bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synchronous motion of the Easter mantle plume and the East Pacific Rise
Authors: O’Connor John M. , Regelous Marcel , Haase Karsten M. , Hemond Christophe , Koppers Anthony A. P. , Miggins Daniel P. , Heaton Daniel E. 
Published: November 2024

AbstractThe Easter mantle plume has produced one of the longest hotspot tracks in the Pacific Ocean. While previous studies have focused on the eastern side extending across the Nazca Plate, we use 40Ar/39Ar isotopic and geochemical data to investigate the less explored western side around the Easter Microplate. We propose a dynamic model in which a deeper (600 km-depth), less buoyant mantle exerts a westward force on the East Pacific Rise (EPR), while a more buoyant plume region drives Easter hotspot volcanism and a localised acceleration in seafloor spreading. Our findings suggest that the Easter hotspot is the more focused surface expression of the most buoyant region of a vast, deep-seated mantle plume extending from the Pacific Large Low Shear Velocity Province (LLSVP). This challenges the traditional view of hotspots as isolated phenomena and suggests they are part of broader LLSVP-related mantle structures. Our results imply a more intricate, large-scale relationship between hotspots, mantle plumes, spreading ridges, and mantle dynamics.
A self-eliminating allelic-drive reverses insecticide resistance in Drosophila leaving no transgene in the population
Authors: Auradkar Ankush , Corder Rodrigo M. , Marshall John M. , Bier Ethan 
Published: November 2024

AbstractInsecticide resistance (IR) poses a significant global challenge to public health and welfare. Here, we develop a locally-acting unitary self-eliminating allelic-drive system, inserted into the Drosophila melanogaster yellow (y) locus. The drive cassette encodes both Cas9 and a single gRNA to bias inheritance of the favored wild-type (1014 L) allele over the IR (1014 F) variant of the voltage-gated sodium ion channel (vgsc) target locus. When enduring a fitness cost, this transiently-acting drive can increase the frequency of the wild-type allele to 100%, depending on its seeding ratio, before being eliminated from the population. However, in a fitness-neutral “hover” mode, the drive maintains a constant frequency in the population, completely converting IR alleles to wild-type, even at low initial seeding ratios.
Breaking the capacity bottleneck of lithium-oxygen batteries through reconceptualizing transport and nucleation kinetics
Authors: Zhang Zhuojun , Xiao Xu , Yan Aijing , Sun Kai , Yu Jianwen , Tan Peng 
Published: November 2024

AbstractThe practical capacity of lithium-oxygen batteries falls short of their ultra-high theoretical value. Unfortunately, the fundamental understanding and enhanced design remain lacking, as the issue is complicated by the coupling processes between Li2O2 nucleation, growth, and multi-species transport. Herein, we redefine the relationship between the microscale Li2O2 behaviors and the macroscopic electrochemical performance, emphasizing the importance of the inherent modulating ability of Li+ ions through a synergy of visualization techniques and cross-scale quantification. We find that Li2O2 particle distributed against the oxygen gradient signifies a compatibility match for the nucleation and transport kinetics, thus enabling the output of the electrode’s maximum capacity and providing a basis for evaluating operating protocols for future applications. In this case, a 150% capacity enhancement is further achieved through the development of a universalizing methodology. This work opens the door for the rules and control of energy conversion in metal-air batteries, greatly accelerating their path to commercialization.
Deep phenotypic profiling of neuroactive drugs in larval zebrafish
Authors: Gendelev Leo , Taylor Jack , Myers-Turnbull Douglas , Chen Steven , McCarroll Matthew N. , Arkin Michelle R. , Kokel David , Keiser Michael J. 
Published: November 2024

