bluuebunny/crossref_metadata_2025_split huggingface dataset:
Consequences of post‐weaning sleep deprivation on behaviour and oxidative stress parameters in rat plasma and brain
Authors: Wearick‐Silva Luis Eduardo , Nunes Magda Lahorgue , Luft Carolina , Camargo Nathalia F. , Fernandes Nathalia F. , Taurisano Melissa R. G. , de Oliveira Jarbas R. 
Published: January 2023

AbstractSleep is essential for health: Adequate sleep is essential for healthy development and sleep deprivation results in several consequences. Indeed, sleep deprivation early in life is associated with poor behaviour and cognition, as well as impaired mental and physical health. Preclinical studies have shown that sleep deprivation alters several physiological functions later in life such as the cardiovascular, immune and endocrine systems, resulting in altered oxidative states. Most of the preclinical literature is focused on adult animals, and little is known about oxidative alterations during development, especially in the context of sleep deprivation. Hence, we adapted a classic and well‐documented model of sleep deprivation, paradoxical sleep deprivation using multiple platforms, for juvenile rats and explored central and peripheral oxidative parameters, as well as the behavioural consequences of sleep deprivation post‐weaning. We showed that 96 h of paradoxical sleep deprivation induced a significant reduction in body weight, decreased sucrose preference—a behaviour suggestive of anhedonia—and increased glucose and decreased cholesterol in the plasma. In the brain, we observed a decrease in reduced glutathione levels in the medial prefrontal cortex and an increase in thiobarbituric acid reactive substance levels in the hypothalamus, indicating oxidative damage in these regions. Taken together, our findings suggest that paradoxical sleep deprivation during development induces anhedonic behaviour and promotes central and peripheral alterations in oxidative parameters.
Quasi‐continuous infrared matrix‐assisted laser desorption electrospray ionization source coupled to a quadrupole time‐of‐flight mass spectrometer for direct analysis from well plates
Authors: Arciniega Cristina , Garrard Kenneth P. , Guymon Jacob P. , Manni Jeffrey G. , Apffel Alex , Fjeldsted John C. , Muddiman David C. 
Published: January 2023

AbstractHigh‐throughput screening (HTS) is a technique mostly used by pharmaceutical companies to rapidly screen multiple libraries of compounds to find drug hits with biological or pharmaceutical activity. Mass spectrometry (MS) has become a popular option for HTS given that it can simultaneously resolve hundreds to thousands of compounds without additional chemical derivatization. For this application, it is convenient to do direct analysis from well plates. Herein, we present the development of an infrared matrix‐assisted laser desorption electrospray ionization (IR‐MALDESI) source coupled directly to an Agilent 6545 for direct analysis from well plates. The source is coupled to a quadrupole time‐of‐flight (Q‐TOF) mass spectrometer to take advantage of the high acquisition rates without sacrificing resolving power as required with Orbitrap or Fourier‐transform ion cyclotron resonance (FTICR) instruments. The laser used for this source operates at 100 Hz, firing 1 pulse‐per‐burst, and delivers around 0.7 mJ per pulse. Continuously firing this laser for an extended duration makes it a quasi‐continuous ionization source. Additionally, a metal capillary was constructed to extend the inlet of the mass spectrometer, increase desolvation of electrospray charged droplets, improve ion transmission, and increase sensitivity. Its efficiency was compared with the conventional dielectric glass capillary by measured signal and demonstrated that the metal capillary increased ionization efficiency due to its more uniformly distributed temperature gradient. Finally, we present the functionality of the source by analyzing tune mix directly from well plates. This source is a proof of concept for HTS applications using IR‐MALDESI coupled to a different MS platform.
How does SARS‐CoV‐2 infection impact on immunity, procession and treatment of pan cancers
Authors: Xiong Nan , Sun Qiangming 
Published: January 2023

