bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bayesian dynamical system analysis of the effects of methylphenidate in children with attention-deficit/hyperactivity disorder: a randomized trial
Authors: Cai Weidong , Mizuno Yoshifumi , Tomoda Akemi , Menon Vinod 
Published: July 2023

AbstractMethylphenidate is a widely used and effective treatment for attention-deficit/hyperactivity disorder (ADHD), yet the underlying neural mechanisms and their relationship to changes in behavior are not fully understood. Specifically, it remains unclear how methylphenidate affects brain and behavioral dynamics, and the interplay between these dynamics, in individuals with ADHD. To address this gap, we used a novel Bayesian dynamical system model to investigate the effects of methylphenidate on latent brain states in 27 children with ADHD and 49 typically developing children using a double-blind, placebo-controlled crossover design. Methylphenidate remediated greater behavioral variability on a continuous performance task in children with ADHD. Children with ADHD exhibited aberrant latent brain state dynamics compared to typically developing children, with a single latent state showing particularly abnormal dynamics, which was remediated by methylphenidate. Additionally, children with ADHD showed brain state-dependent hyper-connectivity in the default mode network, which was also remediated by methylphenidate. Finally, we found that methylphenidate-induced changes in latent brain state dynamics, as well as brain state-related functional connectivity between salience and default mode networks, were correlated with improvements in behavioral variability. Taken together, our findings reveal a novel latent brain state dynamical process and circuit mechanism underlying the therapeutic effects of methylphenidate in childhood ADHD. We suggest that Bayesian dynamical system models may be particularly useful for capturing complex nonlinear changes in neural activity and behavioral variability associated with ADHD. Our approach may be of value to clinicians and researchers investigating the neural mechanisms underlying pharmacological treatment of psychiatric disorders.
Ammonium tetrathiomolybdate relieves oxidative stress in cisplatin-induced acute kidney injury via NRF2 signaling pathway
Authors: Qi Hao , Shi Haoyu , Yan Minbo , Zhao Liangyu , Yin Yinghao , Tan Xiaolin , Qi Huiyue , Li Hu , Weng Kangqiang , Tang Yuxin , Dai Yingbo 
Published: July 2023

AbstractCisplatin is an efficient chemotherapeutic agent for various solid tumors, but its usage is restricted by nephrotoxicity. A single dose of cisplatin can cause acute kidney injury (AKI), which is characterized by rapid reduction in kidney function. However, the current therapies, such as hydration, are limited. It is vital to develop novel therapeutic reagents that have both anticancer and renoprotective properties. The objective of this study was to determine whether ammonium tetrathiomolybdate (TM), a copper chelator used to treat cancer and disorders of copper metabolism, may offer protection against cisplatin-induced AKI. In this study, we demonstrated that TM treatment had antioxidative effects and mitigated cisplatin-induced AKI both in vivo and in vitro. Mechanically, TM inhibited NRF2 ubiquitination, which activated the NRF2 pathway in HK-2 cells and promoted the expression of target genes. It should be noted that the protective effect conferred by TM against cisplatin was compromised by the knockdown of the NRF2 gene. Furthermore, TM selectively activated the NRF2 pathways in the liver and kidney. The current study provided evidence for additional clinical applications of TM by showing that it activates NRF2 and has a favorable therapeutic impact on cisplatin-induced AKI.
Evaluation of pigment distribution in contact lenses with iris patterns by multiprobe analysis methods
Authors: Ito Eri , Takase Hiroshi , Yamamoto Katsuhiro 
Published: July 2023

AbstractIris-patterned lenses have recently become popular in the field of soft contact lenses (SCLs), and SCLs are typical examples of an industrial application of gel materials. As these patterns can be printed using oxidized-metal-containing pigments, the oxidized metals may be exposed on the lens surface depending on the printing method, potentially accounting for various health hazards. To evaluate the state of the exposed oxidized metal on the lens surface, we analyzed the colored layer, mainly consisting of pigments, on the lens surface via multiple probing methods, such as transmission electron microscopy (TEM), scanning TEM with energy-dispersive X-ray spectroscopy, hard X-ray photoelectron spectroscopy, and X-ray absorption fine structure (XAFS). Furthermore, we confirmed that the XAFS conversion electron yield (CEY) and fluorescence yield (FY) methods demonstrated high sensitivity toward revealing the existence of pigments. Thus, the use of a combination of XAFS CEY and FY methods can facilitate the effective distribution analysis of various elements in the interior of materials such as cosmetic CLs.
Genetically diverse mouse models of SARS-CoV-2 infection reproduce clinical variation in type I interferon and cytokine responses in COVID-19
Authors: Robertson Shelly J. , Bedard Olivia , McNally Kristin L. , Shaia Carl , Clancy Chad S. , Lewis Matthew , Broeckel Rebecca M. , Chiramel Abhilash I. , Shannon Jeffrey G. , Sturdevant Gail L. , Rosenke Rebecca , Anzick Sarah L. , Forte Elvira , Preuss Christoph , Baker Candice N. , Harder Jeffrey M. , Brunton Catherine , Munger Steven , Bruno Daniel P. , Lack Justin B. , Leung Jacqueline M. , Shamsaddini Amirhossein , Gardina Paul , Sturdevant Daniel E. , Sun Jian , Martens Craig , Holland Steven M. , Rosenthal Nadia A. , Best Sonja M. 
Published: July 2023