AbstractBehavioral larval zebrafish screens leverage a high-throughput small molecule discovery format to find neuroactive molecules relevant to mammalian physiology. We screen a library of 650 central nervous system active compounds in high replicate to train deep metric learning models on zebrafish behavioral profiles. The machine learning initially exploited subtle artifacts in the phenotypic screen, necessitating a complete experimental re-run with rigorous physical well-wise randomization. These large matched phenotypic screening datasets (initial and well-randomized) provide a unique opportunity to quantify and understand shortcut learning in a full-scale, real-world drug discovery dataset. The final deep metric learning model substantially outperforms correlation distance–the canonical way of computing distances between profiles–and generalizes to an orthogonal dataset of diverse drug-like compounds. We validate predictions by prospective in vitro radio-ligand binding assays against human protein targets, achieving a hit rate of 58% despite crossing species and chemical scaffold boundaries. These neuroactive compounds exhibit diverse chemical scaffolds, demonstrating that zebrafish phenotypic screens combined with metric learning achieve robust scaffold hopping capabilities.
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows
Authors: Menon Sarath , Lysogorskiy Yury , Knoll Alexander L. M. , Leimeroth Niklas , Poul Marvin , Qamar Minaam , Janssen Jan , Mrovec Matous , Rohrer Jochen , Albe Karsten , Behler Jörg , Drautz Ralf , Neugebauer Jörg 
Published: November 2024

AbstractWe present a comprehensive and user-friendly framework built upon the integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.
Best practice advice for asthma exacerbation prevention and management in primary care: an international expert consensus
Authors: Skolnik Neil , Yawn Barbara P. , Correia de Sousa Jaime , Vázquez María Mar Martínez , Barnard Amanda , Wright Wendy L. , Ulrich Austin , Winders Tonya , Brunton Stephen 
Published: November 2024

AbstractPrimary care clinicians play a key role in asthma and asthma exacerbation management worldwide because most patients with asthma are treated in primary care settings. The high burden of asthma exacerbations persists and important practice gaps remain, despite continual advances in asthma care. Lack of primary care-specific guidance, uncontrolled asthma, incomplete assessment of exacerbation and asthma control history, and reliance on systemic corticosteroids or short-acting beta2-agonist-only therapy are challenges clinicians face today with asthma care. Evidence supports the use of inhaled corticosteroids (ICS) + fast-acting bronchodilator treatments when used as needed in response to symptoms to improve asthma control and reduce rates of exacerbations, and the symptoms that occur leading up to an asthma exacerbation provide a window of opportunity to intervene with ICS. Incorporating patient perspectives and preferences when designing asthma regimens will help patients be more engaged in their therapy and may contribute to improved adherence and outcomes. This expert consensus contains 10 Best Practice Advice Points from a panel of primary care clinicians and a patient representative, formed in collaboration with the International Primary Care Respiratory Group (IPCRG), a clinically led charitable organization that works locally and globally in primary care to improve respiratory health. The panel met virtually and developed a series of best practice statements, which were drafted and subsequently voted on to obtain consensus. Primary care clinicians globally are encouraged to review and adapt these best practice advice points on preventing and managing asthma exacerbations to their local practice patterns to enhance asthma care within their practice.
Phase 1 clinical trial of Hantaan and Puumala virus DNA vaccines delivered by needle-free injection
Authors: Hooper Jay W. , Kwilas Steven A. , Josleyn Matthew , Norris Sarah , Hutter Jack N. , Hamer Melinda , Livezey Jeffrey , Paolino Kristopher , Twomey Patrick , Koren Michael , Keiser Paul , Moon James E. , Nwaeze Ugo , Koontz Jason , Ledesma-Feliciano Carmen , Landry Nathalie , Wellington Trevor 
Published: November 2024

