bluuebunny/crossref_metadata_2025_split huggingface dataset:
Closed‐Loop Control of Macrophage Engineering Enabled by Focused‐Ultrasound Responsive Mechanoluminescence Nanoplatform for Precise Cancer Immunotherapy
Authors: Xu Sixin , Meng Lingkai , Hu Qian , Li Fang , Zhang Jieying , Kong Na , Xing Zhenyu , Hong Guosong , Zhu Xingjun 
Published: August 2024

AbstractMacrophage engineering has emerged as a promising approach for modulating the anti‐tumor immune response in cancer therapy. However, the spatiotemporal control and real‐time feedback of macrophage regulatory process is still challenging, leading to off‐targeting effect and delayed efficacy monitoring therefore raising risk of immune overactivation and serious side effects. Herein, a focused ultrasound responsive immunomodulator‐loaded optical nanoplatform (FUSION) is designed to achieve spatiotemporal control and status reporting of macrophage engineering in vivo. Under the stimulation of focused ultrasound (FUS), the immune agonist encapsulated in FUSION can be released to induce selective macrophage M1 phenotype differentiation at tumor site and the near‐infrared mechanoluminescence of FUSION is generated simultaneously to indicate the initiation of immune activation. Meanwhile, the persistent luminescence of FUSION is enhanced due to hydroxyl radical generation in the pro‐inflammatory M1 macrophages, which can report the effectiveness of macrophage regulation. Then, macrophages labeled with FUSION as a living immunotherapeutic agent (FUSION‐M) are utilized for tumor targeting and focused ultrasound activated, immune cell‐based cancer therapy. By combining the on‐demand activation and feedback to form a closed loop, the nanoplatform in this work holds promise in advancing the controllability of macrophage engineering and cancer immunotherapy for precision medicine.
Dehydration Achieving the Iron Spin State Regulation of Prussian Blue for Boosted Sodium‐Ion Storage Performance
Authors: Meng Tianli , Chen Zifang , Lai Xiaoxu , Xing Jiayi , Chen Chi , Sun Dan 
Published: August 2024

AbstractPrussian blue analogs (PBAs) show promise as cathodes for sodium‐ion batteries due to their notable cycle stability, cost‐effectiveness, and eco‐friendly nature, yet the presence of interstitial water limits the specific capacity and obstructs Na+ mobility within the material. Although considerable experimental efforts are focused on dehydrating water for capacity enhancement, there is still a deficiency of a comprehensive understanding of the low capacity of low‐spin Fe resulting from interstitial water, which holds significance in Na+ storage. This study introduces a novel gas‐assisted heat treatment method to efficiently remove interstitial water from Fe‐based PBA (NaFeHCF) electrodes and combines experiments and theoretical calculations to reveal the iron spin state regulation that is related to the capacity enhancement mechanism. This dehydration strategy significantly enhances battery capacity, especially the portion at higher voltages (3.4–4.0 V). The increase in capacity is attributed to the following factors: an enhanced proportion of Fe2+, reduced water content which facilitates faster charge transfer, and the activation of low spin Fe2+. The optimized NaFeHCF demonstrated impressive half‐cell performance of retaining 87.3% capacity after 2000 cycles at a 5 C rate and achieving 100 mAh g−1 capacity over 200 cycles when being paired with hard carbon, exhibiting its practical potential.
Integrating Ozone Pollutant Elimination in N2 Electrolysis to Produce Nitrate with Reduced Reaction Steps
Authors: Yu Yaodong , Liu Jiao , Sun Mingzi , Han Jiani , Chi Jingqi , Huang Bolong , Lai Jianping , Wang Lei 
Published: August 2024

