bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diffusion of Circumpolar Deep Water Towards Antarctica
Authors: Yamazaki Kaihe , Aoki Shigeru , Mizobata Kohei 
Published: February 2023

AbstractWarm, salty Circumpolar Deep Water (CDW) is recognized as the primary driver for Antarctic glacial melt, but the mechanism by which it reaches the continental shelves remains highly uncertain from an observational standpoint. With the scarcity of eddy flux estimation in the Antarctic margin, we quantify the isopycnal diffusivity of CDW using hydrographic variability and satellite altimetry under the mixing length framework. For comparison, the spiciness and thickness are used as isopycnal tracers, and the two tracers yield qualitatively similar estimates. Over the Antarctic Circumpolar Current (ACC), variation of mixing length is generally aligned with the jet‐induced mixing suppression, including in areas where topography blocks the jet's influence. In contrast, the mixing length does not depend on the mean flow in the subpolar zone, likely reflecting the relatively quiescent flow regime. The estimated isopycnal diffusivity ranges from 100 to 500 m2 s−1 south of the ACC. The eddy diffusivity tends to be enhanced where the gradient of isopycnal thickness becomes small and CDW intrudes onshore. The cross‐slope eddy CDW flux is estimated, and the associated onshore heat flux across is calculated as ∼3.6 TW in the eastern Indian sector. The eddy heat flux and coastal solar heating are generally balanced with cryospheric heat sinks including glacial melting and surface freezing, suggesting that the eddy advection is substantial for the onshore CDW flux. The thickness field is essential for determining mixing length and eddy fluxes in the subpolar zone, however this is not the case in the ACC domain.
Different Types of Corona Discharges Associated With High‐Altitude Positive Narrow Bipolar Events Nearby Cloud Top
Authors: Li Dongshuai , Luque Alejandro , Gordillo‐Vazquez F. J. , Pérez‐Invernón F. J. , Husbjerg Lasse Skaaning , Neubert Torsten , Chanrion Olivier , Lu Gaopeng , Zhang Hongbo , Han Jing , Lehtinen Nikolai G. , Østgaard Nikolai , Reglero Víctor 
Published: February 2023

AbstractSingle‐ and multi‐pulse blue corona discharges are frequently observed in thunderstorm clouds. Although we know they often correlate with Narrow Bipolar Events (NBEs) in Very Low Frequency/Low Frequency radio signals, their physics is not well understood. Here, we report a detailed analysis of different types of blue corona discharges observed by the Atmosphere‐Space Interactions Monitor during an overpass of a thundercloud cell nearby Malaysia. Both single‐ and multi‐pulse blue corona discharges were associated with positive NBEs at the top of the cloud, reaching about 18 km altitude. We find that the primary pulses of multi‐pulse discharges have weaker current moments than the single‐pulse discharges, suggesting that the multi‐pulse discharges either have shorter vertical channels or have weaker currents than the single‐pulse discharges. The subsequent pulse trains of the multi‐pulse discharges delayed some milliseconds are likely from horizontally oriented electrical discharges, but some NBEs, correlated with both single‐and multi‐pulse discharges, include small‐amplitude oscillations within a few microseconds inside their waveforms, which are unresolved in the optical observation and yet to be understood. Furthermore, by jointly analyzing the optical and radio observations, we estimate the photon free mean path at the cloud top to be ∼6 m.
Processes Controlling Methane Emissions From a Tropical Peatland Drainage Canal
Authors: Somers Lauren D. , Hoyt Alison , Cobb Alexander R. , Isnin Suhailah , Suhip Muhammad Asri Akmal bin Haji , Sukri Rahayu S. , Gandois Laure , Harvey Charles 
Published: February 2023

