bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structure-guided engineering of a fast genetically encoded sensor for real-time H2O2monitoring
Authors: Lee Justin Daho , Won Woojin , Kimball Kandace , Wang Yihan , Yeboah Fred , Evitts Kira M. , Neiswanger Carlie , Schattauer Selena , Rappleye Michael , Bremner Samantha B , Chun Changho , Smith Netta , Mack David L. , Young Jessica E. , Justin Lee C. , Chavkin Charles , Berndt Andre 
Published: February 2024

AbstractHydrogen Peroxide (H2O2) is a central oxidant in redox biology due to its pleiotropic role in physiology and pathology. However, real-time monitoring of H2O2in living cells and tissues remains a challenge. We address this gap with the development of an optogenetic hydRogen perOxide Sensor (oROS), leveraging the bacterial peroxide binding domain OxyR. Previously engineered OxyR-based fluorescent peroxide sensors lack the necessary sensitivity or response speed for effective real-time monitoring. By structurally redesigning the fusion of Escherichia coli (E. coli) ecOxyR with a circularly permutated green fluorescent protein (cpGFP), we created a novel, green-fluorescent peroxide sensor oROS-G. oROS-G exhibits high sensitivity and fast on-and-off kinetics, ideal for monitoring intracellular H2O2dynamics. We successfully tracked real-time transient and steady-state H2O2levels in diverse biological systems, including human stem cell-derived neurons and cardiomyocytes, primary neurons and astrocytes, and mouse neurons and astrocytes in ex vivo brain slices. These applications demonstrate oROS’s capabilities to monitor H2O2as a secondary response to pharmacologically induced oxidative stress, G-protein coupled receptor (GPCR)-induced cell signaling, and when adapting to varying metabolic stress. We showcased the increased oxidative stress in astrocytes via Aβ-putriscine-MAOB axis, highlighting the sensor’s relevance in validating neurodegenerative disease models. oROS is a versatile tool, offering a window into the dynamic landscape of H2O2signaling. This advancement paves the way for a deeper understanding of redox physiology, with significant implications for diseases associated with oxidative stress, such as cancer, neurodegenerative disorders, and cardiovascular diseases.
Human Amniotic Epithelial Cells Promote Chx10−/Pax6+Müller Glia Subpopulation Reprogramming into Photoreceptor-like Cells
Authors: Gao Hui , Yin Zhiyuan , Huang Xiaona , Zeng Yuxiao , Zou Ting , Luodan A , Cha Zhe , Cheng Xuan , Ge Lingling , Kang Jiahui , Lin Xi , Gong Hong , Xie Jing , Fan Xiaotang , Xu Haiwei 
Published: February 2024

AbstractReprogramming Müller glia to regenerate neurons is a promising strategy for treating retinal degeneration, but whether Müller glia contain subpopulations with different regenerative fates remains unclear. Here, using single-cell RNA-seq analysis and Müller glia lineage-tracing mice with retinal degeneration, we reveal that Müller glia were heterogeneous and identify a specific Müller glial subpopulation (Chx10−/Pax6+) in healthy retinas that is activated and migrate to the outer nuclear layer (ONL) during photoreceptor degeneration. Transplantation of human amniotic epithelial cells (hAECs) facilitates the activation and extensive migration of the Chx10−/Pax6+Müller glial subpopulation to the ONL, where they are reprogrammed into photoreceptor-like cells. Mechanistically, hAECs degrade the inhibitory extracellular matrix through regulating matrix metalloproteinases, which probably induces remodeling of the microenvironment of Müller glia and contributes to cell reprogramming. Consequently, hAEC transplantation improves visual function in rd10 mice. Our findings uncover a distinctive Müller glial subpopulation with the potential for reprogramming into photoreceptors.
Combining systems and synthetic biology for in vivo enzymology
Authors: Castaño-Cerezo Sara , Chamas Alexandre , Kulyk Hanna , Treitz Christian , Bellvert Floriant , Tholey Andreas , Galéote Virginie , Camarasa Carole , Heux Stéphanie , Garcia-Alles Luis F. , Millard Pierre , Truan Gilles 
Published: February 2024