AbstractWe identified 14 immune‐related differentially Expressed Genes (DEGs) between COVID‐19 patients and normal controls and the receiver operator characteristic curve results showed that they could be used to discriminate COVID‐19 patients from healthy controls. Single‐sample gene set enrichment analysis and CIBERSORT analysis displayed immune landscape of COVID‐19 patients that the fraction of immune cells (like B cell subtypes and T cell subtypes) decreased distinctly in the first SARS‐CoV‐2 infection which may further weaken immunity of cancer patients and increasing inflammatory cells (Neutrophils and Macrophages) may further promote inflammatory response of cancer patients. Based on expression levels of 14 DEGs we found that first SARS‐CoV‐2 infection may accelerate progression of cancer patients by Kaplan–Meier survival, immune subtypes and tumor microenvironment analyses, and may weaken anti‐PD‐1 monoclonal antibody treatment effect of cancer patients by weighted gene co‐expression network, tumor mutation burden and microsatellite instability analysis. The second SARS‐CoV‐2 infection was beneficial to control development of tumor seemingly, but it may be difficult for cancer patients to experience destroy successfully from first SARS‐CoV‐2 infection, let alone benefits from second SARS‐CoV‐2 infection. In addition, this study also emphasized significance of multi‐factor analysis when analyzing impacts of SARS‐CoV‐2 infection on cancer patients.
Supervised machine learning for theory building and testing: Opportunities in operations management
Authors: Chou Yen‐Chun , Chuang Howard Hao‐Chun , Chou Ping , Oliva Rogelio 
Published: January 2023

AbstractMachine learning's (ML's) unique power to approximate functions and identify non‐obvious regularities in data have attracted considerable attention from researchers in natural and social sciences. The emergence of predictive modeling applications in OM studies notwithstanding, it remains unclear how OM scholars can effectively leverage supervised ML for theory building and theory testing, the primary goals of scientific research. We attempt to fill this gap by conducting a literature review of recent developments in supervised ML in OM to identify vacancies in the extant literature, shedding light on how ML applications can move beyond problem‐solving into theory building, and formulating a procedure to help OM scholars leverage ML for exploratory theory development. Our procedure employs the random forest with well‐developed properties and inference toolkits that are crucial for empirical research. We then expand the boundary of ML usage and connect supervised ML to the explanatory modeling and hypothesis testing employed by OM empiricists for decades, and discuss the use of supervised ML for causal inference from observational data. We posit that contemporary ML can facilitate pattern exploration and enhance the validity of theory testing. We conclude by discussing directions for future empirical OM studies that aim to leverage ML.
Harmonization and standardization of nucleus pulposus cell extraction and culture methods
Authors: Basatvat Shaghayegh , Bach Frances C. , Barcellona Marcos N. , Binch Abbie L. , Buckley Conor T. , Bueno Brian , Chahine Nadeen O. , Chee Ana , Creemers Laura B. , Dudli Stefan , Fearing Bailey , Ferguson Stephen J. , Gansau Jennifer , Gantenbein Benjamin , Gawri Rahul , Glaeser Juliane D. , Grad Sibylle , Guerrero Julien , Haglund Lisbet , Hernandez Paula A. , Hoyland Judith A. , Huang Charles , Iatridis James C. , Illien‐Junger Svenja , Jing Liufang , Kraus Petra , Laagland Lisanne T. , Lang Gernot , Leung Victor , Li Zhen , Lufkin Thomas , van Maanen Josette C. , McDonnell Emily E. , Panebianco Chris J. , Presciutti Steven M. , Rao Sanjna , Richardson Stephen M. , Romereim Sarah , Schmitz Tara C. , Schol Jordy , Setton Lori , Sheyn Dmitriy , Snuggs Joseph W. , Sun Y. , Tan Xiaohong , Tryfonidou Marianna A. , Vo Nam , Wang Dong , Williams Brandon , Williams Rebecca , Yoon S. Tim , Le Maitre Christine L. 
Published: January 2023