AbstractThe synthesis of nitrate by the electrochemical N2 oxidation reaction (NOR) is currently one of the most promising routes. However, the traditional generation of nitrate depends on the oxidation reaction between N2 and H2O (or ·OH), which involves complex reaction steps and intermediates, showing strong competition from oxygen evolution reaction (OER). Here, an effective NOR method is proposed to directly oxidize N2 by using O3 as a reactive oxygen source to reduce the reaction step. Electrochemical tests demonstrate that the nitrate yield of Pd‐Mn3O4/CNT electrocatalyst reaches the milligram level, which is the highest yield reported so far for electrocatalytic NOR. Quantitative characterization is employed to establish a comprehensive set of benchmarks to confirm the intrinsic nature of nitrogen activation and test the O3‐mediated reaction mechanism. Density functional theory (DFT) calculations show that the heterostructure Pd‐Mn3O4 leads to a strong adsorption preference for N2 and O3, which greatly reduces the activation energy barrier for N2. This accelerates the synthesis of nitrate based on the direct formation mechanism, which reduces energy barriers and the reaction steps, thus increasing the performance of electrocatalytic nitrate production. The techno‐economic analysis underscores the promising feasibility and sustainable economic value of the presented method.
A non‐stationary factor copula model for non‐Gaussian spatial data
Authors: Mondal Sagnik , Krupskii Pavel , Genton Marc G. 
Published: August 2024

AbstractWe introduce a new copula model for non‐stationary replicated spatial data. It is based on the assumption that a common factor exists that controls the joint dependence of all the observations from the spatial process. As a result, our proposal can model tail dependence and tail asymmetry, unlike the Gaussian copula model. Moreover, we show that the new model can cover a full range of dependence between tail quadrant independence and tail dependence. Although the log‐likelihood of the model can be obtained in a simple form, we discuss its numerical computational issues and ways to approximate it for drawing inference. Using the estimated copula model, the spatial process can be interpolated at locations where it is not observed. We apply the proposed model to temperature data over the western part of Switzerland, and we compare its performance with that of its stationary version and with the Gaussian copula model.
Metastatic renal carcinoma in a horse presented for lameness examination and suspected pneumonia
Authors: Hollenbach Elza , Mitchell Emily P. , Randlehoff Corme , Munoz Juan Alberto 
Published: August 2024

AbstractEquine renal cell carcinoma (RCC) is a rare and poorly understood condition, making the diagnosis challenging. We present a case of a 16‐year‐old Warmblood gelding referred for lameness and suspected pneumonia. The horse exhibited diverse non‐specific symptoms, including pyrexia, coughing, weight loss and lameness. Diagnostic imaging revealed an osseous neoplastic lesion and lung metastasis. Postmortem confirmed metastatic renal carcinoma. This case underscores the difficulty of RCC diagnosis due to non‐specific clinical signs, necessitating a comprehensive approach involving clinical, imaging and histopathological findings. RCC offers limited treatment options as it is typically diagnosed at advanced stages, leading to a poor prognosis and euthanasia. Increased awareness and research are crucial for early detection and better understanding of this rare equine disease.
Bilateral idiopathic oculomotor neuropathy in a cat
Authors: Soares Marques de Almeida Francisca , Kumaratunga Vim , Marsh Oliver 
Published: August 2024

AbstractIdiopathic oculomotor neuropathy is a well‐recognised syndrome in people, and it has also been described in dogs. In dogs, the disease appears to have a good prognosis; however, only reports of a unilateral presentation exist. Evidence of oculomotor dysfunction in cats has been previously associated with intracranial neoplasia, with a poor prognosis. This case report describes the clinical and imaging findings and outcome of a 12‐year‐old, male, neutered, domestic shorthair cat with bilateral mydriasis, unresponsive to light, as the sole clinical sign. Thorough diagnostic investigations led to a diagnosis of bilateral idiopathic oculomotor neuropathy. No treatment was prescribed, and there was no change in the clinical signs with 9‐month follow‐up. To the authors’ knowledge, this is the first report of bilateral idiopathic oculomotor neuropathy in a cat, which, based on this case, suggests a good prognosis with minimal adverse impact on the patient.
Marked clinical improvement in a cat with a refractory non‐surgical case of dermatophytic pseudomycetoma responding to combination antifungal therapy
Authors: Putra Andhika Hardani , Plowgian Curtis 
Published: August 2024