AbstractMost peat domes in Southeast Asia are crisscrossed by networks of drainage canals. These canals are a potentially important source of methane to the atmosphere because the groundwater that discharges into them carries high concentrations of dissolved methane that is produced within peat. In this study, we present an isotope‐enabled numerical model that simulates transport, degassing, and oxidation of methane and dissolved inorganic carbon (DIC) along a drainage canal. We then estimate methane fluxes through a 5‐km canal that crosses a disturbed, forested, but undeveloped, peat dome in Brunei Darussalam by applying this model to field data: concentrations and stable carbon isotopic ratios of both methane and dissolved inorganic carbon from both peat porewater and canal water. We estimate that approximately 70% of the methane entering the canal is oxidized within the canal, 26% is degassed to the atmosphere, and 4% is transported toward the ocean, under low to moderate flow conditions. The flux of methane to the atmosphere is lowest at the maximum elevation of the canal, where flow is stagnant and methane concentrations are highest. Downstream, as flow velocity increases, methane emissions plateau even as methane concentrations decrease. The resulting methane emissions from the canal are large compared to emissions from the peat surface and vegetation on a per‐area basis. However, since the canal covers only a small portion of the catchment area, the canal may be a substantial but not dominant source of methane from the peatland.
Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study
Authors: Wang Zhaobin , Zhou Xiaocheng , Gui Yuanyuan , Liu Manhua , Lu Hui 
Published: February 2023

AbstractAttention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in school-aged children. Its accurate diagnosis looks after patients’ interests well with effective treatment, which is important to them and their family. Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used to characterize the abnormal brain function by computing the voxel-wise measures and Pearson’s correlation (PC)-based functional connectivity (FC) for ADHD diagnosis. However, exploring the powerful measures of rsfMRI to improve ADHD diagnosis remains a particular challenge. To this end, this paper proposes an automated ADHD classification framework by fusion of multiple measures of rsfMRI in adolescent brain. First, we extract the voxel-wise measures and ROI-wise time series from the brain regions of rsfMRI after preprocessing. Then, to extract the multiple functional connectivities, we compute the PC-derived FCs including the topographical information-based high-order FC (tHOFC) and dynamics-based high-order FC (dHOFC), the sparse representation (SR)-derived FCs including the group SR (GSR), the strength and similarity guided GSR (SSGSR), and sparse low-rank (SLR). Finally, these measures are combined with multiple kernel learning (MKL) model for ADHD classification. The proposed method is applied to the Adolescent Brain and Cognitive Development (ABCD) dataset. The results show that the FCs of dHOFC and SLR perform better than the others. Fusing multiple measures achieves the best classification performance (AUC = 0.740, accuracy = 0.6916), superior to those from the single measure and the previous studies. We have identified the most discriminative FCs and brain regions for ADHD diagnosis, which are consistent with those of published literature.
Latent class analysis of psychotic-affective disorders with data-driven plasma proteomics
Authors: Rhee Sang Jin , Shin Dongyoon , Shin Daun , Song Yoojin , Joo Eun-Jeong , Jung Hee Yeon , Roh Sungwon , Lee Sang-Hyuk , Kim Hyeyoung , Bang Minji , Lee Kyu Young , Kim Se Hyun , Kim Minah , Lee Jihyeon , Kim Jaenyeon , Kim Yeongshin , Kwon Jun Soo , Ha Kyooseob , Kim Youngsoo , Ahn Yong Min 
Published: February 2023