AbstractBackgroundIn vitro studies using nucleus pulposus (NP) cells are commonly used to investigate disc cell biology and pathogenesis, or to aid in the development of new therapies. However, lab‐to‐lab variability jeopardizes the much‐needed progress in the field. Here, an international group of spine scientists collaborated to standardize extraction and expansion techniques for NP cells to reduce variability, improve comparability between labs and improve utilization of funding and resources.MethodsThe most commonly applied methods for NP cell extraction, expansion, and re‐differentiation were identified using a questionnaire to research groups worldwide. NP cell extraction methods from rat, rabbit, pig, dog, cow, and human NP tissue were experimentally assessed. Expansion and re‐differentiation media and techniques were also investigated.ResultsRecommended protocols are provided for extraction, expansion, and re‐differentiation of NP cells from common species utilized for NP cell culture.ConclusionsThis international, multilab and multispecies study identified cell extraction methods for greater cell yield and fewer gene expression changes by applying species‐specific pronase usage, 60–100 U/ml collagenase for shorter durations. Recommendations for NP cell expansion, passage number, and many factors driving successful cell culture in different species are also addressed to support harmonization, rigor, and cross‐lab comparisons on NP cells worldwide.
Circadian‐mediated regulation of cardiometabolic disorders and aging with time‐restricted feeding
Authors: Roth Jonathan R. , Varshney Shweta , de Moraes Ruan Carlos Macedo , Melkani Girish C. 
Published: January 2023

AbstractCircadian rhythms are present throughout biology, from the molecular level to complex behaviors such as eating and sleeping. They are driven by molecular clocks within cells, and different tissues can have unique rhythms. Circadian disruption can trigger obesity and other common metabolic disorders such as aging, diabetes, and cardiovascular disease, and circadian genes control metabolism. At an organismal level, feeding and fasting rhythms are key drivers of circadian rhythms. This underscores the bidirectional relationship between metabolism and circadian rhythms, and many metabolic disorders have circadian disruption or misalignment. Therefore, studying circadian rhythms may offer new avenues for understanding the etiology and management of obesity. This review describes how circadian rhythm dysregulation is linked with cardiometabolic disorders and how the lifestyle intervention of time‐restricted feeding (TRF) regulates them. TRF reinforces feeding‐fasting rhythms without reducing caloric intake and ameliorates metabolic disorders such as obesity and associated cardiac dysfunction, along with reducing inflammation. TRF optimizes the expression of genes and pathways related to normal metabolic function, linking metabolism with TRF's benefits and demonstrating the molecular link between metabolic disorders and circadian rhythms. Thus, TRF has tremendous therapeutic potential that could be easily adopted to reduce obesity‐linked dysfunction and cardiometabolic disorders.
In silico Genotoxicity Prediction by Similarity Search and Machine Learning Algorithm: Optimization and Validation of the Method for High Energetic Materials
Authors: Fournier Mailys , Vroland Christophe , Megy Simon , Aguero Stéphanie , Chemelle Julie‐Anne , Defoort Brigitte , Jacob Guy , Terreux Raphaël 
Published: January 2023

AbstractThe European regulation REACh (Registration, Evaluation, Authorization, and restriction of Chemicals) has placed responsibility on the industry to manage the risk from chemicals since 2006. In order to ensure a high level of protection of human health and environment, toxicity prediction methods are now a widely used tool for regulatory decision making and selection of leads in new substances design. These in silico methods are an alternative to traditional in vitro and in vivo testing methods, which are laborious, time‐consuming, highly expensive, and even involve animal welfare issues. Many computational methods have been employed to predict the toxicity profile of substances, but they are mostly adapted to pharmaceutical molecules and not to High Energetic Materials (HEMs).In line with these restrictions, ArianeGroup set up a collaborative project with the French CNRS to develop optimized tools for the prediction of HEM properties, such as genotoxicity. Several in silico methods can be used to predict the properties of molecules, such as QSAR, Local QSAR or Machine Learning. We already demonstrated that using Local QSAR allows for better predictions with a good reliability [1].We therefore developed a genotoxicity prediction tool based on the structural similarity search coupled with a supervised machine learning algorithm. This tool is composed of 3 predictive models: the Ames test, the Chromosomal Aberration test and the Mouse Lymphoma Assay. The aim of this paper is to evaluate the performance of these models to predict the genotoxicity of HEMs. We also present the methodology we applied to build these models and to optimize their performances. The dimensional reduction of the training set and the hyperparameters tuning of the different algorithms showed a performance acceleration and a significant reduction of the overfitting, which caused a decline in the generalization capacity of the predictive models. The performance of the predictive models was evaluated on a test set of HEMs and compared to the results of other prediction softwares.
Predictive output feedback control of networked control system with Markov DoS attack and time delay
Authors: Qiu Li , Xiang Caixia , Wen Yidan , Najariyan Marzieh , Liu Chengxiang , Wu Zongze 
Published: January 2023