AbstractEnzymatic parameters are classically determinedin vitro, under conditions that are far from those encountered in cells, casting doubt on their physiological relevance. We developed a generic approach combining tools from synthetic and systems biology to measure enzymatic parametersin vivo. In the context of a synthetic carotenoid pathway inSaccharomyces cerevisiae, we focused on a phytoene synthase and three phytoene desaturases, which are difficult to studyin vitro. We designed, built, and analyzed a collection of yeast strains mimicking substantial variations in substrate concentration by strategically manipulating the expression of geranyl-geranyl pyrophosphate (GGPP) synthase. We successfully determinedin vivoMichaelis-Menten parameters (KM,Vmaxandkcat) for GGPP-converting phytoene synthase from absolute metabolomics, fluxomics and proteomics data, highlighting differences betweenin vivoandin vitroparameters. Leveraging the versatility of the same set of strains, we then extracted enzymatic parameters for two of the three phytoene desaturases. Our approach demonstrates the feasibility of assessing enzymatic parameters directlyin vivo, providing a novel perspective on the kinetic characteristics of enzymes in real cellular conditions.
Large-Scale Functional Connectome Fingerprinting for Generalization and Transfer Learning in Neuroimaging
Authors: Ogg Mattson , Kitchell Lindsey 
Published: February 2024

ABSTRACTFunctional MRI currently supports a limited application space stemming from modest dataset sizes, large interindividual variability and heterogeneity among scanning protocols. These constraints have made it difficult for fMRI researchers to take advantage of modern deep-learning tools that have revolutionized other fields such as NLP, speech transcription, and image recognition. To address these issues, we scaled up functional connectome fingerprinting as a neural network pre-training task, drawing inspiration from speaker recognition research, to learn a generalizable representation of brain function. This approach sets a new high-water mark for neural fingerprinting on a previously unseen scale, across many popular public fMRI datasets (individual recognition over held out scan sessions: 94% on MPI-Leipzig, 94% on NKI-Rockland, 73% on OASIS-3, and 99% on HCP). We show that this representation can also generalize to support accurate neural fingerprinting for completely new datasets and participants not used in training. Finally, we demonstrate that the representation learned by the network encodes features related to individual variability that supports some transfer learning to new tasks. These results open the door for a new generation of clinical applications based on functional imaging data.SIGNIFICANCE STATEMENTDeep learning models that leverage the increasing scale of available fMRI data could address fundamental generalization roadblocks. We drew inspiration from other domains that have successfully used AI to address these problems, namely human language technology, to guide our exploration of the potential for this approach in neuroimaging. Our pre-training approach sets a new high-watermark for functional connectome fingerprinting, achieving very high recognition accuracy across different tasks, scanning sessions, and acquisition parameters, even when the duration of a scan is limited to less than two minutes. We showed that we could re-purpose the representation learned by our model to recognize new individuals from new datasets and to predict new participants’ cognitive performance and traits.
Predicting Diabetes in Canadian Adults Using Machine Learning
Authors: Esser Kayla , Duong Monica , Kain Khalil , Tran Son , Sadeghi Aryan , Guergachi Aziz , Keshavjee Karim , Noaeen Mohammad , Shakeri Zahra 
Published: February 2024

AbstractRising diabetes rates have led to increased health-care costs and health complications. An estimated half of diabetes cases remain undiagnosed. Early and accurate diagnosis is crucial to mitigate disease progression and associated risks. This study addresses the challenge of predicting diabetes prevalence in Canadian adults by employing machine learning (ML) techniques to primary care data. We leveraged the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), Canada’s premier multi-disease electronic medical record surveillance system, and developed and tuned seven ML classification models to predict the likelihood of diabetes. The models were tested and validated, focusing on clinical patient characteristics influential in predicting diabetes. We found XGBoost performed best out of all the models, with an AUC of 92%. The most important features contributing to model prediction were HbA1c, LDL, and hypertension medication. Our research aims to aid healthcare professionals in early diagnosis and to identify key characteristics for targeted interventions. This study contributes to an understanding of how ML can enhance public health planning and reduce healthcare system burdens.
Unbiased, Cell-free Profiling of Single Influenza Genomes at High-throughput
Authors: Cowell Thomas W. , Puryear Wendy , Chen Chih-Lin , Ding Ruihua , Runstadler Jonathan , Han Hee-Sun 
Published: February 2024