AbstractData-driven approaches to subtype transdiagnostic samples are important for understanding heterogeneity within disorders and overlap between disorders. Thus, this study was conducted to determine whether plasma proteomics-based clustering could subtype patients with transdiagnostic psychotic-affective disorder diagnoses. The study population included 504 patients with schizophrenia, bipolar disorder, and major depressive disorder and 160 healthy controls, aged 19 to 65 years. Multiple reaction monitoring was performed using plasma samples from each individual. Pathologic peptides were determined by linear regression between patients and healthy controls. Latent class analysis was conducted in patients after peptide values were stratified by sex and divided into tertile values. Significant demographic and clinical characteristics were determined for the latent clusters. The latent class analysis was repeated when healthy controls were included. Twelve peptides were significantly different between the patients and healthy controls after controlling for significant covariates. Latent class analysis based on these peptides after stratification by sex revealed two distinct classes of patients. The negative symptom factor of the Brief Psychiatric Rating Scale was significantly different between the classes (t = −2.070, p = 0.039). When healthy controls were included, two latent classes were identified, and the negative symptom factor of the Brief Psychiatric Rating Scale was still significant (t = −2.372, p = 0.018). In conclusion, negative symptoms should be considered a significant biological aspect for understanding the heterogeneity and overlap of psychotic-affective disorders.
Neogenin suppresses tumor progression and metastasis via inhibiting Merlin/YAP signaling
Authors: Hu Xiaohan , Li Li , Li Fang , Yang Yuan , An Jingnan , Zhou Xinghua , Zhang Rui , Shi Lingli , Zhao He , Wang Jian , Hu Yizhou , Xu Yunyun 
Published: February 2023

AbstractFrom in situ growth to invasive dissemination is the most lethal attribute of various tumor types. This transition is majorly mediated by the dynamic interplay between two cancer hallmarks, EMT and cell cycle. In this study, we applied nonlinear association analysis in 33 cancer types and found that most signaling receptors simultaneously associating with EMT and cell cycle are potential tumor suppressors. Here we find that a top co-associated receptor, Neogenin (NEO1), inhibits colorectal cancer (CRC) and Glioma in situ growth and metastasis by forming a complex with Merlin (NF2), and subsequent simultaneous promoting the phosphorylation of YAP. Furthermore, Neogenin protein level is associated with good prognosis and correlates with Merlin status in CRC and Glioma. Collectively, our results define Neogenin as a tumor suppressor in CRC and Glioma that acts by restricting oncogenic signaling by the Merlin-YAP pathway, and suggest Neogenin as a candidate therapeutic agent for CRC and Glioma.
Intrinsic macroscale oscillatory modes driving long range functional connectivity in female rat brains detected by ultrafast fMRI
Authors: Cabral Joana , Fernandes Francisca F. , Shemesh Noam 
Published: February 2023

AbstractSpontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals correlate across distant brain areas, shaping functionally relevant intrinsic networks. However, the generative mechanism of fMRI signal correlations, and in particular the link with locally-detected ultra-slow oscillations, are not fully understood. To investigate this link, we record ultrafast ultrahigh field fMRI signals (9.4 Tesla, temporal resolution = 38 milliseconds) from female rats across three anesthesia conditions. Power at frequencies extending up to 0.3 Hz is detected consistently across rat brains and is modulated by anesthesia level. Principal component analysis reveals a repertoire of modes, in which transient oscillations organize with fixed phase relationships across distinct cortical and subcortical structures. Oscillatory modes are found to vary between conditions, resonating at faster frequencies under medetomidine sedation and reducing both in number, frequency, and duration with the addition of isoflurane. Peaking in power within clear anatomical boundaries, these oscillatory modes point to an emergent systemic property. This work provides additional insight into the origin of oscillations detected in fMRI and the organizing principles underpinning spontaneous long-range functional connectivity.
LSD1/PRMT6-targeting gene therapy to attenuate androgen receptor toxic gain-of-function ameliorates spinobulbar muscular atrophy phenotypes in flies and mice
Authors: Prakasam Ramachandran , Bonadiman Angela , Andreotti Roberta , Zuccaro Emanuela , Dalfovo Davide , Marchioretti Caterina , Tripathy Debasmita , Petris Gianluca , Anderson Eric N. , Migazzi Alice , Tosatto Laura , Cereseto Anna , Battaglioli Elena , Sorarù Gianni , Lim Wooi Fang , Rinaldi Carlo , Sambataro Fabio , Pourshafie Naemeh , Grunseich Christopher , Romanel Alessandro , Pandey Udai Bhan , Contestabile Andrea , Ronzitti Giuseppe , Basso Manuela , Pennuto Maria 
Published: February 2023