AbstractHantaan virus (HTNV) and Puumala virus (PUUV) are pathogenic zoonoses found in Asia and Europe, respectively. We conducted a randomized Phase 1 clinical trial of individual HTNV and PUUV DNA vaccines targeting the envelope glycoproteins (GnGc), as well as a combined HTNV/PUUV DNA vaccine delivered at varying doses using the PharmaJet Stratis® needle-free injection system (NCT02776761). Cohort 1 and 2 vaccines consisted of 2 mg/vaccination of HTNV or PUUV plasmid, respectively. Cohort 3 vaccine consisted of 2 mg/vaccination of 1:1 mixture of HTNV and PUUV vaccines. Vaccinations were administered on Days 0, 28, 56, and 168. The vaccines were safe and well tolerated. Neutralizing antibody responses were elicited in 7/7 (100%) subjects who received the HTNV DNA (Cohort 1) and 6/6 (100%) subjects who received the PUUV DNA (Cohort 2) vaccines alone. The combination vaccine resulted in 4/9 (44%) seroconversion against both viruses. After the first two vaccinations, the seroconversion rates for the HTNV and PUUV vaccines were >80%.
Optimum solution of power flow problem based on search and rescue algorithm
Authors: Houssein Essam H. , Ismaeel Alaa A. K. , Said Mokhtar 
Published: November 2024

AbstractIn order to solve the optimal power flow (OPF) problem, a unique algorithm based on a search and rescue method is applied in this study. For the OPF problem under three objective functions, the SAR offers a straightforward and reliable solution. The three objective functions are used to minimize the fuel cost, power loss and voltage deviation as a single objective function. The OPF problem for benchmark test system, including the IEEE-14 bus, IEEE-30 bus, and IEEE-57 bus, are solved by the Search and Rescue algorithm (SAR) under specific objective functions that are determined by the operational and economic performance indices of the power system. To demonstrate the efficacy and possibilities of the SAR algorithm, SAR is contrasted with alternative optimization techniques such as harmony search algorithm, gradient method, adaptive genetic algorithm, biogeography-based optimization, Artificial bee colony, gravitational search algorithm, particle swarm optimization, Jaya algorithm, enhanced genetic algorithm, modified shuffle frog leaping algorithm, practical swarm optimizer, Moth flam optimizer, whale and moth flam optimizer, grey wolf optimizer, cheap optimization algorithm and differential evolution algorithm. The value of minimum power losses based on SAR technique is equal to 0.459733441487247 MW for IEEE-14 bus. The value of minimum total fuel cost based on SAR technique is equal to 8051.12225602148 $/h for IEEE-14 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0357680148269292 for IEEE-14 bus. The value of minimum power losses based on SAR technique is equal to 2.71286428848434 MW for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 798.197578585806 $/h for IEEE-30 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0978069572088536 for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 38017.7691758245 $/h for IEEE-57 bus. The acquired results for the OPF compared to all competitor algorithms in every case of fitness function demonstrate the superiority of the SAR method.
Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device
Authors: Kanchan Mithun , Tambe Prasad Kisan , Bharati Sanjay , Powar Omkar S 
Published: November 2024

AbstractDetecting glucose levels is crucial for diabetes patients as it enables timely and effective management, preventing complications and promoting overall health. In this endeavor, we have designed a novel, affordable point-of-care diagnostic device utilizing microfluidic principles, a smartphone camera, and established laboratory colorimetric methods for accurate glucose estimation. Our proposed microfluidic device comprises layers of adhesive poly-vinyl films stacked on a poly methyl methacrylate (PMMA) base sheet, with micro-channel contours precision-cut using a cutting printer. Employing the gold standard glucose-oxidase/peroxidase reaction on this microfluidic platform, we achieve enzymatic glucose determination. The resulting colored complex, formed by phenol and 4-aminoantipyrine in the presence of hydrogen peroxide generated during glucose oxidation, is captured at various glucose concentrations using a smartphone camera. Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 95% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms. Furthermore, the classifier exhibits outstanding precision, recall, and F1 score of 94%, 93%, and 93%, respectively, as validated through our study, showcasing its exceptional predictive capability. Next, a user-friendly smartphone application named “GLUCOLENS AI” was developed to capture images, perform image processing, and communicate with cloud server containing the CNN classifier. The developed CNN model can be successfully used as a pre-trained model for future glucose concentration predictions.
A sustainable HPTLC approach for green assessment of Tyrosine to phenylalanine ratio in chronic kidney disease
Authors: Kamel Rania M. , Abdel-aal Fatma A. M. , Osman Mostafa M. , Mohamed Fardous A. , Mohamed Abdel-Maaboud I. 
Published: November 2024

