bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gut microbiota alters host bile acid metabolism to contribute to intrahepatic cholestasis of pregnancy
Authors: Tang Bo , Tang Li , Li Shengpeng , Liu Shuang , He Jialin , Li Pan , Wang Sumin , Yang Min , Zhang Longhui , Lei Yuanyuan , Tu Dianji , Tang Xuefeng , Hu Hua , Ouyang Qin , Chen Xia , Yang Shiming 
Published: March 2023

AbstractIntrahepatic cholestasis of pregnancy (ICP) is a female pregnancy-specific disorder that is characterized by increased serum bile acid and adverse fetal outcomes. The aetiology and mechanism of ICP are poorly understood; thus, existing therapies have been largely empiric. Here we show that the gut microbiome differed significantly between individuals with ICP and healthy pregnant women, and that colonization with gut microbiome from ICP patients was sufficient to induce cholestasis in mice. The gut microbiomes of ICP patients were primarily characterized by Bacteroides fragilis (B. fragilis), and B. fragilis was able to promote ICP by inhibiting FXR signaling via its BSH activity to modulate bile acid metabolism. B. fragilis-mediated FXR signaling inhibition was responsible for excessive bile acid synthesis and interrupted hepatic bile excretion to ultimately promote the initiation of ICP. We propose that modulation of the gut microbiota-bile acid-FXR axis may be of value for ICP treatment.
Wind-driven upwelling of iron sustains dense blooms and food webs in the eastern Weddell Gyre
Authors: Moreau Sebastien , Hattermann Tore , de Steur Laura , Kauko Hanna M. , Ahonen Heidi , Ardelan Murat , Assmy Philipp , Chierici Melissa , Descamps Sebastien , Dinter Tilman , Falkenhaug Tone , Fransson Agneta , Grønningsæter Eirik , Hallfredsson Elvar H. , Huhn Oliver , Lebrun Anais , Lowther Andrew , Lübcker Nico , Monteiro Pedro , Peeken Ilka , Roychoudhury Alakendra , Różańska Magdalena , Ryan-Keogh Thomas , Sanchez Nicolas , Singh Asmita , Simonsen Jan Henrik , Steiger Nadine , Thomalla Sandy J. , van Tonder Andre , Wiktor Jozef M. , Steen Harald 
Published: March 2023

AbstractThe Southern Ocean is a major sink of anthropogenic CO2 and an important foraging area for top trophic level consumers. However, iron limitation sets an upper limit to primary productivity. Here we report on a considerably dense late summer phytoplankton bloom spanning 9000 km2 in the open ocean of the eastern Weddell Gyre. Over its 2.5 months duration, the bloom accumulated up to 20 g C m−2 of organic matter, which is unusually high for Southern Ocean open waters. We show that, over 1997–2019, this open ocean bloom was likely driven by anomalies in easterly winds that push sea ice southwards and favor the upwelling of Warm Deep Water enriched in hydrothermal iron and, possibly, other iron sources. This recurring open ocean bloom likely facilitates enhanced carbon export and sustains high standing stocks of Antarctic krill, supporting feeding hot spots for marine birds and baleen whales.
Spider-inspired tunable mechanosensor for biomedical applications
Authors: Kim Taewi , Hong Insic , Roh Yeonwook , Kim Dongjin , Kim Sungwook , Im Sunghoon , Kim Changhwan , Jang Kiwon , Kim Seongyeon , Kim Minho , Park Jieun , Gong Dohyeon , Ahn Kihyeon , Lee Jingoo , Lee Gunhee , Lee Hak-Seung , Kang Jeehoon , Hong Ji Man , Lee Seungchul , Seo Sungchul , Koo Bon-Kwon , Koh Je-sung , Han Seungyong , Kang Daeshik 
Published: March 2023

