bluuebunny/crossref_metadata_2025_split huggingface dataset:
Light-gated integrator for highlighting kinase activity in living cells
Authors: Lin Wei , Phatarphekar Abhishek , Zhong Yanghao , Liu Longwei , Kwon Hyung-Bae , Gerwick William H. , Wang Yingxiao , Mehta Sohum , Zhang Jin 
Published: September 2024

AbstractProtein kinases are key signaling nodes that regulate fundamental biological and disease processes. Illuminating kinase signaling from multiple angles can provide deeper insights into disease mechanisms and improve therapeutic targeting. While fluorescent biosensors are powerful tools for visualizing live-cell kinase activity dynamics in real time, new molecular tools are needed that enable recording of transient signaling activities for post hoc analysis and targeted manipulation. Here, we develop a light-gated kinase activity coupled transcriptional integrator (KINACT) that converts dynamic kinase signals into “permanent” fluorescent marks. KINACT enables robust monitoring of kinase activity across scales, accurately recording subcellular PKA activity, highlighting PKA activity distribution in 3D cultures, and identifying PKA activators and inhibitors in high-throughput screens. We further leverage the ability of KINACT to drive signaling effector expression to allow feedback manipulation of the balance of GαsR201C-induced PKA and ERK activation and dissect the mechanisms of oncogenic G protein signaling.
Divergent mechanisms of steroid inhibition in the human ρ1 GABAA receptor
Authors: Fan Chen , Cowgill John , Howard Rebecca J. , Lindahl Erik 
Published: September 2024

Abstractρ-type γ-aminobutyric acid-A (GABAA) receptors are widely distributed in the retina and brain, and are potential drug targets for the treatment of visual, sleep and cognitive disorders. Endogenous neuroactive steroids including β-estradiol and pregnenolone sulfate negatively modulate the function of ρ1 GABAA receptors, but their inhibitory mechanisms are not clear. By combining five cryo-EM structures with electrophysiology and molecular dynamics simulations, we characterize binding sites and negative modulation mechanisms of β-estradiol and pregnenolone sulfate at the human ρ1 GABAA receptor. β-estradiol binds in a pocket at the interface between extracellular and transmembrane domains, apparently specific to the ρ subfamily, and disturbs allosteric conformational transitions linking GABA binding to pore opening. In contrast, pregnenolone sulfate binds inside the pore to block ion permeation, with a preference for activated structures. These results illuminate contrasting mechanisms of ρ1 inhibition by two different neuroactive steroids, with potential implications for subtype-specific gating and pharmacological design.
Experimental evolution of yeast shows that public-goods upregulation can evolve despite challenges from exploitative non-producers
Authors: Lindsay Richard J. , Holder Philippa J. , Hewlett Mark , Gudelj Ivana 
Published: September 2024

AbstractMicrobial secretions, such as metabolic enzymes, are often considered to be cooperative public goods as they are costly to produce but can be exploited by others. They create incentives for the evolution of non-producers, which can drive producer and population productivity declines. In response, producers can adjust production levels. Past studies suggest that while producers lower production to reduce costs and exploitation opportunities when under strong selection pressure from non-producers, they overproduce secretions when these pressures are weak. We challenge the universality of this trend with the production of a metabolic enzyme, invertase, by Saccharomyces cerevisiae, which catalyses sucrose hydrolysis into two hexose molecules. Contrary to past studies, overproducers evolve during evolutionary experiments even when under strong selection pressure from non-producers. Phenotypic and competition assays with a collection of synthetic strains - engineered to have modified metabolic attributes - identify two mechanisms for suppressing the benefits of invertase to those who exploit it. Invertase overproduction increases extracellular hexose concentrations that suppresses the metabolic efficiency of competitors, due to the rate-efficiency trade-off, and also enhances overproducers’ hexose capture rate by inducing transporter expression. Thus, overproducers are maintained in the environment originally thought to not support public goods production.
Practical feature filter strategy to machine learning for small datasets in chemistry
Authors: Hu Yang , Sandt Roland , Spatschek Robert 
Published: September 2024

