bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring Favorable Supramolecular Interactions of Multifluorinated Aromatics in Dendronized Push‐Pull Chromophores for Electro‐Optics
Authors: Zou Jie , Zhang Di , Thi Quoc Huy , Chen Weilong , Xu Gan , Lu Zhenpin , Ly Thuc Hue , Luo Jingdong 
Published: June 2024

AbstractMultifluorinated aromatics serve as supramolecular synthons in the research of organic electro‐optic (EO) materials by exploiting π‐π stacking interaction between the aromatic hydrocarbon and multifluorinated aromatic groups for performance improvement. However, non‐classical hydrogen bonding remains largely unexplored in fluorinated EO dendrimers. In this study, three Fréchet‐type generation 1 benzyl ether co‐dendrons were synthesized by replacing one benzyl group with 2,3,5,6‐tetrafluorobenzyl (p‐HF4Bz), pentafluorobenzyl (C6F5Bz), and 2,3,4,5‐tetrafluorobenzyl (o‐HF4Bz) groups, to afford the benzoic acid derivatives D1, D2, and D3, which were further bonded to the donor and π‐bridge moieties to afford three co‐dendronized push‐pull phenyltetraene chromophores EOD1, EOD2, and EOD3, respectively. The weak C−H⋅⋅⋅X (X=O, F) interactions in the crystal structure of D1 cumulatively add to the benzoic acid dimers to form an extended hydrogen‐bonded network, while D2 is crystallized into a centric one‐dimensional chain with strong intermolecular interactions. The poled films of EOD1 with PMMA exhibited the largest and most stable EO activity with optical homogeneity among the series. The results identify the effectiveness of weak but favorable hydrogen bonds enabled by the enhanced carbon acidity of p‐HF4Bz synthon in D1, over the interactions in D2 and D3, for the rational design of supramolecular EO dendrimers.
Establishment of a Four‐Cell In Vitro Blood‐Brain Barrier Model With Human Primary Brain Cells
Authors: Malik Johid R. , Modebelu Ukamaka O. , Fletcher Courtney V. , Podany Anthony T. , Scarsi Kimberly K. , Byrareddy Siddappa N. , Anand Robbyn K. , Buch Shilpa , Sil Susmia , Le Jennifer , Bradley John S. , Brown Ashley N. , Sutar Debapriya , Avedissian Sean N. 
Published: June 2024

AbstractThe blood‐brain barrier (BBB) constitutes a crucial protective anatomical layer with a microenvironment that tightly controls material transit. Constructing an in vitro BBB model to replicate in vivo features requires the sequential layering of constituent cell types. Maintaining heightened integrity in the observed tight junctions during both the establishment and post‐experiment phases is crucial to the success of these models. We have developed an in vitro BBB model that replicates the cellular composition and spatial orientation of in vivo BBB observed in humans. The experiment includes comprehensive procedures and steps aimed at enhancing the integration of the four‐cell model. Departing from conventional in vitro BBB models, our methodology eliminates the necessity for pre‐coated plates to facilitate cell adhesion, thereby improving cell visualization throughout the procedure. An in‐house coating strategy and a simple yet effective approach significantly reduce costs and provides superior imaging of cells and corresponding tight junction protein expression. Also, our BBB model includes all four primary cell types that are structural parts of the human BBB. With its innovative and user‐friendly features, our in‐house optimized in vitro four‐cell‐based BBB model showcases novel methodology and provides a promising experimental platform for drug screening processes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Coating and culture systemBasic Protocol 2: Cell seeding and Transwell insert handlingBasic Protocol 3: Assessment of model functionality
Automatic house location identification using location service based VANET
Authors: Ranjan Senapati Biswa , Ranjan Swain Rakesh , Mohan Khilar Pabitra , Kumar Bhoi Sourav , Sagar Sahoo Kshira 
Published: June 2024