AbstractThe kidney is considered the main site for the net release of Tyrosine (Tyr) to the circulation through hydroxylation of Phenylalanine (Phe) using phenylalanine hydroxylase enzyme. In chronic kidney disease (CKD) patients the enzyme activity is impaired, therefore the serum concentration ratio of Tyr/Phe is reduced compared to healthy individuals. This motivates us to develop a cost effective, green, simple, sensitive, and selective HPTLC method to measure the levels of Tyr and Phe in serum samples. The analysis was carried out using acetonitrile: ethanol: 25% ammonia solution: ethyl acetate (6.5: 1.5: 1: 0.5) as a mobile phase. Rf values were 0.55 ± 0.05 for Phe and 0.39 ± 0.05 for Tyr. Densitometry scanning was performed using UV detector and dual wavelengths of 210 and 225 nm were obtained. A linear correlation was observed between the levels of Phe and Tyr, ranging from 50 to 700 ng band− 1 and 50 to 600 ng band− 1, respectively, under the optimum conditions. The method selectivity, linearity, precision, accuracy, and robustness were all confirmed in accordance with ICH recommendations. Calculations of the separation and resolution factors, number of theoretical plates, and height equivalent to theoretical plates prove to the chromatographic system accuracy and high separation efficiency. The developed method exhibits an acceptable eco-scale when measuring the method greenness using AGREE and GAPI softwares. It was applied for the determination of Phe and Tyr concentrations in human serum samples.
Cost-effectiveness analysis of mHealth applications for depression in Germany using a Markov cohort simulation
Authors: Freitag Bettina , Uncovska Marie , Meister Sven , Prinz Christian , Fehring Leonard 
Published: November 2024

AbstractRegulated mobile health applications are called digital health applications (“DiGA”) in Germany. To qualify for reimbursement by statutory health insurance companies, DiGA have to prove positive care effects in scientific studies. Since the empirical exploration of DiGA cost-effectiveness remains largely uncharted, this study pioneers the methodology of cohort-based state-transition Markov models to evaluate DiGA for depression. As health states, we define mild, moderate, severe depression, remission and death. Comparing a future scenario where 50% of patients receive supplementary DiGA access with the current standard of care reveals a gain of 0.02 quality-adjusted life years (QALYs) per patient, which comes at additional direct costs of ~1536 EUR per patient over a five-year timeframe. Influencing factors determining DiGA cost-effectiveness are the DiGA cost structure and individual DiGA effectiveness. Under Germany’s existing cost structure, DiGA for depression are yet to demonstrate the ability to generate overall savings in healthcare expenditures.
Multiomics biomarkers were not superior to clinical variables for pan-cancer screening
Authors: Smelik Martin , Zhao Yelin , Mansour Aly Dina , Mahmud AKM Firoj , Sysoev Oleg , Li Xinxiu , Benson Mikael 
Published: November 2024

Abstract
Background
Cancer screening tests are considered pivotal for early diagnosis and survival. However, the efficacy of these tests for improving survival has recently been questioned. This study aims to test if cancer screening could be improved by biomarkers in peripheral blood based on multi-omics data.

Methods
We utilize multi-omics data from 500,000 participants in the UK Biobank. Machine learning is applied to search for proteins, metabolites, genetic variants, or clinical variables to diagnose cancers collectively and individually.

Results
Here we show that the overall performance of the potential blood biomarkers do not outperform clinical variables for collective diagnosis. However, we observe promising results for individual cancers in close proximity to peripheral blood, with an Area Under the Curve (AUC) greater than 0.8.