AbstractMany potential use cases for machine learning in chemistry and materials science suffer from small dataset sizes, which demands special care for the model design in order to deliver reliable predictions. Hence, feature selection as the key determinant for dataset design is essential here. We propose a practical and efficient feature filter strategy to determine the best input feature candidates. We illustrate this strategy for the prediction of adsorption energies based on a public dataset and sublimation enthalpies using an in-house training dataset. The input of adsorption energies reduces the feature space from 12 dimensions to two and still delivers accurate results. For the sublimation enthalpies, three input configurations are filtered from 14 possible configurations with different dimensions for further productive predictions as being most relevant by using our feature filter strategy. The best extreme gradient boosting regression model possesses a good performance and is evaluated from statistical and theoretical perspectives, reaching a level of accuracy comparable to density functional theory computations and allowing for physical interpretations of the predictions. Overall, the results indicate that the feature filter strategy can help interdisciplinary scientists without rich professional AI knowledge and limited computational resources to establish a reliable small training dataset first, which may make the final machine learning model training easier and more accurate, avoiding time-consuming hyperparameter explorations and improper feature selection.
Uptake of substances into living mammalian cells by microwave induced perturbation of the plasma membrane
Authors: Milden-Appel Manuela , Paravicini Markus , Milden Jannick P. , Schüßler Martin , Jakoby Rolf , Cardoso M. Cristina 
Published: September 2024

AbstractDelivering foreign molecules and genetic material into cells is a crucial process in life sciences and biotechnology, resulting in great interest in effective cell transfection methods. Importantly, physical transfection methods allow delivery of molecules of different chemical composition and are, thus, very flexible. Here, we investigated the influence of microwave radiation on the transfection and survival of mammalian cells. We made use of an optimized microwave-poration device and analyzed its performance (frequency and electric field strength) in comparison with simulations. We, then, tested the effect of microwave irradiation on cells and found that 18 GHz had the least impact on cell survival, viability, cell division and genotoxicity while 10 GHz drastically impacted cell physiology. Using live-cell fluorescence microscopy and image analysis, we tested the uptake of small chemical substances, which was most efficient at 18 GHz and correlated with electric field strength and frequency. Finally, we were able to obtain cellular uptake of molecules of very different chemical composition and sizes up to whole immunoglobulin antibodies. In conclusion, microwave-induced poration enables the uptake of widely different substances directly into mammalian cells growing as adherent cultures and with low physiological impact.
Observational evidence reveals the significance of nocturnal chemistry in seasonal secondary organic aerosol formation
Authors: Liu Lu , Hohaus Thorsten , Franke Philipp , Lange Anne C. , Tillmann Ralf , Fuchs Hendrik , Tan Zhaofeng , Rohrer Franz , Karydis Vlassis , He Quanfu , Vardhan Vaishali , Andres Stefanie , Bohn Birger , Holland Frank , Winter Benjamin , Wedel Sergej , Novelli Anna , Hofzumahaus Andreas , Wahner Andreas , Kiendler-Scharr Astrid 
Published: September 2024

AbstractOxidized Organic Aerosol (OOA), a major component of fine atmospheric particles, impacts climate and human health. Previous experiments and atmospheric models emphasize the importance of nocturnal OOA formation from NO3· oxidation of biogenic VOCs. This seasonal study extends the understanding by showing that nocturnal oxidation of biomass-burning emissions can account for up to half of total OOA production in fall and winter. It is the first to distinguish nocturnal OOA characteristics from daytime OOA across all seasons using bulk aerosol measurements. Summer observations of nocturnal OOA align well with regional chemistry transport model predictions, but discrepancies in other seasons reveal a common model deficiency in representing biomass-burning emissions and their nocturnal oxidation. This study underscores the significance of near-ground nocturnal OOA production, proposes a method to differentiate it using bulk aerosol measurements, and suggests model optimization strategies. These findings enhance the understanding and prediction of nighttime OOA formation.
Readout error mitigated quantum state tomography tested on superconducting qubits
Authors: Aasen Adrian Skasberg , Di Giovanni Andras , Rotzinger Hannes , Ustinov Alexey V. , Gärttner Martin 
Published: September 2024