AbstractThe recent advances of wearable sensors are remarkable but there are still limitations that they need to be refabricated to tune the sensor for target signal. However, biological sensory systems have the inherent potential to adjust their sensitivity according to the external environment, allowing for a broad and enhanced detection. Here, we developed a Tunable, Ultrasensitive, Nature-inspired, Epidermal Sensor (TUNES) that the strain sensitivity was dramatically increased (GF ~30k) and the pressure sensitivity could be tuned (10–254 kPa−1) by preset membrane tension. The sensor adjusts the sensitivity to the pressure regime by preset tension, so it can measure a wide range (0.05 Pa–25 kPa) with the best performance: from very small signals such as minute pulse to relatively large signals such as muscle contraction and respiration. We verified its capabilities as a wearable health monitoring system by clinical trial comparing with pressure wire which is considered the current gold standard of blood pressure (r = 0.96) and home health care system by binary classification of Old’s/Young’s pulse waves via machine learning (accuracy 95%).
Lignin deconstruction by anaerobic fungi
Authors: Lankiewicz Thomas S. , Choudhary Hemant , Gao Yu , Amer Bashar , Lillington Stephen P. , Leggieri Patrick A. , Brown Jennifer L. , Swift Candice L. , Lipzen Anna , Na Hyunsoo , Amirebrahimi Mojgan , Theodorou Michael K. , Baidoo Edward E. K. , Barry Kerrie , Grigoriev Igor V. , Timokhin Vitaliy I. , Gladden John , Singh Seema , Mortimer Jenny C. , Ralph John , Simmons Blake A. , Singer Steven W. , O’Malley Michelle A. 
Published: March 2023

AbstractLignocellulose forms plant cell walls, and its three constituent polymers, cellulose, hemicellulose and lignin, represent the largest renewable organic carbon pool in the terrestrial biosphere. Insights into biological lignocellulose deconstruction inform understandings of global carbon sequestration dynamics and provide inspiration for biotechnologies seeking to address the current climate crisis by producing renewable chemicals from plant biomass. Organisms in diverse environments disassemble lignocellulose, and carbohydrate degradation processes are well defined, but biological lignin deconstruction is described only in aerobic systems. It is currently unclear whether anaerobic lignin deconstruction is impossible because of biochemical constraints or, alternatively, has not yet been measured. We applied whole cell-wall nuclear magnetic resonance, gel-permeation chromatography and transcriptome sequencing to interrogate the apparent paradox that anaerobic fungi (Neocallimastigomycetes), well-documented lignocellulose degradation specialists, are unable to modify lignin. We find that Neocallimastigomycetes anaerobically break chemical bonds in grass and hardwood lignins, and we further associate upregulated gene products with the observed lignocellulose deconstruction. These findings alter perceptions of lignin deconstruction by anaerobes and provide opportunities to advance decarbonization biotechnologies that depend on depolymerizing lignocellulose.
A hydrogenotrophic Sulfurimonas is globally abundant in deep-sea oxygen-saturated hydrothermal plumes
Authors: Molari Massimiliano , Hassenrueck Christiane , Laso-Pérez Rafael , Wegener Gunter , Offre Pierre , Scilipoti Stefano , Boetius Antje 
Published: March 2023

AbstractMembers of the bacterial genus Sulfurimonas (phylum Campylobacterota) dominate microbial communities in marine redoxclines and are important for sulfur and nitrogen cycling. Here we used metagenomics and metabolic analyses to characterize a Sulfurimonas from the Gakkel Ridge in the Central Arctic Ocean and Southwest Indian Ridge, showing that this species is ubiquitous in non-buoyant hydrothermal plumes at Mid Ocean Ridges across the global ocean. One Sulfurimonas species, USulfurimonas pluma, was found to be globally abundant and active in cold (<0−4 °C), oxygen-saturated and hydrogen-rich hydrothermal plumes. Compared with other Sulfurimonas species, US. pluma has a reduced genome (>17%) and genomic signatures of an aerobic chemolithotrophic metabolism using hydrogen as an energy source, including acquisition of A2-type oxidase and loss of nitrate and nitrite reductases. The dominance and unique niche of US. pluma in hydrothermal plumes suggest an unappreciated biogeochemical role for Sulfurimonas in the deep ocean.
Elucidating dynamic anaerobe metabolism with HRMAS 13C NMR and genome-scale modeling
Authors: Pavao Aidan , Girinathan Brintha , Peltier Johann , Altamirano Silva Pamela , Dupuy Bruno , Muti Isabella H. , Malloy Craig , Cheng Leo L. , Bry Lynn 
Published: March 2023

