bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dopamine and serotonin in human substantia nigra track social context and value signals during economic exchange
Authors: Batten Seth R. , Bang Dan , Kopell Brian H. , Davis Arianna N. , Heflin Matthew , Fu Qixiu , Perl Ofer , Ziafat Kimia , Hashemi Alice , Saez Ignacio , Barbosa Leonardo S. , Twomey Thomas , Lohrenz Terry , White Jason P. , Dayan Peter , Charney Alexander W. , Figee Martijn , Mayberg Helen S. , Kishida Kenneth T. , Gu Xiaosi , Montague P. Read 
Published: February 2024

AbstractDopamine and serotonin are hypothesized to guide social behaviours. In humans, however, we have not yet been able to study neuromodulator dynamics as social interaction unfolds. Here, we obtained subsecond estimates of dopamine and serotonin from human substantia nigra pars reticulata during the ultimatum game. Participants, who were patients with Parkinson’s disease undergoing awake brain surgery, had to accept or reject monetary offers of varying fairness from human and computer players. They rejected more offers in the human than the computer condition, an effect of social context associated with higher overall levels of dopamine but not serotonin. Regardless of the social context, relative changes in dopamine tracked trial-by-trial changes in offer value—akin to reward prediction errors—whereas serotonin tracked the current offer value. These results show that dopamine and serotonin fluctuations in one of the basal ganglia’s main output structures reflect distinct social context and value signals.
The astrocyte-produced growth factor HB-EGF limits autoimmune CNS pathology
Authors: Linnerbauer Mathias , Lößlein Lena , Vandrey Oliver , Peter Anne , Han Yanan , Tsaktanis Thanos , Wogram Emile , Needhamsen Maria , Kular Lara , Nagel Lisa , Zissler Julia , Andert Marie , Meszaros Lisa , Hanspach Jannis , Zuber Finnja , Naumann Ulrike J. , Diebold Martin , Wheeler Michael A. , Beyer Tobias , Nirschl Lucy , Cirac Ana , Laun Frederik B. , Günther Claudia , Winkler Jürgen , Bäuerle Tobias , Jagodic Maja , Hemmer Bernhard , Prinz Marco , Quintana Francisco J. , Rothhammer Veit 
Published: February 2024

AbstractCentral nervous system (CNS)-resident cells such as microglia, oligodendrocytes and astrocytes are gaining increasing attention in respect to their contribution to CNS pathologies including multiple sclerosis (MS). Several studies have demonstrated the involvement of pro-inflammatory glial subsets in the pathogenesis and propagation of inflammatory events in MS and its animal models. However, it has only recently become clear that the underlying heterogeneity of astrocytes and microglia can not only drive inflammation, but also lead to its resolution through direct and indirect mechanisms. Failure of these tissue-protective mechanisms may potentiate disease and increase the risk of conversion to progressive stages of MS, for which currently available therapies are limited. Using proteomic analyses of cerebrospinal fluid specimens from patients with MS in combination with experimental studies, we here identify Heparin-binding EGF-like growth factor (HB-EGF) as a central mediator of tissue-protective and anti-inflammatory effects important for the recovery from acute inflammatory lesions in CNS autoimmunity. Hypoxic conditions drive the rapid upregulation of HB-EGF by astrocytes during early CNS inflammation, while pro-inflammatory conditions suppress trophic HB-EGF signaling through epigenetic modifications. Finally, we demonstrate both anti-inflammatory and tissue-protective effects of HB-EGF in a broad variety of cell types in vitro and use intranasal administration of HB-EGF in acute and post-acute stages of autoimmune neuroinflammation to attenuate disease in a preclinical mouse model of MS. Altogether, we identify astrocyte-derived HB-EGF and its epigenetic regulation as a modulator of autoimmune CNS inflammation and potential therapeutic target in MS.
Nanoparticles synthesis in microwave plasmas: peculiarities and comprehensive insight
Authors: Ouaras Karim , Lombardi Guillaume , Hassouni Khaled 
Published: February 2024