AbstractQuantum technologies rely heavily on accurate control and reliable readout of quantum systems. Current experiments are limited by numerous sources of noise that can only be partially captured by simple analytical models and additional characterization of the noise sources is required. We test the ability of readout error mitigation to correct noise found in systems composed of quantum two-level objects (qubits). To probe the limit of such methods, we designed a beyond-classical readout error mitigation protocol based on quantum state tomography (QST), which estimates the density matrix of a quantum system, and quantum detector tomography (QDT), which characterizes the measurement procedure. By treating readout error mitigation in the context of state tomography the method becomes largely readout mode-, architecture-, noise source-, and quantum state-independent. We implement this method on a superconducting qubit and evaluate the increase in reconstruction fidelity for QST. We characterize the performance of the method by varying important noise sources, such as suboptimal readout signal amplification, insufficient resonator photon population, off-resonant qubit drive, and effectively shortened T1 and T2 coherence. As a result, we identified noise sources for which readout error mitigation worked well, and observed decreases in readout infidelity by a factor of up to 30.
The influence of AHR on immune and tissue biology
Authors: Stockinger Brigitta , Diaz Oscar E , Wincent Emma 
Published: September 2024

AbstractThe aryl hydrocarbon receptor is a ligand dependent transcription factor which functions as an environmental sensor. Originally discovered as the sensor for man made pollutants such as 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) it has recently gained prominence as an important mediator for environmental triggers via the diet or microbiota which influences many physiological functions in different cell types and tissues across the body. Notably AHR activity contributes to prevent excessive inflammation following tissue damage in barrier organs such as skin, lung or gut which has received wide attention in the past decade. In this review we will focus on emerging common AHR functions across cell types and tissues and discuss ongoing issues that confound the understanding of AHR physiology. Furthermore, we will discuss the need for deeper molecular understanding of the functional activity of AHR in different contexts with respect to development of potential therapeutic applications.
Supporting Participation Through Project-Based Intervention: A Tutorial for Working With People With Aphasia in Individual Sessions
Authors: Sather Thomas W. , Strong Katie A. 
Published: September 2024

Purpose:
Participation is an integral focus of services to people with aphasia and is considered best practice within the scope of practice for speech-language pathology. The Life Participation Approach to Aphasia encourages meaningful participation in life for people living with aphasia. In theory, providing participation-based services to people with aphasia seems logical; however, embedding these constructs of participation into practice can be challenging for speech-language pathologists (SLPs). Project-based intervention (PBI) provides an authentic opportunity to target participation and support identity reformulation in aphasia intervention. Historically, projects have been provided primarily in group-based settings, which may be difficult for the majority of SLPs who primarily offer individual sessions due to reimbursement. This tutorial provides a framework for using PBI in individual sessions for clients with aphasia using five evidence-based components: (a) shared decision making, (b) patient-reported outcomes, (c) goal setting, (d) the project, and (e) ongoing evaluation. Evidence-based tools and a case example are provided to support each component.

Conclusions:
Projects and PBI provide tangible means of placing participation at the center of intervention while also providing opportunities to target language impairments, identity reconstruction, and various environments in a meaningful and personalized way. Projects can be scaled to the client's needs and abilities as well as to the constraints and options of the service delivery setting.
Multi‐modal fusion attention sentiment analysis for mixed sentiment classification
Authors: Xue Zhuanglin , Xu Jiabin 
Published: September 2024