AbstractAnaerobic microbial metabolism drives critical functions within global ecosystems, host–microbiota interactions, and industrial applications, yet remains ill-defined. Here we advance a versatile approach to elaborate cellular metabolism in obligate anaerobes using the pathogen Clostridioides difficile, an amino acid and carbohydrate-fermenting Clostridia. High-resolution magic angle spinning nuclear magnetic resonance (NMR) spectroscopy of C. difficile, grown with fermentable 13C substrates, informed dynamic flux balance analysis (dFBA) of the pathogen’s genome-scale metabolism. Analyses identified dynamic recruitment of oxidative and supporting reductive pathways, with integration of high-flux amino acid and glycolytic metabolism at alanine’s biosynthesis to support efficient energy generation, nitrogen handling and biomass generation. Model predictions informed an approach leveraging the sensitivity of 13C NMR spectroscopy to simultaneously track cellular carbon and nitrogen flow from [U-13C]glucose and [15N]leucine, confirming the formation of [13C,15N]alanine. Findings identify metabolic strategies used by C. difficile to support its rapid colonization and expansion in gut ecosystems.
Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity
Authors: Wu Jinjian , Fang Yuqi , Tan Xin , Kang Shangyu , Yue Xiaomei , Rao Yawen , Huang Haoming , Liu Mingxia , Qiu Shijun , Yap Pew-Thian 
Published: March 2023

AbstractType 2 diabetes mellitus (T2DM) is closely linked to cognitive decline and alterations in brain structure and function. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to diagnose neurodegenerative diseases, such as cognitive impairment (CI), Alzheimer’s disease (AD), and vascular dementia (VaD). However, whether the functional connectivity (FC) of patients with T2DM and mild cognitive impairment (T2DM-MCI) is conducive to early diagnosis remains unclear. To answer this question, we analyzed the rs-fMRI data of 37 patients with T2DM and mild cognitive impairment (T2DM-MCI), 93 patients with T2DM but no cognitive impairment (T2DM-NCI), and 69 normal controls (NC). We achieved an accuracy of 87.91% in T2DM-MCI versus T2DM-NCI classification and 80% in T2DM-NCI versus NC classification using the XGBoost model. The thalamus, angular, caudate nucleus, and paracentral lobule contributed most to the classification outcome. Our findings provide valuable knowledge to classify and predict T2DM-related CI, can help with early clinical diagnosis of T2DM-MCI, and provide a basis for future studies.
Motor unit number index (MUNIX) loss of 50% occurs in half the time of 50% functional loss according to the D50 disease progression model of ALS
Authors: Ebersbach Theresa , Roediger Annekathrin , Steinbach Robert , Appelfeller Martin , Tuemmler Anke , Stubendorff Beatrice , Axer Hubertus , Witte Otto W. , Grosskreutz Julian 
Published: March 2023

AbstractCapturing disease progression in amyotrophic lateral sclerosis (ALS) is challenging and refinement of progression markers is urgently needed. This study introduces new motor unit number index (MUNIX), motor unit size index (MUSIX) and compound muscle action potential (CMAP) parameters called M50, MUSIX200 and CMAP50. M50 and CMAP50 indicate the time in months from symptom onset an ALS patient needs to lose 50% of MUNIX or CMAP in relation to the mean values of controls. MUSIX200 represents the time in months until doubling of the mean MUSIX of controls. We used MUNIX parameters of Musculi abductor pollicis brevis (APB), abductor digiti minimi (ADM) and tibialis anterior (TA) of 222 ALS patients. Embedded in the D50 disease progression model, disease aggressiveness and accumulation were analyzed separately. M50, CMAP50 and MUSIX200 significantly differed among disease aggressiveness subgroups (p < 0.001) regardless of disease accumulation. ALS patients with a low M50 had a significantly shorter survival compared to high M50 (median 32 versus 74 months). M50 preceded the loss of global function (median of about 14 months). M50, CMAP50 and MUSIX200 characterize the disease course in ALS in a new way and may be applied as early measures of disease progression.
Changes in Internet use patterns among older adults in England from before to after the outbreak of the COVID-19 pandemic
Authors: Kung Claryn S. J. , Steptoe Andrew 
Published: March 2023