AbstractInflammation in response to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection drives severity of coronavirus disease 2019 (COVID-19) and is influenced by host genetics. To understand mechanisms of inflammation, animal models that reflect genetic diversity and clinical outcomes observed in humans are needed. We report a mouse panel comprising the genetically diverse Collaborative Cross (CC) founder strains crossed to human ACE2 transgenic mice (K18-hACE2) that confers susceptibility to SARS-CoV-2. Infection of CC x K18-hACE2 resulted in a spectrum of survival, viral replication kinetics, and immune profiles. Importantly, in contrast to the K18-hACE2 model, early type I interferon (IFN-I) and regulated proinflammatory responses were required for control of SARS-CoV-2 replication in PWK x K18-hACE2 mice that were highly resistant to disease. Thus, virus dynamics and inflammation observed in COVID-19 can be modeled in diverse mouse strains that provide a genetically tractable platform for understanding anti-coronavirus immunity.
Sequestration of histidine kinases by non-cognate response regulators establishes a threshold level of stimulation for bacterial two-component signaling
Authors: Sankhe Gaurav D. , Raja Rubesh , Singh Devendra Pratap , Bheemireddy Sneha , Rana Subinoy , Athira P. J. , Dixit Narendra M. , Saini Deepak Kumar 
Published: July 2023

AbstractBacterial two-component systems (TCSs) consist of a sensor histidine kinase (HK) that perceives a specific signal, and a cognate response regulator (RR) that modulates the expression of target genes. Positive autoregulation improves TCS sensitivity to stimuli, but may trigger disproportionately large responses to weak signals, compromising bacterial fitness. Here, we combine experiments and mathematical modelling to reveal a general design that prevents such disproportionate responses: phosphorylated HKs (HK~Ps) can be sequestered by non-cognate RRs. We study five TCSs of Mycobacterium tuberculosis and find, for all of them, non-cognate RRs that show higher affinity than cognate RRs for HK~Ps. Indeed, in vitro assays show that HK~Ps preferentially bind higher affinity non-cognate RRs and get sequestered. Mathematical modelling indicates that this sequestration would introduce a ‘threshold’ stimulus strength for eliciting responses, thereby preventing responses to weak signals. Finally, we construct tunable expression systems in Mycobacterium bovis BCG to show that higher affinity non-cognate RRs suppress responses in vivo.
Global meta-analysis shows reduced quality of food crops under inadequate animal pollination
Authors: Gazzea Elena , Batáry Péter , Marini Lorenzo 
Published: July 2023

AbstractAnimal pollination supports the production of a wide range of food crops fundamental to maintaining diverse and nutritionally balanced diets. Here, we present a global meta-analysis quantifying the contribution of pollination to multiple facets of crop quality, including both organoleptic and nutritional traits. In fruits and vegetables, pollinators strongly improve several commercially important attributes related to appearance and shelf life, whereas they have smaller effects on nutritional value. Pollination does not increase quality in stimulant crops, nuts, and spices. We report weak signals of a pollination deficit for organoleptic traits, which might indicate a potential service decline across agricultural landscapes. However, the deficit is small and non-significant at the α = 0.05 level, suggesting that pollen deposition from wild and/or managed pollinators is sufficient to maximise quality in most cases. As producing commercially suboptimal fruits can have multiple negative economic and environmental consequences, safeguarding pollination services is important to maintain food security.
Excitonic insulator to superconductor phase transition in ultra-compressed helium
Authors: Liu Cong , Errea Ion , Ding Chi , Pickard Chris , Conway Lewis J. , Monserrat Bartomeu , Fang Yue-Wen , Lu Qing , Sun Jian , Boronat Jordi , Cazorla Claudio 
Published: July 2023