AbstractThe segmented structure of the Influenza A virus (IAV) genome facilitates reassortment, segment exchange during co-infection. When divergent strains mix across human, agricultural, and wildlife reservoirs novel strains are generated, which has been the source of pandemics. Due to the limited throughput and infection-based assays, IAV reassortment studies has been limited to permissive reassortment. We have developed DE-flowSVP to achieve extremely high throughput, direct profiling of as many as 105IAV particles in a single-day experiment and enabled quantitative profiling of reassortment propensity between divergent strains for the first time. By profiling reassortants between two naturally circulating low-pathogenicity avian IAVs, we confirmed that molecular incompatibility yields strong preference toward within-strain mixing. Surprisingly, we revealed that two-to-three particle aggregation contributed primarily to genome mixing (75-99%), suggesting that aggregation mediated by sialic acid binding by viral surface proteins provides a secondary pathway to genome mixing while avoiding the co-packaging fitness cost. We showed that genome mixing is sensitively dependent on co-infection timing, relative segment abundances, and viral surface-protein background. DE-flowSVP enables large-scale survey of reassortment potential among the broad diversity of IAV strains informing pandemic strain emergence.
Circulating serum miRNAs predict response to platinum chemotherapy in high-grade serous ovarian cancer
Authors: Suzuki Kazuhiro , Yokoi Akira , Matsuzaki Juntaro , Yoshida Kosuke , Yamamoto Yusuke , Kato Tomoyasu , Ishikawa Mitsuya , Ochiya Takahiro , Kajiyama Hiroaki 
Published: February 2024

AbstractBackgroundPlatinum chemotherapy is the cornerstone of treatment for high-grade serous ovarian cancer (HGSOC); however, validated biomarkers that can accurately predict platinum response are lacking. Based on their roles in the underlying pathophysiology, circulating microRNAs are potential, noninvasive biomarkers in cancer. In the present study, we aimed to evaluate the circulating miRNA profiles of patients with HGSOC and to assess their potential utility as biomarkers to predict platinum response.MethodsPretreatment serum samples collected from patients who received platinum chemotherapy for stage III–IV HGSOC between 2008 and 2016 were analyzed using miRNA microarray. LASSO logistic regression analysis was used to construct predictive models for treatment-free interval of platinum (TFIp).ResultsThe median follow-up was 54.6 (range, 3.5–144.1) months. The comprehensive analysis of 2,588 miRNAs was performed in serum samples of 153 eligible patients, and predictive models were constructed using a combination of circulating miRNAs with an area under the receiver operating characteristic curve of 0.944 for TFIp > 1 month, 0.637 for TFIp ≥ 6 months, 0.705 for TFIp ≥ 12 months, and 0.938 for TFIp ≥ 36 months. Each predictive model provided a significant TFIp classification (p= 0.001 in TFIp >1 month,p= 0.013 in TFIp ≥ 6 months,p< 0.001 in TFIp ≥ 12 months, andp< 0.001 in TFIp ≥ 36 months).ConclusionCirculating miRNA profiles has potential utility in predicting platinum response in patients with HGSOC and can aid clinicians in choosing appropriate treatment strategies.
Projections of discovery potentials from expected background
Authors: Singh M. K. , Li H. B. , Wong H. T. , Sharma V. , Singh L. ,   TEXONO Collaboration
Published: February 2024

Background channels with their expected strength and uncertainty levels are usually known in the searches of novel phenomena prior to the experiments are conducted at their design stage. We quantitatively study the projected sensitivities in terms of discovery potentials. These are essential for the optimizations of the experimental specifications as well as of the cost effectiveness in various investments. Sensitivities in counting analysis are derived with complete Poisson statistics and its continuous approximation, and are compared with those using maximum likelihood analysis in which additional measurables are included as signatures. The roles and effects due to uncertainties in the background estimates are studied. Two expected features to establish positive effects are verified and quantified: (i) In counting-only experiments, the required signal strength can be derived with complete Poisson analysis, and the continuous approximation would underestimate the results. (ii) Incorporating continuous variables as additional constraints would reduce the required signal strength relative to that of counting-only analysis. The formulations are applied to the case on the experimental searches of neutrinoless double beta decay in which both ambient and two-neutrino backgrounds are considered.