AbstractThe COVID-19 pandemic brought about an increased reliance on the Internet for various daily activities. Given the known digital divide, it is important to understand whether older adults changed their Internet use patterns, but current evidence is limited to cross-sectional studies. This study documents changes in frequency and types of Internet use among older adults from before to shortly after the outbreak of the COVID-19 pandemic (2018/2019 to June/July 2020), and the factors predicting regular use during these early days of the pandemic. Using data on 6,840 adults aged 50 + from the nationally representative English Longitudinal Study of Ageing, we apply longitudinal fixed-effects models to examine within-individual changes in Internet use behaviour. There was no change in the likelihood of daily Internet use between 2018/2019 and June/July 2020, despite the increased digitalisation of services over the pandemic. Daily use in June/July 2020 was negatively related to age, neighbourhood deprivation, and loneliness, and positively related to partnership status, education, employment, income, and organisation membership. Using the Internet for making calls and getting information about Government services increased, which was important given the social restrictions and overall uncertainty. However, Internet use for finding health-related information decreased. As the world moves towards digital alternatives post-pandemic, it is important to continually ensure older adults are not at risk of exclusion.
Quantum deep learning by sampling neural nets with a quantum annealer
Authors: Higham Catherine F. , Bedford Adrian 
Published: March 2023

AbstractWe demonstrate the feasibility of framing a classically learned deep neural network as an energy based model that can be processed on a one-step quantum annealer in order to exploit fast sampling times. We propose approaches to overcome two hurdles for high resolution image classification on a quantum processing unit (QPU): the required number and the binary nature of the model states. With this novel method we successfully transfer a pretrained convolutional neural network to the QPU. By taking advantage of the strengths of quantum annealing, we show the potential for classification speedup of at least one order of magnitude.
Decreased Paneth cell α-defensins promote fibrosis in a choline-deficient L-amino acid-defined high-fat diet-induced mouse model of nonalcoholic steatohepatitis via disrupting intestinal microbiota
Authors: Nakamura Shunta , Nakamura Kiminori , Yokoi Yuki , Shimizu Yu , Ohira Shuya , Hagiwara Mizu , Song Zihao , Gan Li , Aizawa Tomoyasu , Hashimoto Daigo , Teshima Takanori , Ouellette Andre J. , Ayabe Tokiyoshi 
Published: March 2023

AbstractNonalcoholic steatohepatitis (NASH) is a chronic liver disease characterized by fibrosis that develops from fatty liver. Disruption of intestinal microbiota homeostasis, dysbiosis, is associated with fibrosis development in NASH. An antimicrobial peptide α-defensin secreted by Paneth cells in the small intestine is known to regulate composition of the intestinal microbiota. However, involvement of α-defensin in NASH remains unknown. Here, we show that in diet-induced NASH model mice, decrease of fecal α-defensin along with dysbiosis occurs before NASH onset. When α-defensin levels in the intestinal lumen are restored by intravenous administration of R-Spondin1 to induce Paneth cell regeneration or by oral administration of α-defensins, liver fibrosis is ameliorated with dissolving dysbiosis. Furthermore, R-Spondin1 and α-defensin improved liver pathologies together with different features in the intestinal microbiota. These results indicate that decreased α-defensin secretion induces liver fibrosis through dysbiosis, further suggesting Paneth cell α-defensin as a potential therapeutic target for NASH.
Carleman linearization approach for chemical kinetics integration toward quantum computation
Authors: Akiba Takaki , Morii Youhi , Maruta Kaoru 
Published: March 2023

AbstractThe Harrow, Hassidim, Lloyd (HHL) algorithm, known as the pioneering algorithm for solving linear equations in quantum computers, is expected to accelerate solving large-scale linear ordinary differential equations (ODEs). To efficiently combine classical and quantum computers for high-cost chemical problems, non-linear ODEs (e.g., chemical reactions) must be linearized to the highest possible accuracy. However, the linearization approach has not been fully established yet. In this study, Carleman linearization was examined to transform nonlinear first-order ODEs of chemical reactions into linear ODEs. Although this linearization theoretically requires the generation of an infinite matrix, the original nonlinear equations can be reconstructed. For the practical use, the linearized system should be truncated with finite size and the extent of the truncation determines analysis precision. Matrix should be sufficiently large so that the precision is satisfied because quantum computers can treat such huge matrix. Our method was applied to a one-variable nonlinear $$\dot{y}=-{y}^{2}$$

y
˙

=
-

y

2

system to investigate the effect of truncation orders and time step sizes on the computational error. Subsequently, two zero-dimensional homogeneous ignition problems for H2–air and CH4–air gas mixtures were solved. The results revealed that the proposed method could accurately reproduce reference data. Furthermore, an increase in the truncation order improved accuracy with large time-step sizes. Thus, our approach can provide accurate numerical simulations rapidly for complex combustion systems.
Design of intelligent module design for humanoid translation robot by combining the deep learning with blockchain technology
Authors: Yang Fan , Deng Jie 
Published: March 2023