SummaryThis paper studies the networked control systems (NCS) predictive output feedback control issue with random time delay and denial of service (DoS) attacks. The mathematical description of the random DoS attacks and time delays in controller‐to‐actuator (C‐A) and sensor‐to‐controller (S‐C) communication channels is researched based on Markov stochastic theory. Considering the random characteristics of network‐induced attacks and delays, the closed‐loop NCS is modeled as a discrete‐time linear system with Markov DoS attack and time delay. The stochastic stability criterion of a class of networked predictive control systems with random attack and delay is proposed based on Lyapunov stability theory and the predictive compensation control method. The networked model predictive output feedback controller is designed by applying the parameter compensation method and linear matrix inequality (LMI) technique. Numerical simulation results are proposed to verify the effectiveness of the proposed control strategy.
Practical output regulation for nonlinear multiagent systems with event‐triggered control protocol under directed switching networks
Authors: Wang Jiaqi , Ma Jiali , Fei Shumin 
Published: January 2023

SummaryIn this paper, the event‐triggered‐based cooperative global robust practical output regulation problem is studied for nonlinear multiagent systems with directed switching networks. Firstly, the considered problem can be resolved with a prerequisite where the problem is solvable by an input‐to‐state stabilizing controller with measurement feedback. Specifically, the considered problem can be converted into a stabilization one for the so‐called augmented system through the canonical internal model construction. Then by combining the multiple Lyapunov functions (MLF) with the average dwell time (ADT) method, it is demonstrated that this stabilization problem can be solved by output feedback with measurement disturbances, which indicates a solution to the initial problem. At last, a suitable event‐triggering mechanism is proposed to save communication resources and reduce computational burden among all the agents. Finally, a group of FitzHugh‐Nagumo multiagent systems is provided to verify our theoretical results.
Balancing or prioritising for sustainable development? Perceptions of sustainability integration among professionals
Authors: Montesano Francesco S. , Biermann Frank , Kalfagianni Agni , Vijge Marjanneke J. 
Published: January 2023

AbstractThe effective integration of the environmental with the economic and social dimensions of sustainability will only succeed when the core problem perceptions of professionals in these fields adjust as well. Yet, while sustainability integration in general has been thoroughly researched, few studies have analysed the specific role of subjective understandings among professionals. This article bridges this gap by asking how professionals understand and operationalise sustainability integration. Which factors shape their perceptions and practices? We present the results of an online survey that was circulated to a diverse sample of professionals and then analysed using statistical methods. Responses from 508 participants show that sustainability integration is mostly perceived as a balance across all three sustainability dimensions rather than a hierarchy with the environment at its basis. However, perceptions also vary significantly across professional and geographical contexts. This signifies the need to better account for how individual circumstances affect processes of integration.
Analysing agricultural policy outcomes in the uplands of Indonesia: A multi‐dimensional sustainability assessment
Authors: Amaruzaman Sacha , Bardsley Douglas K. , Stringer Randy 
Published: January 2023