AbstractFeline dermatophytosis is a common dermatological manifestation that carries a good prognosis once a proper diagnosis has been achieved. In rare cases, dermatophytosis can manifest as a nodular dermatosis. This condition, known as dermatophytic pseudomycetoma, can be challenging to diagnose and treat. Surgical excision and systemic antifungal therapy are the standard treatments, which generally will carry a good prognosis for the patient. This case report describes a non‐surgical dermatophytic pseudomycetoma, which failed treatment with a singular antifungal agent, but improved when multiple antifungal medications were combined. This case highlights challenges associated with treating a cat with severe dermatophytic pseudomycetoma and concurrent feline leukaemia virus.
Acute liver failure following fatal zinc phosphide poisoning in a dog
Authors: Wardsworth Isaiah , Prittie Jennifer , Mastrocco Alicia 
Published: August 2024

AbstractZinc phosphide is a highly toxic rodenticide that leads to organ dysfunction by disruption of oxidative phosphorylation via inhibition of the final enzyme of the mitochondrial transport chain (cytochrome C oxidase). A 5‐year‐old, female, spayed Shiba Inu presented in status epilepticus secondary to ingestion of this poison. The dog developed rapid progressive neurological decline, severe cardiotoxicity and acute liver failure, the latter manifested by hepatic encephalopathy and clinicopathological abnormalities, including coagulopathy, progressive liver enzyme elevations and hyperbilirubinemia, hypoglycaemia, hypoalbuminaemia, hypocholesterolaemia and low blood urea nitrogen. The dog ultimately died after 22 hours of hospitalisation. While this toxicosis is reported in several other species, this is the first case report documenting clinical acute liver failure secondary to zinc phosphide poisoning in a dog. Previous canine reports of zinc phosphide exposure suggest a generally favourable prognosis, whereas this case of toxicity was rapidly fatal, suggesting that reassessment of prognosis secondary to this toxicity is prudent.
Transformer technology in molecular science
Authors: Jiang Jian , Ke Lu , Chen Long , Dou Bozheng , Zhu Yueying , Liu Jie , Zhang Bengong , Zhou Tianshou , Wei Guo‐Wei 
Published: August 2024

AbstractA transformer is the foundational architecture behind large language models designed to handle sequential data by using mechanisms of self‐attention to weigh the importance of different elements, enabling efficient processing and understanding of complex patterns. Recently, transformer‐based models have become some of the most popular and powerful deep learning (DL) algorithms in molecular science, owing to their distinctive architectural characteristics and proficiency in handling intricate data. These models leverage the capacity of transformer architectures to capture complex hierarchical dependencies within sequential data. As the applications of transformers in molecular science are very widespread, in this review, we only focus on the technical aspects of transformer technology in molecule domain. Specifically, we will provide an in‐depth investigation into the algorithms of transformer‐based machine learning techniques in molecular science. The models under consideration include generative pre‐trained transformer (GPT), bidirectional and auto‐regressive transformers (BART), bidirectional encoder representations from transformers (BERT), graph transformer, transformer‐XL, text‐to‐text transfer transformer, vision transformers (ViT), detection transformer (DETR), conformer, contrastive language‐image pre‐training (CLIP), sparse transformers, and mobile and efficient transformers. By examining the inner workings of these models, we aim to elucidate how their architectural innovations contribute to their effectiveness in processing complex molecular data. We will also discuss promising trends in transformer models within the context of molecular science, emphasizing their technical capabilities and potential for interdisciplinary research. This review seeks to provide a comprehensive understanding of the transformer‐based machine learning techniques that are driving advancements in molecular science.This article is categorized under:Data Science > ChemoinformaticsData Science > Artificial Intelligence/Machine Learning
Ecotoxicological assessment, in freshwater environment, of wastewater sludge coupled and uncoupled with micro‐polyvinyl chloride on algae and water fleas
Authors: Pignattelli Sara , Provenza Francesca , Rampih Darian , Crisci Alfonso , Renzi Monia 
Published: August 2024