AbstractMixed sentiment classification (MSC) technology has a significant research value and application potential in understanding and analysing sentimental interactions. In the process of identifying and analysing complex sentiments, it is still necessary to overcome the difficulties of multi‐dimensional sentiment recognition and improve sensitivity to subtle sentimental differences. Therefore, a multi‐modal fusion attention sentiment analysis based on MSC to address this challenge is proposed. Firstly, the sentiment analysis fusion strategy based on multi‐modal fusion is studied, which can fully utilise the information of multi‐modal inputs such as text, audio, and video, thereby gaining a more comprehensive understanding and recognition of sentiments. Secondly, a sentiment analysis model based on multi‐modal fusion attention is constructed, which focuses on the key information of multi‐modal inputs to achieve an accurate recognition of mixed sentiments. The experimental results show that the proposed method outperforms existing sentiment analysis methods on both datasets, with F1 values of 83.17 and 84.19, accuracy of 39.15 and 39.98, and errors of 0.516 and 0.524, respectively. The accuracy range is 95.38%–99.89%, verifying the superiority of the method in sentiment analysis. It can be seen that this method provides a more effective and reliable MSC solution, which has practical significance for improving the accuracy and recall of sentiment analysis.
A coordinated scheduling optimization method for integrated energy systems with data centres based on deep reinforcement learning
Authors: Sun Yi , Ding Yiyuan , Chen Minghao , Zhang Xudong , Tao Peng , Guo Wei 
Published: September 2024

AbstractAs an emerging multi‐energy consumption subject, data centres (DCs) are bound to become crucial energy users for integrated energy systems (IES). Therefore, how to fully tap the potential of the collaborative operation between DCs and IES to improve total energy efficiency and economic performance is becoming a pressing need. In this article, the authors research an optimization coordinated by the energy scheduling and information service provision within the scenario of an integrated energy system with a data centre (IES‐DC). The mathematical model of IES‐DC is first established to reveal the energy conversion process of the electricity‐heat‐gas IES and the DC's energy consumption affected by the scale of active IT equipment. For dynamical providing multi‐energy and computing service by coordinating scheduling energy and information equipment, the formulations of IES‐DC scheduling, which is described as a Markov decision process (MDP), are presented, and it is solved by introducing the twin‐delayed deep deterministic policy gradient (TD3), which is a model‐free deep reinforcement learning (DRL) algorithm. Finally, the numerical studies show that compared with benchmarks, the proposed method based on the TD3 algorithm can effectively control the operation of energy conversion equipment and the number of active servers in IES‐DC.
Cervical‐YOSA: Utilizing prompt engineering and pre‐trained large‐scale models for automated segmentation of multi‐sequence MRI images in cervical cancer
Authors: Xia Yanwei , Ou Zhengjie , Tan Lihua , Liu Qiang , Cui Yanfen , Teng Da , Zhao Dan 
Published: September 2024

AbstractCervical cancer is a major health concern, particularly in developing countries with limited medical resources. This study introduces two models aimed at improving cervical tumor segmentation: a semi‐automatic model that fine‐tunes the Segment Anything Model (SAM) and a fully automated model designed for efficiency. Evaluations were conducted using a dataset of 8586 magnetic resonance imaging (MRI) slices, where the semi‐automatic model achieved a Dice Similarity Coefficient (DSC) of 0.9097, demonstrating high accuracy. The fully automated model also performed robustly with a DSC of 0.8526, outperforming existing methods. These models offer significant potential to enhance cervical cancer diagnosis and treatment, especially in resource‐limited settings.
Maximum torque per ampere control of permanent magnet reluctance hybrid rotor dual stator synchronous motor based on sliding mode speed controller
Authors: Jin Wuhen , Jin Shi , Yu Siyang , Wang Hao , Zhang Zhaoyu 
Published: September 2024