SummaryThe vehicular ad hoc network (VANET) provides a variety of applications and is gaining popularity due to the reuse of network resources. Exact location identification with optimum delay is the demand of all vehicle users. Currently, individuals are utilizing the Global Positioning System (GPS) to ascertain the precise geographical coordinates of a given location. The drawback of GPS technology is its inability to accurately determine location and use GPS in some remote areas. Searching for the location of a particular house randomly incurs a loss of fuel as well as increases delay. This motivates us to propose one automated method through VANET using location service‐based routing for the location identification of a house. The proposed work involves searching for the location of a house using the open‐source MongoDB database, and the operations on the database are performed using the tool Node‐Red. By simulation using SUMO and Network Simulator 2.35, the proposed work is evaluated and compared with existing location service‐based routing like geographic location service (GLS) and hierarchical location service (HLS). The proposed work performs better in terms of routing efficiency (end‐to‐end latency, packet delivery rate), location efficiency (request sends, query success rate, and request travel time), and routing and location overhead (MAC bandwidth consumption). Also, the performance of the proposed work is presented as stable by increasing the number of vehicles. The statistical analysis of the packet delivery ratio and CBR end‐to‐end latency is carried out using T score.
A machine learning approach for potential Super‐Agers identification using neuronal functional connectivity networks
Authors: Fili Mohammad , Mohammadiarvejeh Parvin , Klinedinst Brandon S. , Wang Qian , Moody Shannin , Barnett Neil , Pollpeter Amy , Larsen Brittany , Li Tianqi , Willette Sara A. , Mochel Jonathan P. , Allenspach Karin , Hu Guiping , Willette Auriel A. 
Published: June 2024

AbstractINTRODUCTIONAging is often associated with cognitive decline. Understanding neural factors that distinguish adults in midlife with superior cognitive abilities (Positive‐Agers) may offer insight into how the aging brain achieves resilience. The goals of this study are to (1) introduce an optimal labeling mechanism to distinguish between Positive‐Agers and Cognitive Decliners, and (2) identify Positive‐Agers using neuronal functional connectivity networks data and demographics.METHODSIn this study, principal component analysis initially created latent cognitive trajectories groups. A hybrid algorithm of machine learning and optimization was then designed to predict latent groups using neuronal functional connectivity networks derived from resting state functional magnetic resonance imaging. Specifically, the Optimal Labeling with Bayesian Optimization (OLBO) algorithm used an unsupervised approach, iterating a logistic regression function with Bayesian posterior updating. This study encompassed 6369 adults from the UK Biobank cohort.RESULTSOLBO outperformed baseline models, achieving an area under the curve of 88% when distinguishing between Positive‐Agers and cognitive decliners.DISCUSSIONOLBO may be a novel algorithm that distinguishes cognitive trajectories with a high degree of accuracy in cognitively unimpaired adults.Highlights
Design an algorithm to distinguish between a Positive‐Ager and a Cognitive‐Decliner.
Introduce a mathematical definition for cognitive classes based on cognitive tests.
Accurate Positive‐Ager identification using rsfMRI and demographic data (AUC = 0.88).
Posterior default mode network has the highest impact on Positive‐Aging odds ratio.
Tooth Mg/Ca ratios and Aristotle's lantern morphometrics reflect trophic types in echinoids
Authors: Vafidis Dimitris , Varkoulis Anastasios , Zaoutsos Stefanos , Voulgaris Konstantinos 
Published: June 2024

AbstractSeveral studies have inferred the ecological significance regarding the morphometrics of Aristotle's lantern and the mechanical properties of magnesium in echinoid teeth. This study attempts to combine these aspects, connecting them to the trophic habits of three native and an invasive echinoid in the Eastern Mediterranean Sea. Spatiotemporal data from the central and southern Aegean Sea were obtained, regarding the relative size of lanterns and demi‐pyramids of Arbacia lixula, Paracentrotus lividus, Sphaerechinus granularis, and the invasive echinoid Diadema setosum and the Mg/Ca ratios of four zones on the tooth cross‐section. Since environmental factors affect the examined factors, data for temperature, salinity, and concentration of chlorophyll‐a were included in a principal component analysis. A. lixula and P. lividus presented intraspecific differences in the relative size of the lantern and demi‐pyramid, while S. granularis and D. setosum exhibited variation in the elongation index. Differences in the Mg/Ca ratios were observed for all species although in different zones. Temperature appears to be related to all Mg/Ca zones except for the stone part, while the elongation index showed an inverse trend to all other morphometric parameters. The results of the PCA for the four species on the spatiotemporal level exhibited a distinction of individuals with season but not species, except for A. lixula, an omnivore with a carnivorous tendency, which was clearly separated from the herbivorous species. Using hierarchical clustering on the principal components it was evident that the three native species occupy different clusters, but when D. setosum was added, it shared the same cluster with S. granularis. This might infer similar feeding preferences, specifically for coralline algae, which might lead to a swift in the ecological equilibrium in regions, where D. setosum is found, either by affecting habitat type, or by restricting the distribution of S. granularis as was previously observed with Diadema africanum.
Body length changes for Atlantic salmon (Salmo salar) over five decades exhibit weak spatial synchrony over a broad latitudinal gradient
Authors: Imlay Tara L. , Breau Cindy , Dauphin Guillaume J. R. , Chaput Gérald , April Julien , Douglas Scott , Hogan J. Derek , McWilliam Sherise , Notte Daniela , Robertson Martha J. , Taylor Andrew , Underhill Kari , Weir Laura K. 
Published: June 2024