AbstractSpinobulbar muscular atrophy (SBMA) is caused by CAG expansions in the androgen receptor gene. Androgen binding to polyQ-expanded androgen receptor triggers SBMA through a combination of toxic gain-of-function and loss-of-function mechanisms. Leveraging cell lines, mice, and patient-derived specimens, we show that androgen receptor co-regulators lysine-specific demethylase 1 (LSD1) and protein arginine methyltransferase 6 (PRMT6) are overexpressed in an androgen-dependent manner specifically in the skeletal muscle of SBMA patients and mice. LSD1 and PRMT6 cooperatively and synergistically transactivate androgen receptor, and their effect is enhanced by expanded polyQ. Pharmacological and genetic silencing of LSD1 and PRMT6 attenuates polyQ-expanded androgen receptor transactivation in SBMA cells and suppresses toxicity in SBMA flies, and a preclinical approach based on miRNA-mediated silencing of LSD1 and PRMT6 attenuates disease manifestations in SBMA mice. These observations suggest that targeting overexpressed co-regulators can attenuate androgen receptor toxic gain-of-function without exacerbating loss-of-function, highlighting a potential therapeutic strategy for patients with SBMA.
The p97-UBXD8 complex regulates ER-Mitochondria contact sites by altering membrane lipid saturation and composition
Authors: Ganji Rakesh , Paulo Joao A. , Xi Yuecheng , Kline Ian , Zhu Jiang , Clemen Christoph S. , Weihl Conrad C. , Purdy John G. , Gygi Steve P. , Raman Malavika 
Published: February 2023

AbstractThe intimate association between the endoplasmic reticulum (ER) and mitochondrial membranes at ER-Mitochondria contact sites (ERMCS) is a platform for critical cellular processes, particularly lipid synthesis. How contacts are remodeled and the impact of altered contacts on lipid metabolism remains poorly understood. We show that the p97 AAA-ATPase and its adaptor ubiquitin-X domain adaptor 8 (UBXD8) regulate ERMCS. The p97-UBXD8 complex localizes to contacts and its loss increases contacts in a manner that is dependent on p97 catalytic activity. Quantitative proteomics and lipidomics of ERMCS demonstrates alterations in proteins regulating lipid metabolism and a significant change in membrane lipid saturation upon UBXD8 deletion. Loss of p97-UBXD8 increased membrane lipid saturation via SREBP1 and the lipid desaturase SCD1. Aberrant contacts can be rescued by unsaturated fatty acids or overexpression of SCD1. We find that the SREBP1-SCD1 pathway is negatively impacted in the brains of mice with p97 mutations that cause neurodegeneration. We propose that contacts are exquisitely sensitive to alterations to membrane lipid composition and saturation.
Microbiota-derived acetate enhances host antiviral response via NLRP3
Authors: Niu Junling , Cui Mengmeng , Yang Xin , Li Juan , Yao Yuhui , Guo Qiuhong , Lu Ailing , Qi Xiaopeng , Zhou Dongming , Zhang Chenhong , Zhao Liping , Meng Guangxun 
Published: February 2023