AbstractThis paper proposes a maximum torque per ampere (MTPA) control method for a permanent magnet reluctance hybrid rotor dual stator synchronous motor (PMRHRDSSM) based on a sliding mode speed controller, in response to the complex and special electromagnetic relationship of the motor. The paper focuses on the special electromagnetic relationship of PMRHRDSSM with stator winding series structure. Firstly, the trajectory equation of PMRHRDSSM MTPA stator current vector is derived, and based on the relationship between PMRHRDSSM torque and reluctance dq‐axis current, a PMRHRDSSM current controller that meets MTPA constraints is designed. Afterwards, a PMRHRDSSM sliding mode speed controller was designed with input speed error and output reference torque. Finally, an experimental platform for the proposed control method was established, and the control method was experimentally validated.
x2DL: A high throughput architecture for binary‐ring‐learning‐with‐error‐based post quantum cryptography schemes
Authors: Ahmadunnisa Shaik , Mathe Sudha Ellison 
Published: September 2024

AbstractLattice‐based cryptography is one of the most promising cryptographic scheme which lies on the hardness of ring‐learning‐with‐error (RLWE). A new variant of RLWE, known as binary‐ring‐learning‐with‐error (BRLWE), has less key size and more efficient hardware implementations compared to RLWE‐based schemes. The key arithmetic operation for BRLWE‐based encryption scheme is the implementation of arithmetic operation represented by , where both and are integer polynomials, and is a binary polynomial. An efficient architecture to perform the arithmetic operation over a polynomial ring is proposed. We employ two linear feedback shift register structures comprising ‐net units in our design to reduce the computational time. This reduction in computational time yields to a significant improvement in the other performance metrics such as delay, area‐delay product (ADP), power‐delay product, throughput and efficiency compared to the existing designs. As per the experimental results, the authors’ proposed design has improvement in ADP when compared to the recently reported work.
Spin states of X-complex asteroids in the inner main belt
Authors: Athanasopoulos D. , Hanuš J. , Avdellidou C. , van Belle G. , Ferrero A. , Bonamico R. , Gazeas K. , Delbo M. , Rivet J. P. , Apostolovska G. , Todorović N. , Novakovic B. , Bebekovska E. V. , Romanyuk Y. , Bolin B. T. , Zhou W. , Agrusa H. 
Published: September 2024

Context. Based on the V-shape search method, two families, Athor and Zita, have been identified within the X-complex population of asteroids located in the inner main belt. The Athor family is ~3 Gyr old while the Zita family could be as old as the Solar System. Both families were found to be capable of delivering near-Earth asteroids (NEAs). Moreover, the Athor family was linked to the low-iron enstatite (EL) meteorites.
Aims. The aim of our study is to characterise the spin states of the members of the Athor and Zita collisional families and test whether these members have a spin distribution consistent with a common origin from the break up of their respective family parent asteroids.
Methods. To perform this test, our method is based on the well-established asteroid family evolution, which indicates that there should be a statistical predominance of retrograde-rotating asteroids on the inward side of family’s V-shape, and prograde-rotating asteroids on the outward side of family’s V-shape. To implement the method, we used photometric data from our campaign and the literature in order to reveal the spin states, and hence their rotation sense (prograde or retrograde), of the asteroids belonging to these families. We combined dense and sparse-in-time photometric data in order to construct asteroid rotational light curves; we performed the light curve inversion method to estimate the sidereal period and 3D convex shape along with the spin axis orientation in space of several family member asteroids.
Results. We obtained 34 new asteroid models for Athor family members and 17 for Zita family members. Along with the literature and revised models, the Athor family contains 60% (72% considering only the family’s core) of retrograde asteroids on the inward side and, conversely, 76% (77% considering only the family’s core) of prograde asteroids on the outward side. We also found that the Zita family exhibits 80% of retrograde asteroids on the inward side. In addition, the Zita family presents an equal amount of prograde and retrograde rotators (50% each) on the outward side. However, when we applied Kernel density estimation (KDE), we also found a clear peak for prograde asteroids on the outward side, as expected from the theory.
Conclusions. The spin states of these asteroids validate the existence of both families, with the Athor family exhibiting a stronger signature for the presence of retrograde-rotating and prograde-rotating asteroids on the inner and outer side of the family, respectively. Our work provides an independent confirmation and characterisation of these very old families, whose presence and characteristics offer constraints for theories and models of the Solar System’s evolution.