AbstractHelium, the second most abundant element in the universe, exhibits an extremely large electronic band gap of about 20 eV at ambient pressures. While the metallization pressure of helium has been accurately determined, thus far little attention has been paid to the specific mechanisms driving the band-gap closure and electronic properties of this quantum crystal in the terapascal regime (1 TPa = 10 Mbar). Here, we employ density functional theory and many-body perturbation calculations to fill up this knowledge gap. It is found that prior to reaching metallicity helium becomes an excitonic insulator (EI), an exotic state of matter in which electrostatically bound electron-hole pairs may form spontaneously. Furthermore, we predict metallic helium to be a superconductor with a critical temperature of ≈ 20 K just above its metallization pressure and of ≈ 70 K at 100 TPa. These unforeseen phenomena may be critical for improving our fundamental understanding and modeling of celestial bodies.
Expulsion mechanism of the substrate-translocating subunit in ECF transporters
Authors: Thangaratnarajah Chancievan , Nijland Mark , Borges-Araújo Luís , Jeucken Aike , Rheinberger Jan , Marrink Siewert J. , Souza Paulo C. T. , Paulino Cristina , Slotboom Dirk J. 
Published: July 2023

AbstractEnergy-coupling factor (ECF)-type transporters mediate the uptake of micronutrients in many bacteria. They consist of a substrate-translocating subunit (S-component) and an ATP-hydrolysing motor (ECF module) Previous data indicate that the S-component topples within the membrane to alternately expose the binding site to either side of the membrane. In many ECF transporters, the substrate-free S-component can be expelled from the ECF module. Here we study this enigmatic expulsion step by cryogenic electron microscopy and reveal that ATP induces a concave-to-convex shape change of two long helices in the motor, thereby destroying the S-component’s docking site and allowing for its dissociation. We show that adaptation of the membrane morphology to the conformational state of the motor may favour expulsion of the substrate-free S-component when ATP is bound and docking of the substrate-loaded S-component after hydrolysis. Our work provides a picture of bilayer-assisted chemo-mechanical coupling in the transport cycle of ECF transporters.
Performance of two complementary machine-learned potentials in modelling chemically complex systems
Authors: Gubaev Konstantin , Zaverkin Viktor , Srinivasan Prashanth , Duff Andrew Ian , Kästner Johannes , Grabowski Blazej 
Published: July 2023

AbstractChemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space. The complexity however makes interatomic potential development challenging. We explore two complementary machine-learned potentials—the moment tensor potential (MTP) and the Gaussian moment neural network (GM-NN)—in simultaneously describing configurational and vibrational degrees of freedom in the Ta-V-Cr-W alloy family. Both models are equally accurate with excellent performance evaluated against density-functional-theory. They achieve root-mean-square-errors (RMSEs) in energies of less than a few meV/atom across 0 K ordered and high-temperature disordered configurations included in the training. Even for compositions not in training, relative energy RMSEs at high temperatures are within a few meV/atom. High-temperature molecular dynamics forces have similarly small RMSEs of about 0.15 eV/Å for the disordered quaternary included in, and ternaries not part of training. MTPs achieve faster convergence with training size; GM-NNs are faster in execution. Active learning is partially beneficial and should be complemented with conventional human-based training set generation.
Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy
Authors: Shin Dongil , Alberdi Ryan , Lebensohn Ricardo A. , Dingreville Rémi 
Published: July 2023

AbstractRecent developments integrating micromechanics and neural networks offer promising paths for rapid predictions of the response of heterogeneous materials with similar accuracy as direct numerical simulations. The deep material network is one such approaches, featuring a multi-layer network and micromechanics building blocks trained on anisotropic linear elastic properties. Once trained, the network acts as a reduced-order model, which can extrapolate the material’s behavior to more general constitutive laws, including nonlinear behaviors, without the need to be retrained. However, current training methods initialize network parameters randomly, incurring inevitable training and calibration errors. Here, we introduce a way to visualize the network parameters as an analogous unit cell and use this visualization to “quilt” patches of shallower networks to initialize deeper networks for a recursive training strategy. The result is an improvement in the accuracy and calibration performance of the network and an intuitive visual representation of the network for better explainability.
Sampling frequency thresholds for the quantum advantage of the quantum approximate optimization algorithm
Authors: Lykov Danylo , Wurtz Jonathan , Poole Cody , Saffman Mark , Noel Tom , Alexeev Yuri 
Published: July 2023

AbstractWe compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers Gurobi and MQLib to solve the MaxCut problem on 3-regular graphs. We identify the minimum noiseless sampling frequency and depth p required for a quantum device to outperform classical algorithms. There is potential for quantum advantage on hundreds of qubits and moderate depth with a sampling frequency of 10 kHz. We observe, however, that classical heuristic solvers are capable of producing high-quality approximate solutions in linear time complexity. In order to match this quality for large graph sizes N, a quantum device must support depth p > 11. Additionally, multi-shot QAOA is not efficient on large graphs, indicating that QAOA p ≤ 11 does not scale with N. These results limit achieving quantum advantage for QAOA MaxCut on 3-regular graphs. Other problems, such as different graphs, weighted MaxCut, and 3-SAT, may be better suited for achieving quantum advantage on near-term quantum devices.
Genome-wide association study identified six loci associated with adverse drug reactions to aripiprazole in schizophrenia patients
Authors: Wang Xueping , Mei Dongli , Lu Zhe , Zhang Yuyanan , Sun Yaoyao , Lu Tianlan , Yan Hao , Yue Weihua 
Published: July 2023