AbstractUnderstanding the factors that drive spatial synchrony among populations or species is important for management and recovery of populations. The range‐wide declines in Atlantic salmon (Salmo salar) populations may be the result of broad‐scale changes in the marine environment. Salmon undergo rapid growth in the ocean; therefore changing marine conditions may affect body size and fecundity estimates used to evaluate whether stock reference points are met. Using a dataset that spanned five decades, 172,268 individuals, and 19 rivers throughout Eastern Canada, we investigated the occurrence of spatial synchrony in changes in the body size of returning wild adult Atlantic salmon. Body size was then related to conditions in the marine environment (i.e., climate indices, thermal habitat availability, food availability, density‐dependence, and fisheries exploitation rates) that may act on all populations during the ocean feeding phase of their life cycle. Body size increased during the 1980s and 1990s for salmon that returned to rivers after one (1SW) or two winters at sea (2SW); however, significant changes were only observed for 1SW and/or 2SW in some mid‐latitude and northern rivers (10/13 rivers with 10 of more years of data during these decades) and not in southern rivers (0/2), suggesting weak spatial synchrony across Eastern Canada. For 1SW salmon in nine rivers, body size was longer when fisheries exploitation rates were lower. For 2SW salmon, body size was longer when suitable thermal habitat was more abundant (significant for 3/8 rivers) and the Atlantic Multidecadal Oscillation was higher (i.e., warmer sea surface temperatures; significant for 4/8 rivers). Overall, the weak spatial synchrony and variable effects of covariates on body size across rivers suggest that changes in Atlantic salmon body size may not be solely driven by shared conditions in the marine environment. Regardless, body size changes may have consequences for population management and recovery through the relationship between size and fecundity.
In‐hospital and long‐term outcomes of cardiogenic shock complicating myocardial infarction versus heart failure
Authors: Choi Ki Hong , Kang Danbee , Park Hyejeong , Park Taek Kyu , Lee Joo Myung , Song Young Bin , Hahn Joo‐Yong , Choi Seung‐Hyuk , Gwon Hyeon‐Cheol , Cho Juhee , Yang Jeong Hoon 
Published: June 2024

AimsThis study sought to examine the difference in clinical characteristics, treatment strategy, trends in mortality, and medical costs according to the aetiologies of cardiogenic shock (CS).Methods and resultsThis was a population‐based, nationwide, cohort study from the Korean National Health Insurance Service database. All CS adults (≥18 years) were admitted to an intensive care unit from January 2010 to December 2020. The primary outcome was in‐hospital mortality. The secondary outcomes were cardiac replacement therapy (left ventricular assisted device implantation or heart transplantation), all‐cause mortality, ischaemic stroke, rehospitalization for heart failure (HF) during follow‐up, and actual in‐hospital medical costs. Among 136 092 individuals with CS, 48 704 (29.7%) cases were due to acute myocardial infarction‐related CS (AMI‐CS), and the remaining 87 388 (71.3%) were due to HF‐CS (ischaemic cardiomyopathy [ICM] vs. non‐ICM, 49 504 [56.6%] vs. 37 884 [45.4%]). Patients with HF‐CS were older, less likely to be male, and less likely to receive mechanical circulatory support, compared to those with AMI‐CS. During the 10‐year study period, the in‐hospital mortality rate decreased, and actual medical costs tended to increase, regardless of CS aetiology. Compared with AMI‐CS, HF‐CS was associated with higher risks of in‐hospital mortality (40.3% vs. 28.5%; adjusted odds ratio [OR] 1.47, 95% confidence interval [CI] 1.43–1.52), cardiac replacement therapy (adjusted OR 1.65, 95% CI 1.16–2.34), as well as follow‐up mortality after successful discharge (19.3% vs. 8.5%; adjusted‐hazard ratio 1.54, 95% CI 1.48–1.59). HF‐CS had lower medical costs than AMI‐CS (adjusted ratio 0.79, 95% CI 0.79–0.80).ConclusionsWith medical advances during the past 10 years, the mortality of CS has decreased significantly, but the mortality of HF‐CS remains high. The findings highlight the need for effective treatment strategies for patients with HF‐CS.
Momentary mechanisms of binge‐eating symptoms using ecological momentary assessment: The moderating role of food addiction
Authors: Li Shirley S. , Kalan Rachel E. , Smith Alexandro , Mason Tyler B. , Smith Kathryn E. 
Published: June 2024

AbstractObjectiveFood addiction (FA) shows phenotypic and diagnostic overlap with eating disorders characterised by binge eating, though it is unknown how momentary processes driving binge‐eating symptoms differ by FA. The present study examined the possible moderating influence of FA severity on momentary mechanisms underlying binge‐eating symptomatology using ecological momentary assessment (EMA).MethodAdults (N = 49, mean age = 34.9 ± 12.1, cis‐gender female = 77.1%) who met criteria for FA and/or binge‐eating disorder completed baseline measures including the Yale Food Addiction Scale (YFAS) followed by a 10‐day EMA protocol. Generalised linear mixed models assessed main effects of YFAS, momentary antecedents (affect, impulsivity, food cue exposure, appetite, and eating expectancies) and two‐way interactions between YFAS and within‐person antecedents.ResultsFA severity moderated momentary associations between food cue exposure and subsequent binge‐eating symptoms: the association was stronger among participants with lower but not higher YFAS scores. No other interactions were significant.ConclusionsSome functional associations underlying binge‐eating symptoms vary based on individuals' level of FA symptoms. Future research to further understand how observed associations may differ amongst diverse populations and over course of illness may also inform future prevention and interventions.
AdaBoost‐powered cloud of things framework for low‐latency, energy‐efficient chronic kidney disease prediction
Authors: Al‐Kateeb Zeena N. , Abdullah Dhuha Basheer 
Published: June 2024

AbstractThe United Nations' sustainable development agenda has set an ambitious goal of reducing premature mortality from non‐communicable diseases by 33% by 2030. Among these diseases, chronic kidney disease (CKD) is a significant contributor to both morbidity and mortality. Integrating the Internet of Things (IoT) and cloud computing in healthcare has gained momentum, particularly in remote patient monitoring. However, it is essential to acknowledge that cloud computing has limitations, particularly in handling vast volumes of Big Data, mainly due to scalability and latency concerns. This article proposes a novel framework, AdaBoostCoTCKD, to mitigate latency issues, minimize response times, reduce power consumption, and optimize network resources for predicting CKD. The framework leverages the synergy between the AdaBoost machine learning technique and fog computing paradigms to enhance the precision and efficiency of CKD prediction methods. In addition, it introduces an auxiliary cloud‐based database, enriching the pool of future insights and facilitating prospective database infrastructure expansions. This augmentation is expected to impact predictive accuracy positively. We conducted comprehensive experiments to demonstrate the effectiveness of our approach. Our model achieved an impressive training accuracy of 99.928% and testing accuracy of 99.975%, while the fog environment reduced latency by 31% and energy consumption by 75% compared to traditional cloud‐based solutions. Our proposed system enables early CKD detection and offers advantages over cloud‐only solutions, providing a robust and efficient platform for healthcare IoT applications with significant clinical value. These promising results underscore the potential of combining fog computing and the AdaBoost machine learning technique to advance healthcare by addressing latency, response time, power consumption, and network resource optimization challenges in CKD prediction.
Astrocyte coverage of excitatory synapses correlates to measures of synapse structure and function in ferret primary visual cortex
Authors: Thomas Connon I. , Ryan Melissa A. , McNabb Micaiah C. , Kamasawa Naomi , Scholl Benjamin 
Published: June 2024

AbstractMost excitatory synapses in the mammalian brain are contacted or ensheathed by astrocyte processes, forming tripartite synapses. Astrocytes are thought to be critical regulators of the structural and functional dynamics of synapses. While the degree of synaptic coverage by astrocytes is known to vary across brain regions and animal species, the reason for and implications of this variability remains unknown. Further, how astrocyte coverage of synapses relates to in vivo functional properties of individual synapses has not been investigated. Here, we characterized astrocyte coverage of synapses of pyramidal neurons in the ferret visual cortex and, using correlative light and electron microscopy, examined their relationship to synaptic strength and sensory‐evoked Ca2+ activity. Nearly, all synapses were contacted by astrocytes, and most were contacted along the axon–spine interface. Structurally, we found that the degree of synaptic astrocyte coverage directly scaled with synapse size and postsynaptic density complexity. Functionally, we found that the amount of astrocyte coverage scaled with how selectively a synapse responds to a particular visual stimulus and, at least for the largest synapses, scaled with the reliability of visual stimuli to evoke postsynaptic Ca2+ events. Our study shows astrocyte coverage is highly correlated with structural metrics of synaptic strength of excitatory synapses in the visual cortex and demonstrates a previously unknown relationship between astrocyte coverage and reliable sensory activation.
Behavioral dishonesty in multiscenes: Associations with trait honesty and neural patterns during (dis)honesty video‐watching
Authors: Guo Xiaoli , Yin Lijun 
Published: June 2024

AbstractCross‐situational inconsistency is common in the expression of honesty traits; yet, there is insufficient emphasis on behavioral dishonesty across multiple contexts. The current study aimed to investigate behavioral dishonesty in various contexts and reveal the associations between trait honesty, behavioral dishonesty, and neural patterns of observing others behave honestly or dishonestly in videos (abbr.: (dis)honesty video‐watching). First, the results revealed limitations in using trait honesty to reflect variations in dishonest behaviors and predict behavioral dishonesty. The finding highlights the importance of considering neural patterns in understanding and predicting dishonest behaviors. Second, by comparing the predictive performance of seven types of data across three neural networks, the results showed that functional connectivity in the hypothesis‐driven network during (dis)honesty video‐watching provided the highest predictive power in predicting multitask behavioral dishonesty. Last, by applying the feature elimination method, the midline self‐referential regions (medial prefrontal cortex, posterior cingulate cortex, and anterior cingulate cortex), anterior insula, and striatum were identified as the most informative brain regions in predicting behavioral dishonesty. In summary, the study offered insights into individual differences in deception and the intricate connections among trait honesty, behavioral dishonesty, and neural patterns during (dis)honesty video‐watching.
Enhancing decision‐making in higher education: Exploring the integration of ChatGPT and data visualization tools in data analysis
Authors: Jiang Tristan , Liu Elina , Baig Tasawar , Li Qingrong 
Published: June 2024

AbstractThis chapter explores the potential of integrating conversational AI tools such as ChatGPT with data visualization (DV) tools such as Power BI in higher education settings. A brief history of chatbots is summarized and challenges and opportunities in higher education are outlined. The highlights include AI's prospects for enhancing data‐informed decision‐making while needing safeguards to mitigate risks. Through a pioneering exercise, we integrated ChatGPT's conversational capabilities with Power BI's interface via API and tested functionality. Suggestions for good practice and implications for higher education are discussed.Practical Takeaways
The importance of data security, recognizing current limitations in accuracy, and good practices for integrating DV tools with ChatGPT. The exercise shows that AI tools have the potential to aid higher education leaders, but still face many challenges, and human judgment remains essential.
Integration of ChatGPT with Power BI in higher education aims to enable natural language interaction with data visualizations, facilitating dynamic discussions and insights for leaders.
Considerations include data security protocols, technical challenges, financial constraints, and the potential and limitations of AI. Technical challenges include limitations in ChatGPT's interpretation of complex queries, API performance issues, and data processing inefficiencies.
Affordability of enterprise AI versions poses a barrier to adoption for institutions with budget constraints despite recognizing potential benefits.
ChatGPT represents a significant advancement in AI capabilities but has limitations, including generating inaccurate information and lacking awareness of its limitations. Human oversight and critical evaluation are crucial in navigating the evolving AI landscape and leveraging its benefits effectively while mitigating risks.