AbstractLow-pressure plasma processes are routinely used to grow, functionalize or etch materials, and thanks to some of its unique attributes, plasma has become a major player for some applications such as microelectronics. Plasma processes are however still at a research level when it comes to the synthesis and functionalization of nanoparticles. Yet plasma processes can offer a particularly suitable solution to produce nanoparticles having very peculiar features since they enable to: (i) reach particle with a variety of chemical compositions, (ii) tune the size and density of the particle cloud by acting on the transport dynamics of neutral or charged particles through a convenient setting of the thermal gradients or the electric field topology in the reactor chamber and (iii) manipulate nanoparticles and deposit them directly onto a substrate, or codeposit them along with a continuous film to produce nanocomposites or (iv) use them as a template to produce 1D materials. In this article, we present an experimental investigation of nanoparticles synthesis and dynamics in low-pressure microwave plasmas by combining time-resolved and in-situ laser extinction and scattering diagnostics, QCL absorption spectroscopy, mass spectrometry, optical emission spectroscopy and SEM along with a particle transport model. We showed for the first time the thermophoresis-driven dynamic of particle cloud in electrodless microwave plasmas. We showed that this effect is linked to particular fluctuations in the plasma composition and results in the formation of a void region in the bulk of the plasma surrounded by a particle cloud in the peripherical post-discharge. We also reveals and analyze the kinetics of precursor dissociation and molecular growth that result in the observed nanoparticle nucleation.
The optimal cut-off values of Klotho for predicting all-cause and cardiovascular mortality among chronic kidney disease: results from NHANES
Authors: Liu Lili , Jia Junya , Cheng Xi , Gao Shan , Yan Tiekun 
Published: February 2024

AbstractTo explore the optimal cut-off values of Klotho for predicting all-cause and cardiovascular mortality among chronic kidney disease (CKD) patients. Klotho was measured in 40–79-year-old individuals in the NHANES 2007–2016. A total of 2418 patients with stage 1–4 CKD were included. The optimal cut-off values of Klotho were utilized using receiver operator characteristic (ROC) curves and be verified on the effects of all-cause and cardiovascular mortality. Restricted cubic splines were used to examine the relationship between Klotho and all-cause and cardiovascular mortality with the optimal cutpoints as the reference. After a mean follow-up period of 87.9 months, 535 deaths occurred and 188 died of cardiovascular disease. Cubic splines showed that the risk of all-cause and cardiovascular mortality increased gradually for Klotho < 700 pg/ml. ROC curves revealed that the optimal cut-off values of Klotho for all-cause and cardiovascular mortality are 548.8 pg/ml and 660.9 pg/ml, respectively. Compared to patients with higher levels of Klotho, HRs (95% CIs) for all-cause and cardiovascular mortality were 1.52 (1.23, 1.87) and 1.58 (1.13, 2.22) among patients with lower levels of Klotho, respectively, in the multivariate model (P < .0001 and P = 0.008). Our findings revealed the optimal cut-off values of Klotho for all-cause and cardiovascular mortality in CKD.
Rural communities experience higher radon exposure versus urban areas, potentially due to drilled groundwater well annuli acting as unintended radon gas migration conduits
Authors: Khan Selim M. , Pearson Dustin D. , Eldridge Evangeline L. , Morais Tiago A. , Ahanonu Marvit I. C. , Ryan M. Cathryn , Taron Joshua M. , Goodarzi Aaron A. 
Published: February 2024

AbstractRepetitive, long-term inhalation of radioactive radon gas is one of the leading causes of lung cancer, with exposure differences being a function of geographic location, built environment, personal demographics, activity patterns, and decision-making. Here, we examine radon exposure disparities across the urban-to-rural landscape, based on 42,051 Canadian residential properties in 2034 distinct communities. People living in rural, lower population density communities experience as much as 31.2% greater average residential radon levels relative to urban equivalents, equating to an additional 26.7 Bq/m3 excess in geometric mean indoor air radon, and an additional 1 mSv/year in excess alpha radiation exposure dose rate to the lungs for occupants. Pairwise and multivariate analyses indicate that community-based radon exposure disparities are, in part, explained by increased prevalence of larger floorplan bungalows in rural areas, but that a majority of the effect is attributed to proximity to, but not water use from, drilled groundwater wells. We propose that unintended radon gas migration in the annulus of drilled groundwater wells provides radon migration pathways from the deeper subsurface into near-surface materials. Our findings highlight a previously under-appreciated determinant of radon-induced lung cancer risk, and support a need for targeted radon testing and reduction in rural communities.
The role of dorsal premotor cortex in joint action inhibition
Authors: Dolfini Elisa , Cardellicchio Pasquale , Fadiga Luciano , D’Ausilio Alessandro 
Published: February 2024