AbstractAripiprazole is recommended for routine use in schizophrenia patients. However, the biological mechanism for the adverse drug reactions (ADRs) among schizophrenia patients with the antipsychotic drug aripiprazole is far from clear. To explore the potential genetic factors that may cause movement-related adverse antipsychotic effects in patients, we conducted an association analysis between movement-related ADRs and SNPs in schizophrenia patients receiving aripiprazole monotherapy. In this study, multiple ADRs of 384 patients were quantified within 6-week treatment, and the scores of movement-related ADRs at baseline and follow-up time points during treatment were obtained. The highest score record was used as the quantitative index in analysis, and genetic analysis at the genome-wide level was conducted. The SNP rs4149181 in SLC22A8 [P = 2.28 × 10−8] showed genome-wide significance, and rs2284223 in ADCYAP1R1 [P = 9.76 × 10−8], rs73258503 in KCNIP4 [P = 1.39 × 10−7], rs678428 in SMAD9 [P = 4.70 × 10−7], rs6421034 in NAP1L4 [P = 6.80 × 10−7], and rs1394796 in ERBB4 [P = 8.60 × 10−7] were found to be significantly associated with movement-related ADRs. The combined prediction model of these six loci showed acceptable performance in predicting adverse events [area under the curve (AUC): 0.84]. Combined with the function and network of the above genes and other candidate loci (KCNA1, CACNG1, etc.), we hypothesize that SLC22A8 and KCNIP4-Kv channel perform their respective functions as transporter or channel and participate in the in vivo metabolism or effects of aripiprazole. The above results imply the important function of ion transporters and channels in movement-related adverse antipsychotic effects in aripiprazole monotherapy schizophrenia patients.
A comprehensive multi-domain dataset for mitotic figure detection
Authors: Aubreville Marc , Wilm Frauke , Stathonikos Nikolas , Breininger Katharina , Donovan Taryn A. , Jabari Samir , Veta Mitko , Ganz Jonathan , Ammeling Jonas , van Diest Paul J. , Klopfleisch Robert , Bertram Christof A. 
Published: July 2023

AbstractThe prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.
Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools
Authors: Ingle Ruchita , Habib Wahaj , Connolly John , McCorry Mark , Barry Stephen , Saunders Matthew 
Published: July 2023

AbstractWetlands are one of the major contributors of methane (CH4) emissions to the atmosphere and the intensity of emissions is driven by local environmental variables and spatial heterogeneity. Peatlands are a major wetland class and there are numerous studies that provide estimates of methane emissions at chamber or eddy covariance scales, but these are not often aggregated to the site/ecosystem scale. This study provides a robust approach to map dominant vegetation communities and to use these areas to upscale methane fluxes from chamber to site scale using a simple weighted-area approach. The proposed methodology was tested at three peatlands in Ireland over a duration of 2 years. The annual vegetation maps showed an accuracy ranging from 83 to 99% for near-natural to degraded sites respectively. The upscaled fluxes were highest (2.25 and 3.80 gC m−2 y−1) at the near-natural site and the rehabilitation (0.17 and 0.31 gC m−2 y−1), degraded (0.15 and 0.27 gC m−2 y−1) site emissions were close to net-zero throughout the study duration. Overall, the easy to implement methodology proposed in this study can be applied across various landuse types to assess the impact of peatland rehabilitation on methane emissions by mapping ecological change.
Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA
Authors: Ham Sungwon , Seo Jiyeon , Yun Jihye , Bae Yun Jung , Kim Tackeun , Sunwoo Leonard , Yoo Sooyoung , Jung Seung Chai , Kim Jeong-Whun , Kim Namkug 
Published: July 2023

AbstractAccurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA). A total of 154 3D TOF-MRA datasets with intracranial aneurysms were acquired, and the gold standards were manually drawn by neuroradiologists. We also obtained 113 subjects from a public dataset for external validation. These angiograms were pre-processed by using skull-stripping, signal intensity normalization, and N4 bias correction. The 3D patches along the vessel skeleton from MRA were extracted. Values of the ratio between the aneurysmal and the normal patches ranged from 1:1 to 1:5. The semantic segmentation on intracranial aneurysms was trained using a 3D U-Net with an auxiliary classifier to overcome the imbalance in patches. The proposed method achieved an accuracy of 0.910 in internal validation and external validation accuracy of 0.883 with a 2:1 ratio of normal to aneurysmal patches. This multi-task learning method showed that the aneurysm segmentation performance was sufficient to be helpful in an actual clinical setting.