AbstractPathogenic viral infections represent a major challenge to human health. Host immune responses to respiratory viruses are closely associated with microbiome and metabolism via the gut-lung axis. It has been known that host defense against influenza A virus (IAV) involves activation of the NLRP3 inflammasome, however, mechanisms behind the protective function of NLRP3 are not fully known. Here we show that an isolated bacterial strain, Bifidobacterium pseudolongum NjM1, enriched in the gut microbiota of Nlrp3−/− mice, protects wild-type but not Nlrp3 deficient mice against IAV infection. This effect depends on the enhanced production of type I interferon (IFN-I) mediated by NjM1-derived acetate. Application of exogenous acetate reproduces the protective effect of NjM1. Mechanistically, NLRP3 bridges GPR43 and MAVS, and promotes the oligomerization and signalling of MAVS; while acetate enhances MAVS aggregation upon GPR43 engagement, leading to elevated IFN-I production. Thus, our data support a model of NLRP3 mediating enhanced induction of IFN-I via acetate-producing bacterium and suggest that the acetate-GPR43-NLRP3-MAVS-IFN-I signalling axis is a potential therapeutic target against respiratory viral infections.
Pulse-driven self-reconfigurable meta-antennas
Authors: Ushikoshi Daiju , Higashiura Riku , Tachi Kaito , Fathnan Ashif Aminulloh , Mahmood Suhair , Takeshita Hiroki , Homma Haruki , Akram Muhammad Rizwan , Vellucci Stefano , Lee Jiyeon , Toscano Alessandro , Bilotti Filiberto , Christopoulos Christos , Wakatsuchi Hiroki 
Published: February 2023

AbstractWireless communications and sensing have notably advanced thanks to the recent developments in both software and hardware. Although various modulation schemes have been proposed to efficiently use the limited frequency resources by exploiting several degrees of freedom, antenna performance is essentially governed by frequency only. Here, we present an antenna design concept based on metasurfaces to manipulate antenna performances in response to the time width of electromagnetic pulses. We numerically and experimentally show that by using a proper set of spatially arranged metasurfaces loaded with lumped circuits, ordinary omnidirectional antennas can be reconfigured by the incident pulse width to exhibit directional characteristics varying over hundreds of milliseconds or billions of cycles, far beyond conventional performance. We demonstrate that the proposed concept can be applied for sensing, selective reception under simultaneous incidence and mutual communications as the first step to expand existing frequency resources based on pulse width.
Machine learning potentials for metal-organic frameworks using an incremental learning approach
Authors: Vandenhaute Sander , Cools-Ceuppens Maarten , DeKeyser Simon , Verstraelen Toon , Van Speybroeck Veronique 
Published: February 2023

AbstractComputational modeling of physical processes in metal-organic frameworks (MOFs) is highly challenging due to the presence of spatial heterogeneities and complex operating conditions which affect their behavior. Density functional theory (DFT) may describe interatomic interactions at the quantum mechanical level, but is computationally too expensive for systems beyond the nanometer and picosecond range. Herein, we propose an incremental learning scheme to construct accurate and data-efficient machine learning potentials for MOFs. The scheme builds on the power of equivariant neural network potentials in combination with parallelized enhanced sampling and on-the-fly training to simultaneously explore and learn the phase space in an iterative manner. With only a few hundred single-point DFT evaluations per material, accurate and transferable potentials are obtained, even for flexible frameworks with multiple structurally different phases. The incremental learning scheme is universally applicable and may pave the way to model framework materials in larger spatiotemporal windows with higher accuracy.
Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table
Authors: Rodriguez Alejandro , Lin Changpeng , Yang Hongao , Al-Fahdi Mohammed , Shen Chen , Choudhary Kamal , Zhao Yong , Hu Jianjun , Cao Bingyang , Zhang Hongbin , Hu Ming 
Published: February 2023

AbstractExisting machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness and precision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55 elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learning and data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralow lattice thermal conductivity (<1 Wm−1 K−1) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, a class of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550 quaternary Heuslers, respectively.
A multi-centre polyp detection and segmentation dataset for generalisability assessment
Authors: Ali Sharib , Jha Debesh , Ghatwary Noha , Realdon Stefano , Cannizzaro Renato , Salem Osama E. , Lamarque Dominique , Daul Christian , Riegler Michael A. , Anonsen Kim V. , Petlund Andreas , Halvorsen Pål , Rittscher Jens , de Lange Thomas , East James E. 
Published: February 2023

AbstractPolyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation.