bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sex differences in motivational biases over instrumental actions
Authors: Degni Luigi A. E. , Garofalo Sara , Finotti Gianluca , Starita Francesca , Robbins Trevor W. , di Pellegrino Giuseppe 
Published: October 2024

AbstractMotivational (i.e., appetitive or aversive) cues can bias value-based decisions by affecting either direction and intensity of instrumental actions. Despite several findings describing important interindividual differences in these biases, whether biological sex can also play a role is still up to debate. By comparing females and males in both appetitive and aversive Pavlovian-to-Instrumental Transfer paradigms we found that, while motivational cues similarly bias the direction of instrumental actions in both sexes, the intensity of such actions is increased by the cue in male participants only. The present results constitute compelling evidence that a crucial motivational bias of daily actions directed to obtaining rewards or avoiding punishments is modulated by biological sex. This evidence sheds new light on the role of sex in motivational processes that underlie decision-making, highlighting the importance of considering sex as a crucial factor in future research on this topic.
Potent neutralization of SARS-CoV-2 variants by RBD nanoparticle and prefusion-stabilized spike immunogens
Authors: Miranda Marcos C. , Kepl Elizabeth , Navarro Mary Jane , Chen Chengbo , Johnson Max , Sprouse Kaitlin R. , Stewart Cameron , Palser Anne , Valdez Adian , Pettie Deleah , Sydeman Claire , Ogohara Cassandra , Kraft John C. , Pham Minh , Murphy Michael , Wrenn Sam , Fiala Brooke , Ravichandran Rashmi , Ellis Daniel , Carter Lauren , Corti Davide , Kellam Paul , Lee Kelly , Walls Alexandra C. , Veesler David , King Neil P. 
Published: October 2024

AbstractWe previously described a two-component protein nanoparticle vaccine platform that displays 60 copies of the SARS-CoV-2 spike protein RBD (RBD-NP). The vaccine, when adjuvanted with AS03, was shown to elicit robust neutralizing antibody and CD4 T cell responses in Phase I/II clinical trials, met its primary co-endpoints in a Phase III trial, and has been licensed by multiple regulatory authorities under the brand name SKYCovioneTM. Here we characterize the biophysical properties, stability, antigenicity, and immunogenicity of RBD-NP immunogens incorporating mutations from the B.1.351 (β) and P.1 (γ) variants of concern (VOCs) that emerged in 2020. We also show that the RBD-NP platform can be adapted to the Omicron strains BA.5 and XBB.1.5. We compare β and γ variant and E484K point mutant nanoparticle immunogens to the nanoparticle displaying the Wu-1 RBD, as well as to soluble prefusion-stabilized (HexaPro) spike trimers harboring VOC-derived mutations. We find the properties of immunogens based on different SARS-CoV-2 variants can differ substantially, which could affect the viability of variant vaccine development. Introducing stabilizing mutations in the linoleic acid binding site of the RBD-NPs resulted in increased physical stability compared to versions lacking the stabilizing mutations without deleteriously affecting immunogenicity. The RBD-NP immunogens and HexaPro trimers, as well as combinations of VOC-based immunogens, elicited comparable levels of neutralizing antibodies against distinct VOCs. Our results demonstrate that RBD-NP-based vaccines can elicit neutralizing antibody responses against SARS-CoV-2 variants and can be rapidly designed and stabilized, demonstrating the potential of two-component RBD-NPs as a platform for the development of broadly protective coronavirus vaccines.
Emergent actin flows explain distinct modes of gliding motility
Authors: Hueschen Christina L. , Segev-Zarko Li-av , Chen Jian-Hua , LeGros Mark A. , Larabell Carolyn A. , Boothroyd John C. , Phillips Rob , Dunn Alexander R. 
Published: October 2024

AbstractDuring host infection, Toxoplasma gondii and related unicellular parasites move using gliding, which differs fundamentally from other known mechanisms of eukaryotic cell motility. Gliding is thought to be powered by a thin layer of flowing filamentous (F)-actin sandwiched between the plasma membrane and a myosin-covered inner membrane complex. How this surface actin layer drives the various gliding modes observed in experiments—helical, circular, twirling and patch, pendulum or rolling—is unclear. Here we suggest that F-actin flows arise through self-organization and develop a continuum model of emergent F-actin flow within the confines provided by Toxoplasma geometry. In the presence of F-actin turnover, our model predicts the emergence of a steady-state mode in which actin transport is largely directed rearward. Removing F-actin turnover leads to actin patches that recirculate up and down the cell, which we observe experimentally for drug-stabilized actin bundles in live Toxoplasma gondii parasites. These distinct self-organized actin states can account for observed gliding modes, illustrating how different forms of gliding motility can emerge as an intrinsic consequence of the self-organizing properties of F-actin flow in a confined geometry.
Structural basis for Vipp1 membrane binding: from loose coats and carpets to ring and rod assemblies
Authors: Junglas Benedikt , Kartte David , Kutzner Mirka , Hellmann Nadja , Ritter Ilona , Schneider Dirk , Sachse Carsten 
Published: October 2024

AbstractVesicle-inducing protein in plastids 1 (Vipp1) is critical for thylakoid membrane biogenesis and maintenance. Although Vipp1 has recently been identified as a member of the endosomal sorting complexes required for transport III superfamily, it is still unknown how Vipp1 remodels membranes. Here, we present cryo-electron microscopy structures of Synechocystis Vipp1 interacting with membranes: seven structures of helical and stacked-ring assemblies at 5–7-Å resolution engulfing membranes and three carpet structures covering lipid vesicles at ~20-Å resolution using subtomogram averaging. By analyzing ten structures of N-terminally truncated Vipp1, we show that helix α0 is essential for membrane tubulation and forms the membrane-anchoring domain of Vipp1. Lastly, using a conformation-restrained Vipp1 mutant, we reduced the structural plasticity of Vipp1 and determined two structures of Vipp1 at 3.0-Å resolution, resolving the molecular details of membrane-anchoring and intersubunit contacts of helix α0. Our data reveal membrane curvature-dependent structural transitions from carpets to rings and rods, some of which are capable of inducing and/or stabilizing high local membrane curvature triggering membrane fusion.
A Multi-Layer Techno-Economic-Environmental Energy Management Optimization in Cooperative Multi-Microgrids with Demand Response Program and Uncertainties Consideration
Authors: Alamir Nehmedo , Kamel Salah , Megahed Tamer F. , Hori Maiya , Abdelkader Sobhy M. 
Published: October 2024

AbstractThis paper presents a multi-layer, multi-objective (MLMO) optimization model for techno-economic-environmental energy management in cooperative multi-Microgrids (MMGs) that incorporates a Demand Response Program (DRP). The proposed MLMO approach simultaneously optimizes operating costs, MMG operator benefits, environmental emissions, and MMG dependency. This paper proposed a new hybrid ε-lexicography–weighted-sum that eliminates the need to normalize or scalarize objectives. The first layer of the model schedules MMG resources with DRP to minimize operating costs (local generation and power transactions with the utility grid) and maximize MMG profit. The second layer achieves the environmental operation of the MMG, while the third layer maximizes MMG reliability. This paper also proposed a new application of a recently developed enhanced equilibrium optimizer (EEO) for solving the three-layer EM problem. In addition, the uncertainties of solar power generation, wind power generation, load demand, and energy prices are considered based on the probabilistic 2m + 1 Point estimation method (PEM) approach. Three case studies are presented to verify the proposed MLMO approach on an MMG test system. In Case I, a deterministic EM is solved to simulate the MMG as a single layer to minimize costs and maximize benefits through DRP, while Case II solves the MLMO optimization problem. Simulation results show that the proposed MLMO technique reduces environmental emissions by 2.45% and 3.5% in its optimization layer and at the final layer, respectively. The independence index is also enhanced by 2.49% and 4.8% in its layer only and as a total increase, respectively. Case III is for the probabilistic EM simulation; due to the uncertain variables effect, the mean value in this case is increased by about 2.6% over Case I.
Oxidation is a potentially significant methane sink in land-terminating glacial runoff
Authors: Strock Kristin E. , Krewson Rachel B. , Hayes Nicole M. , Deemer Bridget R. 
Published: October 2024