AbstractIn the frame of bioeconomy and circular economy, wastewater sludge (WS) could be a good candidate for its use in agriculture as fertilizer, due to its high content of organic matter, N and P, but on the other hand, it is full of toxicants such as heavy metal, microplastics, detergent, antibiotics, and so on that can reach groundwater and water bodies in leachate form. In this study, we have investigated different sludge concentrations in the eluate form, combined and not with PVC on two different freshwater organisms Selenastrum capricornutum and Daphnia magna, using ecotoxicity tests. At the endpoint, we have evaluated inhibition growth rate, oxidative stress, and pigments production for S. capricornutum, while in case of D. magna, we have assessed organism immobilization and development. From our results, it emerged that at the higher WS concentration, there was not inhibition growth rate, while at oxidative stress, it was higher in algae treated with WS and PVC. Higher Chl‐a production was shown for algae treated with 0.3 g/L of sludge coupled with PVC, where higher phaeopigments production were recorded for algae treated with 0.3 g/L of WS. D. magna has shown an opposite trend when compared with algae, where at the highest WS concentrations supplied was corresponding to an increased mortality explaned as the highest immobility percentage.Practitioner Points
Wastewater sludge is used in agriculture as fertilizer.
PVC microplastic presence and associate ecotoxicity was tested.
PVC presence increased oxidative stress in S. capricornutum.
D. magna was significantly affected by sludge concentrations supplied.
Highly accelerated life testing (HALT): A review from a statistical perspective
Authors: Collins David H. , Huzurbazar Aparna V. , Warr Richard L. 
Published: August 2024

AbstractDespite its use in one form or another for at least four decades, HALT and related techniques [e.g., highly accelerated‐stress screening (HASS) and stress audits (HASA)] are not well understood within the statistical community and remain controversial. This largely reflects a conflict in motivation between engineers, testing under harsh conditions to discover and eliminate failure modes, and statisticians, taking a more cautious approach to develop quantitative estimates of parameters such as mean time between failures (MTBF). This review article will clarify HALT concepts and methods and explain where it fits within the universe of methods that involve the application of accelerating factors to compress the time required to evaluate or enhance product reliability. A major distinction is between methods such as HALT, a high‐stress test‐analyze‐fix‐test iterative process directed at improving reliability by discovering and fixing weak points in a design, and quantitative accelerated life testing (QALT), whose goal is the estimation of product life for a fixed design. We discuss methods such as physics of failure that offer some hope of bridging the gap between the qualitative nature of HALT, and purely quantitative statistical methods. We present a variety of engineering applications of HALT including metal fatigue, piping and pressure vessels, structural damage, radiation damage, and rotating machinery. We also discuss potential synergies between HALT and QALT, such as rapid identification, through HALT, of failure modes requiring quantitative analysis. For further study, extensive references to the applicable literature are provided as well as an appendix that describes related methods.This article is categorized under:
Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control
Inertial drag combined with non‐uniform heat generation/absorption effects on the hydromagnetic flow of polar nanofluid over an elongating permeable surface due to the impose of chemical reaction
Authors: Panda Subhajit , Pattnaik Pradyumna Kumar , Baithalu Rupa , Mishra Satya Ranjan 
Published: August 2024

AbstractThe pivotal role of Brownian and thermophoresis in investigating the flow characteristic of polar nanofluid is important nowadays due to various engineering applications. The enhanced thermal and transport properties in the field of biomedical nanomedicine for hyperthermia treatments, and for enhancing the efficiency of heat exchange processes in cooling electronic devices, heat exchangers, and automotive engines the use of Brownian and thermophoresis is important. Therefore, the present study reveals the importance of Darcy–Forchheimer inertial drag combined with the space‐ and temperature‐dependent heat generation/absorption on the flow of polar nanofluid over an elongating surface. The surface is considered to be permeable for which the hydromagnetic flow in the presence of thermal radiation specifically, Brownian and thermophoresis affects the flow phenomena significantly. The proposed flow model designed with the aforementioned physical properties is standardized into the set of nonlinear ordinary equations by the implementation of suitable similarity rules. Further, the characteristics of various physical quantities are deployed by solving the system by using traditional Rung–Kutta fourth‐order technique. Further, the analysis of several factors is deployed briefly via graphically, and simulation of rate coefficients is presented through tables. The important outcomes of the study are deployed as the inclusion of thermal and solutal buoyancy enhances the velocity distribution, whereas reverse impact is observed for the increasing inertial drag. Also, space‐ and temperature‐dependent heat source augments the temperature profile but Lewis number decelerates the fluid concentration significantly.
Information Extraction from Time Series in the EDM Drilling Process
Authors: Jażdżewski Tomasz , Regulski Krzysztof , Bułka Adam , Malara Pawel , Czeszkiewicz Adrian , Trajer Marcin 
Published: August 2024