Published by the American Physical Society
2024
Multiphoton signatures as a probe of
CP
violation in extended Higgs sectors
Authors: Kanemura Shinya , Katayama Kento , Mondal Tanmoy , Yagyu Kei 
Published: February 2024

We propose a novel signature with four-photon final states to probe CP-violating (CPV) extended Higgs sectors via ff¯→Z*→H1H2→4γ processes with H1,2 being additional neutral Higgs bosons. We focus on the nearly Higgs alignment scenario, in which the discovered Higgs boson almost corresponds to a neutral scalar state belonging to the isospin doublet field with the vacuum expectation value v≃246  GeV. We show that the branching ratios of H1,2→γγ can simultaneously be sizable when CPV phases in the Higgs potential are of order one due to the enhancement of charged-Higgs boson loops. Such branching ratios can be especially significant when the fermiophobic scenario is taken into account. As a simple example, we consider the general two Higgs doublet model, and demonstrate that the cross section for the four-photon process can be 0.1 fb at LHC with the masses of H1,2 to be a few 100 GeV in the Higgs alignment limit under the constraints from electric dipole moments (EDMs) and LHC Run-II data. We also illustrate that the searches for EDMs and diphoton resonances at high-luminosity LHC play complementary roles to explore CPV extended Higgs sectors.

Published by the American Physical Society
2024
Nucleon axial and pseudoscalar form factors using twisted-mass fermion ensembles at the physical point
Authors: Alexandrou Constantia , Bacchio Simone , Constantinou Martha , Finkenrath Jacob , Frezzotti Roberto , Kostrzewa Bartosz , Koutsou Giannis , Spanoudes Gregoris , Urbach Carsten ,   Extended Twisted Mass Collaboration
Published: February 2024

We compute the nucleon axial and pseudoscalar form factors using three Nf=2+1+1 twisted-mass fermion ensembles with all quark masses tuned to approximately their physical values. The values of the lattice spacings of these three physical point ensembles are 0.080, 0.068, and 0.057 fm and spatial sizes 5.1, 5.44, and 5.47 fm, respectively, yielding mπL>3.6. Convergence to the ground-state matrix elements is assessed using multistate fits. We study the momentum dependence of the three form factors and check the partially conserved axial-vector current (PCAC) hypothesis and the pion pole dominance (PPD). We show that in the continuum limit, the PCAC and PPD relations are satisfied. We also show that the Goldberger-Treimann relation is approximately fulfilled and determine the Goldberger-Treiman discrepancy. Our final results are gA=1.245(28)(14) for the nucleon axial charge, ⟨rA2⟩=0.339(48)(06)fm2 for the axial radius, gπNN≡limQ2→−mπ2GπNN(Q2)=13.25(67)(69) for the pion-nucleon coupling constant, and GP(0.88mμ2)≡gP*=8.99(39)(49) for the induced pseudoscalar form factor at the muon capture point.

Published by the American Physical Society
2024
Quantum critical behavior of the hyperkagome magnet
Mn3CoSi
Authors: Yamauchi Hiroki , Sari Dita Puspita , Yasui Yukio , Sakakura Terutoshi , Kimura Hiroyuki , Nakao Akiko , Ohhara Takashi , Honda Takashi , Kodama Katsuaki , Igawa Naoki , Ikeda Kazutaka , Iida Kazuki , Ueta Daichi , Yokoo Tetsuya , Frontzek Matthias D. , Chi Songxue , Fernandez-Baca Jaime A. , Kojima Kenji M. , Arseneau Donald , Morris Gerald , Hitti Bassam , Cai Yipeng , Berlie Adam , Watanabe Isao , Hsu Pai-Tse , Chen Yu-Sheng , Lee Min Kai , Hall Amelia Elisabeth , Balakrishnan Geetha , Chang Lieh-Jeng , Shamoto Shin-ichi 
Published: February 2024

