identified from the Pareto front have a perovskite layer thickness ranging from 420 to 580 nm. Further analysis reveals the front contact electrode needs to be thin, while the back contact electrode can have a thickness ranging from 100 to 145 nm and still achieve high efficiency. The charge-carrier transport layers play a crucial role in minimizing interface recombination and ensuring unidirectional current flow. The optimal design space suggests thinner electron and hole transport layer thicknesses of 7 nm, down from 23 to 10 nm, respectively. It indicates a balanced charge-carrier extraction is crucial for an optimized perovskite cell. Overall, the presented methodology and optimized design parameters have the potential to enhance the performance of 4T perovskite/CIS TSC while reducing material fabrication costs.
Dynamical properties of solid and hydrated collagen: Insight from nuclear magnetic resonance relaxometry
Authors: Masiewicz Elzbieta , Ullah Farman , Mieloch Adrianna , Godlewski Janusz , Kruk Danuta 
Published: April 2024

1H spin-lattice Nuclear Magnetic Resonance relaxometry experiments have been performed for collagen and collagen-based artificial tissues in the frequency range of 10 kHz–20 MHz. The studies were performed for non-hydrated and hydrated materials. The relaxation data have been interpreted as including relaxation contributions originating from 1H–1H and 1H–14N dipole–dipole interactions, the latter leading to Quadrupole Relaxation Enhancement effects. The 1H–1H relaxation contributions have been decomposed into terms associated with dynamical processes on different time scales. A comparison of the parameters for the non-hydrated and hydrated systems has shown that hydration leads to a decrease in the dipolar relaxation constants without significantly affecting the dynamical processes. In the next step, the relaxation data for the hydrated systems were interpreted in terms of a model assuming two-dimensional translational diffusion of water molecules in the vicinity of the macromolecular surfaces and a sub-diffusive motion leading to a power law of the frequency dependencies of the relaxation rates. It was found that the water diffusion process is slowed down by at least two orders of magnitude compared to bulk water diffusion. The frequency dependencies of the relaxation rates in hydrated tissues and hydrated collagen are characterized by different power laws (ωH−β, where ωH denotes the 1H resonance frequency): the first of about 0.4 and the second close to unity.
Collapse and expansion kinetics of a single polyelectrolyte chain with hydrodynamic interactions
Authors: Yuan Jiaxing , Curk Tine 
Published: April 2024

We investigate the collapse and expansion dynamics of a linear polyelectrolyte (PE) with hydrodynamic interactions. Using dissipative particle dynamics with a bead–spring PE model, long-range electrostatics, and explicit ions, we examine how the timescales of collapse tcol and expansion texp depend on the chain length N and obtain scaling relationships tcol ∼ Nα and texp ∼ Nβ. For neutral polymers, we derive values of α = 0.94 ± 0.01 and β = 1.97 ± 0.10. Interestingly, the introduction of electrostatic interaction markedly shifts α to α ≈ 1.4 ± 0.1 for salt concentrations within c = 10−4 to 10−2 M. A reduction in the ion-to-monomer size ratio noticeably reduces α. On the other hand, the expansion scaling remains approximately constant, β ≈ 2, regardless of the salt concentration or ion size considered. We find β &gt; α for all conditions considered, implying that expansion is always slower than collapse in the limit of long polymers. This asymmetry is explained by distinct kinetic pathways of collapse and expansion processes.
Experimental and DFT studies of structural and optoelectronic properties of CdS, Zn doped CdS, and (Zn-Ni) co-doping CdS nanomaterials
Authors: Gandouzi Mohamed , Alshammary Hind , Khan Z R , Alshammari Abdullah S , Hedhili F 
Published: April 2024

Abstract
This paper presents experimental and theoretical studies of binary semiconductor CdS, Zn:CdS, and (Zn-Ni) co-doped CdS. Thin films of pure CdS, Cd35ZnS36, and Cd34ZnNiS36 alloys grown by sol–gel spin coating were analyzed using x-ray diffraction, EDX, and UV–vis spectroscopy. The experimental results show the success of growing nanomaterials in hexagonal structures with crystallite sizes ranging from 1.6 to 2.11 nm and possessing band gaps in the region 2.30–2.49 eV. Additionally, we investigate the structural and optoelectronic properties of these materials in the ground state using the density functional theory implemented in the WIEN2k software. The first principles calculations confirmed that the structural and optical properties of CdS align with the experimental results. For nanostructure Cd35ZnS36, the lattice parameters decrease, and the band gap increases to 2.85 eV with Zn doping. The (Zn-Ni) co-doped CdS structure optimization shows that the ferromagnetic configuration is more stable than the non-magnetic structure. The spin-polarized band structure investigations reveal that the majority spin-up channel is about 2.79 eV while the minority spin-down channel is around 2.19 eV. These results increase the importance of Zn:CdS and CdZnNiS alloys for optoelectronic and spintronic applications. The calculated optical properties of CdS, Zn:CdS, and (Zn-Ni) co-doped CdS show slight changes in refractive index and extinction coefficient with the doping and a quantitative agreement with the experimental findings.
Thermal management system for stable EV battery operation with composite phase change materials
Authors: Chen Xuan , Yang HaoKun , Song Yuxin , Lai Waiwah , Zheng Lili 
Published: April 2024

Abstract
Li-ion battery has been one of the cornerstone of the mobile era especially given the burst sales of Electrical vehicles (EV) which have eclipsed that of internal combustion engine vehicles over last decade. While extensive advances in smart production and technologies make it booming to the development of EV, there have been frequently reported fatal fire incidents originated from excessive thermal hazards of Li-ion batteries. In this paper, we developed a new Li-ion battery thermal management system, by making use of porous Aluminum structure as skeleton and Phase Change Materials (PCMs) as filler, to maintain its optimal operating temperature below 35 °C within a small tolerance range of ± 1 °C fluctuation by continuous heat absorption and heat release during the phase changes of the PCM. The thermal stress and energy consumption in the internal battery pack caused by temperature fluctuations can thus be reduced, ultimately sustaining the overall functional life span of the battery. We envision that our work will advance the operation of EV battery and incubate a versatile framework ranging from thermal management to battery assembly.