AbstractGlobally, aquatic ecosystems are one of the largest but most uncertain sources of methane, a potent greenhouse gas. It is unclear how climate change will affect methane emissions, but recent work suggests that glacial systems, which are melting faster with climate change, may be an important source of methane to the atmosphere. Currently, studies quantifying glacial emissions are limited in number, and the role of methanotrophy, or microbial methane oxidizers, in reducing atmospheric emissions from source and receiving waters is not well known. Here we discuss three potential sites for methane oxidation that could mitigate emissions from glaciers into the atmosphere: under ice oxidation, oxidation within proglacial lakes, and oxidation within melt rivers. The research presented here increases the number of glacial sites with methane concentration data and is one of only a few studies to quantify the net microbial activity of methane production and oxidation in two types of land-terminating glacial runoff (lake and river). We find that oxidation in a glacial river may reduce atmospheric methane emissions from glacial melt by as much as 53%. Incorporating methane oxidation in estimates of glacial methane emissions may significantly reduce the estimated magnitude of this source in budgeting exercises.
Frequency and spectrum of mutations in human sperm measured using duplex sequencing correlate with trio-based de novo mutation analyses
Authors: Axelsson Jonatan , LeBlanc Danielle , Shojaeisaadi Habiballah , Meier Matthew J , Fitzgerald Devon M. , Nachmanson Daniela , Carlson Jedidiah , Golubeva Alexandra , Higgins Jake , Smith Thomas , Lo Fang Yin , Pilsner Richard , Williams Andrew , Salk Jesse , Marchetti Francesco , Yauk Carole 
Published: October 2024

AbstractDe novo mutations (DNMs) are drivers of genetic disorders. However, the study of DNMs is hampered by technological limitations preventing accurate quantification of ultra-rare mutations. Duplex Sequencing (DS) theoretically has < 1 error/billion base-pairs (bp). To determine the DS utility to quantify and characterize DNMs, we analyzed DNA from blood and spermatozoa from six healthy, 18-year-old Swedish men using the TwinStrand DS mutagenesis panel (48 kb spanning 20 genic and intergenic loci). The mean single nucleotide variant mutation frequency (MF) was 1.2 × 10− 7 per bp in blood and 2.5 × 10− 8 per bp in sperm, with the most common base substitution being C > T. Blood MF and substitution spectrum were similar to those reported in blood cells with an orthogonal method. The sperm MF was in the same order of magnitude and had a strikingly similar spectrum to DNMs from publicly available whole genome sequencing data from human pedigrees (1.2 × 10− 8 per bp). DS revealed much larger numbers of insertions and deletions in sperm over blood, driven by an abundance of putative extra-chromosomal circular DNAs. The study indicates the strong potential of DS to characterize human DNMs to inform factors that contribute to disease susceptibility and heritable genetic risks.
Coupling Taguchi experimental designs with deep adaptive learning enhanced AI process models for experimental cost savings in manufacturing process development
Authors: Zubair Syed Wasim Hassan , Arafat Syed Muhammad , Khan Sarmad Ali , Niazi Sajawal Gul , Rehan Muhammad , Arshad Muhammad Usama , Hayat Nasir , Aized Tauseef , Uddin Ghulam Moeen , Riaz Fahid 
Published: October 2024

AbstractThe Aluminum alloy AA7075 workpiece material is observed under dry finishing turning operation. This work is an investigation reporting promising potential of deep adaptive learning enhanced artificial intelligence process models for L18 (6133) Taguchi orthogonal array experiments and major cost saving potential in machining process optimization. Six different tool inserts are used as categorical parameter along with three continuous operational parameters i.e., depth of cut, feed rate and cutting speed to study the effect of these parameters on workpiece surface roughness and tool life. The data obtained from special L18 (6133) orthogonal array experimental design in dry finishing turning process is used to train AI models. Multi-layer perceptron based artificial neural networks (MLP-ANNs), support vector machines (SVMs) and decision trees are compared for better understanding ability of low resolution experimental design. The AI models can be used with low resolution experimental design to obtain causal relationships between input and output variables. The best performing operational input ranges are identified for output parameters. AI-response surfaces indicate different tool life behavior for alloy based coated tool inserts and non-alloy based coated tool inserts. The AI-Taguchi hybrid modelling and optimization technique helped in achieving 26% of experimental savings (obtaining causal relation with 26% less number of experiments) compared to conventional Taguchi design combined with two screened factors three levels full factorial experimentation.
Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation
Authors: Cui Zixin , Sato Tetsuya , Jackson Austin , Jayarathna Sampath , Itoh Makoto , Yamani Yusuke 
Published: October 2024