AbstractElectrical discharge machining (EDM) allows to obtain small holes with the high efficiency and high quality. Such features are most common in jet engine turbine airfoils. The main problem of the analysis is detection of a moment when the machine should stop the drilling process—the breakthrough detection. Machine learning applications requires that data and models to be prepared by specialists that can extract the most important information from an input data and choose most suitable Artificial Intelligence (AI) algorithm for particular case. This Article describes an experiment on how to extract valuable information from heterogeneous time series data with various sources (which is popular in an industry 4.0—Internet of Things) from the EDM drilling process. EDM process is conducted by a Computerized Numerical Control (CNC) drilling device that measures the position of the electrode, drilling speed, Additional sensor, monitors impulses current and voltage. The research is focused on classic AI algorithms (decision tree, random forest and eXtreme Gradient Boosting (XGBoost)) which provide fast training and possibility to check more hyperparameters in a time than neural networks algorithms. It is also described how feature extraction can high up AI algorithm predictions.
Material Parameters Identification of Thin Fiber-Based Materials Using the Method of Machine Learning Exploiting Numerically Generated Simulation Data
Authors: Sanjon Cedric Wilfried , Leng Yuchen , Hauptmann Marek , Majschak Jens-Peter , Groche Peter 
Published: August 2024

AbstractThe determination and validation of material parameters required for finite element simulation of the forming processes of fiber-based materials such as paperboard can be accomplished by strain-based loading of a specimen in combination with a simulation-based reverse engineering approach. Due to the complexity of the material itself, such as anisotropy, the development of such approaches can be very time-consuming and requires programming skills as well as expertise in FEM analysis and optimization. Machine learning methods offer a practical alternative to optimization, parameterization, and reverse engineering approaches, assuming that the data is fully known, generalized, and learned by the machine learning model. More specifically, a machine learning model can compute the material parameters required for a finite element simulation directly from the experimental measurements, if the hypothetical mapping function in this case is learned from the numerical study between material parameters and deformation behavior. In this paper, such data generated by numerical studies are used to train the machine learning model and, based on this, to determine elastic (e.g., Young’s modulus), plastic, and Hill’s parameters.
Determining the spin of light primordial black holes with Hawking radiation. Part II. High spin regime
Authors: Calzà Marco , Rosa João G. 
Published: August 2024

Abstract
We propose a method to determine the mass and spin of primordial black holes based on measuring the energy and emission rate at the dipolar and quadrupolar peaks in the primary photon Hawking spectrum, applicable for dimensionless spin parameters $$ \overset{\sim }{a} $$

a
~

≳ 0.6. In particular, we show that the ratio between the energies of the two peaks is only a function of the black hole spin, while the ratio between their emission rates depends also on the line-of-sight inclination. The black hole mass and distance from the Earth may then be inferred from the absolute values of the peak energies and emission rates. This method is relevant for primordial black holes born with large spin parameters that are presently still in the early stages of their evaporation process. While Hawking radiation from such black holes is somewhat below the reach of planned gamma-ray telescopes, we expect the required sensitivity to be attained in the not too distant future.
GNS Construction for $$C^*$$-Valued Positive Sesquilinear Maps on a quasi *-algebra
Authors: Bellomonte Giorgia , Ivković Stefan , Trapani Camillo 
Published: August 2024

AbstractThe GNS construction for positive invariant sesquilinear forms on quasi *-algebra $$(\mathfrak A,{\mathfrak A}_{\scriptscriptstyle 0})$$

(
A
,

A

0

)

is generalized to a class of positive sesquilinear maps from $$\mathfrak A\times \mathfrak A$$

A
×
A

into a $$C^*$$

C
∗

-algebra $${\mathfrak {C}}$$
C
. The result is a *-representation taking values in a space of operators acting on a certain quasi-normed $${\mathfrak {C}}$$
C
-module.