AbstractTo accelerate the deep application of deep learning in text data processing, an English statistical translation system is established and applied to the question answering of humanoid robot. Firstly, the model of machine translation based on recursive neural network is implemented. A crawler system is established to collect English movie subtitle data. On this basis, an English subtitle translation system is designed. Then, combined with sentence embedding technology, the Particle Swarm Optimization (PSO) algorithm of meta-heuristic algorithm is adopted to locate the defects of translation software. A translation robot automatic question and answer interactive module is constructed. Additionally, the hybrid recommendation mechanism based on personalized learning is built using blockchain technology. Finally, the performance of translation model and software defect location model is evaluated. The results show that the Recurrent Neural Network (RNN) embedding algorithm has certain effect of word clustering. RNN embedded model has a strong ability to process short sentences. The strongest translated sentences are between 11 and 39 words long, while the weakest translated sentences are between 71 and 79 words long. Therefore, the model must strengthen the processing of long sentences, especially character—level input. The average sentence length is much longer than word-level input. The model based on PSO algorithm shows good accuracy in different data sets. This model averages better performance on Tomcat, standard widget toolkits, and Java development tool datasets than other comparison methods. The average reciprocal rank and average accuracy of the weight combination of PSO algorithm are very high. Moreover, this method is greatly affected by the dimension of the word embedding model, and the 300-dimension word embedding model has the best effect. To sum up, this study proposes a good statistical translation model for humanoid robot English translation, which lays the foundation for intelligent interaction between humanoid robots.
Projection of August 2021 pumice dispersion from the Fukutoku-Oka-no-Ba eruption in the western North Pacific
Authors: Chang Yu-Lin K. , McIntosh Iona M. , Miyama Toru , Miyazawa Yasumasa 
Published: March 2023

AbstractMarine hazards often occur unexpectedly. Long-term (> few weeks) projections are sometimes needed to predict the potential route of drifting targets (e.g. pumice, oil, shipwreck) in order to prevent further disaster, yet reliable long-term forecast data may be unavailable. The present study examined the long-term projection of pumice dispersion originating from the 2021 submarine eruption of Fukutoku-Oka-no-Ba volcano, Japan, based on hindcast reanalysis of the past 28 years of wind and ocean currents using the particle tracking method. The ensemble distribution showed a wide dispersion, which was dominated by the ocean currents. By contrast, wind provided a relatively uniform transport. Apart from the prevailing wind, typhoons also play a role in affecting pumice dispersion. The multi-year simulation provides a general view of pumice dispersion accounting for different uncertainty, which could be used for deducing the potential dispersion under different wind and ocean conditions.
Development of a patient-specific chest computed tomography imaging phantom with realistic lung lesions using silicone casting and three-dimensional printing
Authors: Hong Dayeong , Moon Sojin , Seo Joon Beom , Kim Namkug 
Published: March 2023

AbstractThe validation of the accuracy of the quantification software in computed tomography (CT) images is very challenging. Therefore, we proposed a CT imaging phantom that accurately represents patient-specific anatomical structures and randomly integrates various lesions including disease-like patterns and lesions of various shapes and sizes using silicone casting and three-dimensional (3D) printing. Six nodules of various shapes and sizes were randomly added to the patient’s modeled lungs to evaluate the accuracy of the quantification software. By using silicone materials, CT intensities suitable for the lesions and lung parenchyma were realized, and their Hounsfield unit (HU) values were evaluated on a CT scan of the phantom. As a result, based on the CT scan of the imaging phantom model, the measured HU values for the normal lung parenchyma, each nodule, fibrosis, and emphysematous lesions were within the target value. The measurement error between the stereolithography model and 3D-printing phantoms was 0.2 ± 0.18 mm. In conclusion, the use of 3D printing and silicone casting allowed the application and evaluation of the proposed CT imaging phantom for the validation of the accuracy of the quantification software in CT images, which could be applied to CT-based quantification and development of imaging biomarkers.