AbstractBehavioral interpersonal coordination requires smooth negotiation of actions in time and space (joint action—JA). Inhibitory control may play a role in fine-tuning appropriate coordinative responses. To date, little research has been conducted on motor inhibition during JA and on the modulatory influence that premotor areas might exert on inhibitory control. Here, we used an interactive task in which subjects were required to reach and open a bottle using one hand. The bottle was held and stabilized by a co-actor (JA) or by a mechanical holder (vice clamp, no-JA). We recorded two TMS-based indices of inhibition (short-interval intracortical inhibition—sICI; cortical silent period—cSP) during the reaching phase of the task. These reflect fast intracortical (GABAa-mediated) and slow corticospinal (GABAb-mediated) inhibition. Offline continuous theta burst stimulation (cTBS) was used to interfere with dorsal premotor cortex (PMd), ventral premotor cortex (PMv), and control site (vertex) before the execution of the task. Our results confirm a dissociation between fast and slow inhibition during JA coordination and provide evidence that premotor areas drive only slow inhibitory mechanisms, which in turn may reflect behavioral co-adaptation between trials. Exploratory analyses further suggest that PMd, more than PMv, is the key source of modulatory drive sculpting movements, according to the socio-interactive context.
Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI)
Authors: Dodo Yakubu , Arif Kiran , Alyami Mana , Ali Mujahid , Najeh Taoufik , Gamil Yaser 
Published: February 2024

AbstractGeo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.
Effect of rock properties on wear and cutting performance of multi blade circular saw with iron based multi-layer diamond segments
Authors: Rajpurohit Sohan Singh , Fissha Yewuhalashet , Sinha Rabindra Kumar , Ali Mujahid , Ikeda Hajime , Ghribi Wade , Najeh Taoufik , Gamil Yaser , Kawamura Youhei 
Published: February 2024

AbstractThis study is an attempt for comprehensive, combining experimental data with advanced analytical techniques and machine learning for a thorough understanding of the factors influencing the wear and cutting performance of multi-blade diamond disc cutters on granite blocks. A series of sawing experiments were performed to evaluate the wear and cutting performance of multi blade diamond disc cutters with varying diameters in the processing of large-sized granite blocks. The multi-layer diamond segments comprising the Iron (Fe) based metal matrix were brazed on the sawing blades. The segment’s wear was studied through micrographs and data obtained from the Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDS). Granite rock samples of nine varieties were tested in the laboratory to determine the quantitative rock parameters. The contribution of individual rock parameters and their combined effects on wear and cutting performance of multi blade saw were correlated using statistical machine learning methods. Moreover, predictive models were developed to estimate the wear and cutting rate based on the most significant rock properties. The point load strength index, uniaxial compressive strength, and deformability, Cerchar abrasivity index, and Cerchar hardness index were found to be the significant variables affecting the sawing performance.
Measurement of synaptic density in Down syndrome using PET imaging: a pilot study
Authors: DiFilippo Alexandra , Jonaitis Erin , Makuch Renee , Gambetti Brianna , Fleming Victoria , Ennis Gilda , Barnhart Todd , Engle Jonathan , Bendlin Barbara , Johnson Sterling , Handen Benjamin , Krinsky-McHale Sharon , Hartley Sigan , Christian Bradley 
Published: February 2024

AbstractDown syndrome (DS) is the most prevalent genetic cause of intellectual disability, resulting from trisomy 21. Recently, positron emission tomography (PET) imaging has been used to image synapses in vivo. The motivation for this pilot study was to investigate whether synaptic density in low functioning adults with DS can be evaluated using the PET radiotracer [11C]UCB-J. Data were acquired from low functioning adults with DS (n = 4) and older neurotypical (NT) adults (n = 37). Motion during the scans required the use of a 10-minute acquisition window for the calculation of synaptic density using SUVR50–60,CS which was determined to be a suitable approximation for specific binding in this analysis using dynamic data from the NT group. Of the regions analyzed a large effect was observed when comparing DS and NT hippocampus and cerebral cortex synaptic density as well as hippocampus and cerebellum volumes. In this pilot study, PET imaging of [11C]UCB-J was successfully completed and synaptic density measured in low functioning DS adults. This work provides the basis for studies where synaptic density may be compared between larger groups of NT adults and adults with DS who have varying degrees of baseline cognitive status.
Evaluation of physical parameters and spectral characterization of the quality of soaps containing by-products from the food industry
Authors: Łusiak Patrycja , Różyło Renata , Mazur Jacek , Sobczak Paweł , Matwijczuk Arkadiusz 
Published: February 2024

AbstractThe present study investigated several properties such as friction, hardness, penetration work, and cutting forces in soaps formulated with apple and carrot pomace at varying concentrations of 5%, 10%, and 15%. To gain insights into the molecular-level alterations within the formulated soap samples, they were spectroscopically analyzed using Fourier-transform infrared spectroscopy. The sliding friction analyses revealed that the investigated extrudate additives had no significant impact on the frictional forces of the soaps. However, notable differences were observed in the cutting force, hardness, and penetration work between the control and pomace-added samples. Excluding the control samples, no statistically significant distinctions were found between the cutting force, hardness, and work of penetration of soaps containing apple pomace and carrot pomace. Moreover, the quantity of pomace incorporated did not induce any significant variations in the results. The obtained samples were characterised at the molecular level using FTIR Fourier transform infrared spectroscopy. On the other hand, alterations in band intensities suggested improved molecular packing of the compounds within the samples due to the presence of the additives.
Evaluation of retrieval accuracy and visual similarity in content-based image retrieval of chest CT for obstructive lung disease
Authors: Choe Jooae , Choi Hye Young , Lee Sang Min , Oh Sang Young , Hwang Hye Jeon , Kim Namkug , Yun Jihye , Lee Jae Seung , Oh Yeon-Mok , Yu Donghoon , Kim Byeongsoo , Seo Joon Beom 
Published: February 2024