β-Mn-type family alloys Mn3TX (T=Co, Rh, and Ir; X=Si and Ge) have a three-dimensional antiferromagnetic (AF) corner-shared triangular network, i.e., the hyperkagome lattice. The antiferromagnet Mn3RhSi shows magnetic short-range order over a wide temperature range of approximately 500 K above the Néel temperature TN of 190 K. In this family of compounds, as the lattice parameter decreases, the long-range magnetic ordering temperature decreases. Mn3CoSi has the smallest lattice parameter and the lowest TN in the family. The quantum critical point (QCP) from AF to the quantum paramagnetic state is expected near a cubic lattice parameter of 6.15 Å. Although the Néel temperature of Mn3CoSi is only 140 K, the emergence of the quantum critical behavior in Mn3CoSi is discussed. We study how the magnetic short-range order appears in Mn3CoSi by using neutron scattering, μSR, and bulk characterization such as specific heat capacity. According to the results, the neutron scattering intensity of the magnetic short-range order in Mn3CoSi does not change much at low temperatures from that of Mn3RhSi, although the μSR short-range order temperature of Mn3CoSi is largely suppressed to 240 K from that of Mn3RhSi. Correspondingly, the volume fraction of the magnetic short-range order regions, as shown by the initial asymmetry drop ratio of μSR above TN, also becomes small. Instead, the electronic-specific heat coefficient γ of Mn3CoSi is the largest in this Mn3TSi system, possibly due to the low-energy spin fluctuation near the quantum critical point.

Published by the American Physical Society
2024
Do family factors affect the usage intention of smart homes? A PLS-SEM empirical study from China
Authors: Cui Ting , Tang Shenlong , Hassan Siti Hasnah 
Published: February 2024

PurposeDespite the enormous benefits, smart homes (SHs) are still not widely adopted by residents in China. Furthermore, research on the intention to use SHs has overlooked the role of family factors. Thus, this study aims to propose a new research model to examine the impact of family factors on the usage intention (UI) of SHs.Design/methodology/approachThis study collected 265 valid data from potential users of SHs in China using a convenience sampling method. The PLS-SEM method was applied to test the research model and related hypotheses.FindingsThe empirical results confirm the mediating role of optimism (OP) in perceived family support (PFS)/perceived family trust (PFT) and attitude (AT). Unsurprisingly, the results validated the relationship between perceived usefulness (PU), attitude (AT) and UI of SHs through TAM theory. Besides, this study also identified the moderating effect of perceived risk (PR) between AT and UI.Practical implicationsTo improve SH adoption, practitioners should focus on family factors and utilize family influence to promote the spread of smart home reputation. Besides, SH practitioners should enhance user trust and reduce perceived risks through technological upgrades and security measures.Originality/valueBased on the Social Impact Theory and Technology Acceptance Model (TAM), this study is an empirical attempt to explore the impact of family factors on the intention to use SHs, expanding the research on smart home adoption.
Analysis of critical factors influencing sustainable infrastructure vulnerabilities using an ISM-MICMAC approach
Authors: Zhao Luwei , Wang Qing’e , Hwang Bon-Gang , Chang-Richards Alice Yan 
Published: February 2024

PurposeThe purpose of this study is to develop a new hybrid method that combines interpretative structural modeling (ISM) and matrix cross-impact multiplication applied to classification (MICMAC) to investigate the influencing factors of sustainable infrastructure vulnerability (SIV).Design/methodology/approach(1) Literature review and case study were used to identify the possible influencing factors; (2) a semi-structured interview was conducted to identify representative factors and the interrelationships among influencing factors; (3) ISM was adopted to identify the hierarchical structure of factors; (4) MICMAC was used to analyze the driving power (DRP) and dependence power (DEP) of each factor and (5) Semi-structured interview was used to propose strategies for overcoming SIV.FindingsResults indicate that (1) 18 representative factors related to SIV were identified; (2) the relationship between these factors was divided into a five-layer hierarchical structure. The 18 representative factors were divided into driving factors, dependent factors, linkage factors and independent factors and (3) 12 strategies were presented to address the negative effects of these factors.Originality/valueThe findings illustrate the factors influencing SIV and their hierarchical structures, which can benefit the stakeholders and practitioners of an infrastructure project by encouraging them to take effective countermeasures to deal with related SIVs.