Conclusions
Our findings suggest that the identification of blood biomarkers for cancer might be complicated by variable overlap between molecular changes in tumor tissues and peripheral blood. This explanation is supported by local proteomics analyses of different tumors, which all show high AUCs, greater than 0.9. Thus, multi-omics biomarkers for the diagnosis of individual cancers may potentially be effective, but not for groups of cancers.
Caregiver policies in the United States: a systematic review
Authors: Green Makenna R. , Hughes M. Courtney , Afrin Sadia , Vernon Erin 
Published: November 2024

Abstract
In the United States, there are nearly 53 million informal or unpaid caregivers, many of whom experience mental and physical stress related to their caregiving duties and increased financial responsibility. We identified federal and state informal caregiver support policies authorized by specific legislation along with their key provisions and conducted a systematic review of the academic literature related to quantitative evaluations of these policies. Twenty policies, eight academic studies, and four gray literature reports were included in the study, with half of the policies introduced since 2000. Our study criteria yielded few academic valuations tied to caregiver policies and few policies including research provisions. Of the provision areas identified in policies, respite services, caregiver training, and workplace protections appeared the most. Future policies and the studies examining them should incorporate cost outcomes and equity as focus areas and disaggregate data by vulnerable groups to ensure value and equity in caregiver support legislation.

Key messages

Increased legislation to support informal caregivers may be warranted. The limited academic research examining existing caregiver policies identifies mixed outcomes for caregivers.

Prioritizing vulnerable populations in such policy research examining outcomes could help improve caregiver support efforts.

The included studies investigated the outcomes of three policies and identified more negative than positive outcomes for caregivers.
Performance analysis of position sensorless control of antagonistic shape memory alloy actuators
Authors: Bai Fengqiang , Zhang Xiangjun , Xu Dianguo 
Published: November 2024

Abstract
This paper presents a comprehensive performance analysis of position sensorless control of antagonistic shape memory alloy actuators (ASMAA), focusing on enhancing the sensorless position sensing capabilities and servo performance. The mechanisms and influencing factors of ASMAA self-sensing characteristics are comprehensively analyzed. A self-sensing model framework based on the constitutive model of ASMAA is proposed, and a self-sensing model based on differential resistance feedback is developed. An experimental platform based on the ASMAA motion mechanism is established to test and analyze SMA wires of two different materials under various pretension force conditions. The experimental results show that the antagonistic configuration mitigates hysteresis and improves linearity. A simple polynomial fitting is employed to establish the resistance-displacement relationship, achieving good accuracy with minimal residuals. The system identification is employed to avoid the parameter complexity and time-varying in the constitutive model. Based on the identified transfer function, a traditional PID controller is designed for position sensorless servo control. However, the inherent nonlinear hysteresis of ASMAA is difficult to suppress using a linear PID controller. Furthermore, a compound control strategy based on the Duhem model and PID controller is proposed, which utilizes the particle swarm optimization algorithm to identify the Duhem inverse model controller. Experimental results demonstrate that the compound controller significantly enhances position tracking accuracy and response speed compared to a standalone PID controller.
All-in-one, flexible, diversified self-powered sensors through embedded 3D printing
Authors: Xian Shuai , Wu Zhenfeng , Li Yixin , Wu Zhigang , Zhong Yong 
Published: November 2024

Abstract
Triboelectric nanogenerators are characterized by zero power consumption and are often employed as self-powered sensors. However, the complex manufacturing process and expensive equipment limit the further promotion and application of self-powered sensors, which have become urgent challenges in this field. Here, a simple strategy using embedded three-dimensional (EMB 3D) printing is proposed to enable the fabrication of diverse self-powered sensors in one-step, reducing production costs while increasing design flexibility. Specifically, the designed sensors composed of the silicone as the triboelectric layer and silicone/multi-walled carbon nanotubes as flexible electrodes with excellent all-in-one structures. Meanwhile, diversified self-powered sensors with different complex structures (e.g. planar array sensors and gyroid-structures sensors) were developed to meet the diverse needs of different applications, verifying the capability of the proposed embedded 3D printing method to design and customize sensors with various shapes and structures. In addition, the applications of these functionalized self-powered sensors in cryptographic simulation, pressure position detection, and impact force recognition have been successfully demonstrated. Therefore, this self-powered sensor based on EMB 3D printing has a promising future in human-computer interaction, collision detection and other fields.