AbstractA wide range of holistic frameworks are used to assess the sustainability of agricultural policies and programs, but much of the existing research tends to overlook the socio‐cultural and governance dimensions of sustainability. This article aims to address those gaps by comprehensively assessing the environmental, economic, social, and political dimensions of sustainability. We use a case study of irrigation policies for agricultural expansion that target the Pagar Alam upland region in Indonesia. The assessment reveals opportunities and threats from the policy that affect the sustainability of upland landscapes and communities. By overly focusing on productivity goals while ignoring other sustainability criteria, the policy generates risks that threaten existing sustainable development pathways. To achieve positive policy outcomes, Indonesia needs to reconcile its national food production goal with local development goals. Lastly, to optimise policy outcomes, agri‐sustainability research should apply comprehensive approaches that simultaneously address multiple sustainability dimensions.
Towards federated learning: An overview of methods and applications
Authors: Silva Paula Raissa , Vinagre João , Gama João 
Published: January 2023

AbstractFederated learning (FL) is a collaborative, decentralized privacy‐preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub‐area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an in‐depth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation.This article is categorized under:
Technologies > Machine Learning
Technologies > Artificial Intelligence
Mathematical considerations for evaluating X‐ray beam size in micro‐XRF analysis
Authors: Nakae Masanori , Matsuyama Tsugufumi , Ishi Hideyuki , Tsuji Kouichi 
Published: January 2023

AbstractMicro X‐ray fluorescence spectrometry (micro‐XRF) is conducted for elemental imaging and analysis of small regions, by focusing on primary X‐rays. An appropriate beam diameter evaluation is essential as is directly related to the spatial resolution of micro‐XRF imaging. The X‐ray beam size was experimentally determined from the XRF intensity with respect to the scanning distance (thin‐wire scanning method) or its differentiated curve (knife‐edge scanning method). However, a beam diameter is not currently evaluated in a mathematically appropriate method and is known to differ significantly from the actual value under certain conditions. A formula to correct this difference has been proposed; however, to the best of our knowledge, no study has discussed the performance of the correction formula and conditions under which they can be applied. By mathematically analyzing the overlapping volume between the X‐ray beam and the scanning material, we succeeded in deriving an appropriate fitting function that replaces the Gaussian function taking into account the effect of absorption. The fitting function is effective for each method. This technique was applied to evaluate the micro X‐ray beam created using polycapillary X‐ray focusing optics. Both thin‐wire and knife‐edge scanning methods were used with wires of different sizes to determine the X‐ray beam size. The beam size obtained by the conventional fitting function can be accurately corrected using the newly derived correction formula. Simulations were performed using the derived fitting function, and the applicability of the correction formulas was demonstrated.
Transition to Peace, 1945–1946
Authors: Goss W. M. , Hooker Claire , Ekers Ronald D. 
Published: January 2023

AbstractWartime can create significant changes in people and institutions. New opportunities are envisaged. Australian scientists imagined many new scientific projects, and they also dreamed of improvements to society and culture to which science might contribute. Many people emerged from wartime with significant, passionate commitments to building a better future. As Schedvin (1987, p. 334) wrote of the immediate post-war period:These general remarks were certainly true of most of those who worked on radar during the war and became part of radio astronomy in the post-war decades. The importance of building capacity in Australian science—independent of the UK, and able to thrive nationally—was a priority for many. At the same time, the importance of developing scientific internationalism, which often seemed like the obverse of the populist authoritarianism that had underpinned the dreadful brutality of WWII, was also keenly felt.
A New Field of Science, Postwar
Authors: Goss W. M. , Hooker Claire , Ekers Ronald D. 
Published: January 2023

AbstractBy the time Joe and Lenore Pawsey were ready to embark on their voyage to North America and Europe in September 1947, the scientific importance of the trip had increased substantially. In the 2 years since the conclusion of the war, radio observations of the sun had produced dramatic results. The first discrete radio source had been discovered by Hey in the UK, and early data from radio observations of the cosmos promised to lead to startling new discoveries.