AbstractThe aim of our study was to assess the performance of content-based image retrieval (CBIR) for similar chest computed tomography (CT) in obstructive lung disease. This retrospective study included patients with obstructive lung disease who underwent volumetric chest CT scans. The CBIR database included 600 chest CT scans from 541 patients. To assess the system performance, follow-up chest CT scans of 50 patients were evaluated as query cases, which showed the stability of the CT findings between baseline and follow-up chest CT, as confirmed by thoracic radiologists. The CBIR system retrieved the top five similar CT scans for each query case from the database by quantifying and comparing emphysema extent and size, airway wall thickness, and peripheral pulmonary vasculatures in descending order from the database. The rates of retrieval of the same pairs of query CT scans in the top 1–5 retrievals were assessed. Two expert chest radiologists evaluated the visual similarities between the query and retrieved CT scans using a five-point scale grading system. The rates of retrieving the same pairs of query CTs were 60.0% (30/50) and 68.0% (34/50) for top-three and top-five retrievals. Radiologists rated 64.8% (95% confidence interval 58.8–70.4) of the retrieved CT scans with a visual similarity score of four or five and at least one case scored five points in 74% (74/100) of all query cases. The proposed CBIR system for obstructive lung disease integrating quantitative CT measures demonstrated potential for retrieving chest CT scans with similar imaging phenotypes. Further refinement and validation in this field would be valuable.
Highly conductive, conformable ionic laser-induced graphene electrodes for flexible iontronic devices
Authors: Kim So Young , Kim Ji Hong , Kim Kyeong Nam , Oh Hayoung , Myung Sung , Kim Do Hwan 
Published: February 2024

AbstractIontronic devices, recognized for user-friendly soft electronics, establish an electrical double layer (EDL) at the interface between ion gels and electrodes, significantly influencing device performance. Despite extensive research on ion gels and diverse electrode materials, achieving a stable interfacial formation remains a persistent challenge. In this work, we report a solution to address this challenge by employing CO2 irradiation as a bottom-up methodology to directly fabricate highly conductive, conformable laser-induced graphene (LIG) electrodes on a polyimide (PI)-based ion gel. The PI ion gel exhibits exceptional EDL formation at the electrode interface, primarily attributable to efficient ion migration. Particularly, ionic laser-induced graphene (i-LIG) electrodes, derived from the PI ion gel as a precursor, yield high-quality graphene with enhanced crystallinity and an expanded porous structure in the upward direction. This outcome is achieved through a pronounced thermal transfer effect and intercalation phenomenon between graphene layers, facilitated by the presence of ionic liquids (ILs) within the PI ion gel. Ultimately, in comparison to alternative soft electrode-based vertical capacitors, the utilization of i-LIGs and PI ion gels in the vertical capacitor demonstrates reduced interfacial resistance and increased EDL capacitance, emphasizing the extensive potential of iontronic devices. These results not only highlight these features but also introduce a new perspective for advancing next-generation iontronic devices.
Prediction of heavy-section ductile iron fracture toughness based on machine learning
Authors: Song Liang , Zhang Hongcheng , Zhang Junxing , Guo Hai 
Published: February 2024

AbstractThe preparation process and composition design of heavy-section ductile iron are the key factors affecting its fracture toughness. These factors are challenging to address due to the long casting cycle, high cost and complex influencing factors of this type of iron. In this paper, 18 cubic physical simulation test blocks with 400 mm wall thickness were prepared by adjusting the C, Si and Mn contents in heavy-section ductile iron using a homemade physical simulation casting system. Four locations with different cooling rates were selected for each specimen, and 72 specimens with different compositions and cooling times of the heavy-section ductile iron were prepared. Six machine learning-based heavy-section ductile iron fracture toughness predictive models were constructed based on measured data with the C content, Si content, Mn content and cooling rate as input data and the fracture toughness as the output data. The experimental results showed that the constructed bagging model has high accuracy in predicting the fracture toughness of heavy-section ductile iron, with a coefficient of coefficient (R2) of 0.9990 and a root mean square error (RMSE) of 0.2373.