AbstractReal-world work environments require operators to perform multiple tasks with continual support from an automated system. Eye movement is often used as a surrogate measure of operator attention, yet conventional summary measures such as percent dwell time do not capture dynamic transitions of attention in complex visual workspace. This study analyzed eye movement data collected in a controlled a MATB-II task environment using gaze transition entropy analysis. In the study, human subjects performed a compensatory tracking task, a system monitoring task, and a communication task concurrently. The results indicate that both gaze transition entropy and stationary gaze entropy, measures of randomness in eye movements, decrease when the compensatory tracking task required more continuous monitoring. The findings imply that gaze transition entropy reflects attention allocation of operators performing dynamic operational tasks consistently.
Bio-inspired feature selection for early diagnosis of Parkinson’s disease through optimization of deep 3D nested learning
Authors: Priyadharshini S. , Ramkumar K. , Vairavasundaram Subramaniyaswamy , Narasimhan K. , Venkatesh S. , Madhavasarma P. , Kotecha Ketan 
Published: October 2024

AbstractParkinson’s disease (PD) is one of the most common neurodegenerative disorders that affect the quality of human life of millions of people throughout the world. The probability of getting affected by this disease increases with age, and it is common among the elderly population. Early detection can help in initiating medications at an earlier stage. It can significantly slow down the progression of this disease, assisting the patient to maintain a good quality of life for a more extended period. Magnetic resonance imaging (MRI)-based brain imaging is an area of active research that is used to diagnose PD disease early and to understand the key biomarkers. The prior research investigations using MRI data mainly focus on volume, structural, and morphological changes in the basal ganglia (BG) region for diagnosing PD. Recently, researchers have emphasized the significance of studying other areas of the human brain for a more comprehensive understanding of PD and also to analyze changes happening in brain tissue. Thus, to perform accurate diagnosis and treatment planning for early identification of PD, this work focuses on learning the onset of PD from images taken from whole-brain MRI using a novel 3D-convolutional neural network (3D-CNN) deep learning architecture. The conventional 3D-Resent deep learning model, after various hyper-parameter tuning and architectural changes, has achieved an accuracy of 90%. In this work, a novel 3D-CNN architecture was developed, and after several ablation studies, the model yielded results with an improved accuracy of 93.4%. Combining features from the 3D-CNN and 3D ResNet models using Canonical Correlation Analysis (CCA) resulted in 95% accuracy. For further enhancements of the model performance, feature fusion with optimization was employed, utilizing various optimization techniques. Whale optimization based on a biologically inspired approach was selected on the basis of a convergence diagram. The performance of this approach is compared to other methods and has given an accuracy of 97%. This work represents a critical advancement in improving PD diagnosis techniques and emphasizing the importance of deep nested 3D learning and bio-inspired feature selection.
Assessment of basic pharmacokinetic parameters of dapagliflozin in TTS formulations in male minipigs
Authors: Paweł Biernat , Balwierz Radosław , Mieszko Dyliński , Mikołaj Kołacki , Micewicz Ewa , Dawid Bursy , Pogorzelec Łukasz 
Published: October 2024

AbstractGiven the extended time over which diabetes treatment is administered, the transdermal delivery system is anticipated to be a more suitable option for older individuals who may experience difficulty swallowing. The continuous delivery of dapagliflozin and more stable plasma levels are anticipated to reduce the incidence of side effects and the frequency of dosing. The objectives of the study were to determine the safety and plasma pharmacokinetics of dapagliflozin in male minipigs following application of the ointment and skin patch. In the initial phase of the study, the potential for transdermal permeation of dapagliflozin from ointment and transdermal patch to blood plasma of 15 male Göttingen minipigs was investigated. In the subsequent phase, the efficacy of utilising patches of varying strengths and sizes was assessed. The LC/MS method was employed to quantify the concentration of the active substance. The transportation of the studied API to the general circulation and accumulation in tissues were confirmed. The maximum drug concentration (122.99 ng/mL) in plasma was observed on the fourth day of application. The highest calculated Cmax was 131.91 ng/mL with a mean AUC0-last of 6620.7 ng h/mL. Following transdermal administration, dapagliflozin is excreted in the urine. The trend between urinary dapagliflozin 3-O-glucuronide levels and urinary glucose excretion was also observed. The transdermal patch has been demonstrated to be an effective drug delivery system for dapagliflozin.
Disease coverage of human genome-wide association studies and pharmaceutical research and development
Authors: Gordillo-Marañón María , Schmidt Amand F. , Warwick Alasdair , Tomlinson Chris , Ytsma Cai , Engmann Jorgen , Torralbo Ana , Maclean Rory , Sofat Reecha , Langenberg Claudia , Shah Anoop D. , Denaxas Spiros , Pirmohamed Munir , Hemingway Harry , Hingorani Aroon D. , Finan Chris 
Published: October 2024

AbstractBackgroundDespite the growing interest in the use of human genomic data for drug target identification and validation, the extent to which the spectrum of human disease has been addressed by genome-wide association studies (GWAS), or by drug development, and the degree to which these efforts overlap remain unclear.MethodsIn this study we harmonize and integrate different data sources to create a sample space of all the human drug targets and diseases and identify points of convergence or divergence of GWAS and drug development efforts.ResultsWe show that only 612 of 11,158 diseases listed in Human Disease Ontology have an approved drug treatment in at least one region of the world. Of the 1414 diseases that are the subject of preclinical or clinical phase drug development, only 666 have been investigated in GWAS. Conversely, of the 1914 human diseases that have been the subject of GWAS, 1121 have yet to be investigated in drug development.ConclusionsWe produce target-disease indication lists to help the pharmaceutical industry to prioritize future drug development efforts based on genetic evidence, academia to prioritize future GWAS for diseases without effective treatments, and both sectors to harness genetic evidence to expand the indications for licensed drugs or to identify repurposing opportunities for clinical candidates that failed in their originally intended indication.
Word Learning in Arabic Diglossia in Children With Typical Language Development and Developmental Language Disorder
Authors: Ghawi-Dakwar Ola , Saiegh-Haddad Elinor 
Published: October 2024

Purpose:
Word learning requires the creation of phonological and semantic representations and links in long-term memory. Phonological distance of a given word from the spoken language affects children's lexical–phonological representations and processing. The study investigates the role of the phonological distance of Modern Standard Arabic (StA) words from the child's Spoken Arabic (SpA) vernacular in word learning in Arabic diglossia. It also examines whether, given their vulnerable phonological skills, children with developmental language disorder (DLD) show a stronger impact of phonological distance on word learning than children with typical language development (TLD).

Method:
One hundred children with TLD and DLD in kindergarten and in first grade (25 per group) were tested on comprehension and production word-learning probes manipulating phonological distance. Learning monosyllabic and disyllabic nonwords encoding only SpA phonemes was compared with the learning of parallel nonwords encoding one unique StA consonant each.

Results:
Repeated-measures analyses of variance showed higher word learning scores in children with TLD on both probes and for both syllable lengths. Moreover, all children fared significantly lower, in both comprehension and production probes, when the target stimulus was phonologically distant from the spoken language. Finally, an interaction effect was observed on the production probes, revealing differences in the developmental dynamics of phonological distance effects between the groups: Phonological distance hindered word learning among children with TLD in kindergarten, but among children with DLD in the first grade.

Conclusions:
The results support the role of phonological distance as a phonological complexity factor in word learning in Arabic diglossia. Furthermore, they show that the effect of phonological distance is complex and it interacts with modality, language aptitude, and grade level. The theoretical and clinical implications are discussed.