bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sphingosine kinase 1 promotes tumor immune evasion by regulating the MTA3-PD-L1 axis
Authors: Lau Poyee , Zhang Guanxiong , Zhao Shuang , Liang Long , Zhang Hailun , Zhou Guowei , Hung Mien-Chie , Chen Xiang , Liu Hong 
Published: September 2022

AbstractImmune checkpoint blockade (ICB) exhibits considerable benefits in malignancies, but its overall response rate is limited. Previous studies have shown that sphingosine kinases (SPHKs) are critical in the tumor microenvironment (TME), but their role in immunotherapy is unclear. We performed integrative analyses including bioinformatics analysis, functional study, and clinical validation to investigate the role of SPHK1 in tumor immunity. Functionally, we demonstrated that the inhibition of SPHK1 significantly suppressed tumor growth by promoting antitumor immunity in immunocompetent melanoma mouse models and tumor T-cell cocultures. A mechanistic analysis revealed that MTA3 functions as the downstream target of SPHK1 in transcriptionally regulating tumor PD-L1. Preclinically, we found that anti-PD-1 monoclonal antibody (mAb) treatment significantly rescued tumor SPHK1 overexpression or tumor MTA3 overexpression-mediated immune evasion. Significantly, we identified SPHK1 and MTA3 as biological markers for predicting the efficacy of anti-PD-1 mAb therapy in melanoma patients. Our findings revealed a novel role for SPHK1 in tumor evasion mediated by regulating the MTA3-PD-L1 axis, identified SPHK1 and MTA3 as predictors for assessing the efficacy of PD-1 mAb treatment, and provided a therapeutic possibility for the treatment of melanoma patients.
Multi-omics analysis defines highly refractory RAS burdened immature subgroup of infant acute lymphoblastic leukemia
Authors: Isobe Tomoya , Takagi Masatoshi , Sato-Otsubo Aiko , Nishimura Akira , Nagae Genta , Yamagishi Chika , Tamura Moe , Tanaka Yosuke , Asada Shuhei , Takeda Reina , Tsuchiya Akiho , Wang Xiaonan , Yoshida Kenichi , Nannya Yasuhito , Ueno Hiroo , Akazawa Ryo , Kato Itaru , Mikami Takashi , Watanabe Kentaro , Sekiguchi Masahiro , Seki Masafumi , Kimura Shunsuke , Hiwatari Mitsuteru , Kato Motohiro , Fukuda Shiro , Tatsuno Kenji , Tsutsumi Shuichi , Kanai Akinori , Inaba Toshiya , Shiozawa Yusuke , Shiraishi Yuichi , Chiba Kenichi , Tanaka Hiroko , Kotecha Rishi S. , Cruickshank Mark N. , Ishikawa Fumihiko , Morio Tomohiro , Eguchi Mariko , Deguchi Takao , Kiyokawa Nobutaka , Arakawa Yuki , Koh Katsuyoshi , Aoki Yuki , Ishihara Takashi , Tomizawa Daisuke , Miyamura Takako , Ishii Eiichi , Mizutani Shuki , Wilson Nicola K. , Göttgens Berthold , Miyano Satoru , Kitamura Toshio , Goyama Susumu , Yokoyama Akihiko , Aburatani Hiroyuki , Ogawa Seishi , Takita Junko 
Published: September 2022

AbstractKMT2A-rearranged infant acute lymphoblastic leukemia (ALL) represents the most refractory type of childhood leukemia. To uncover the molecular heterogeneity of this disease, we perform RNA sequencing, methylation array analysis, whole exome and targeted deep sequencing on 84 infants with KMT2A-rearranged leukemia. Our multi-omics clustering followed by single-sample and single-cell inference of hematopoietic differentiation establishes five robust integrative clusters (ICs) with different master transcription factors, fusion partners and corresponding stages of B-lymphopoietic and early hemato-endothelial development: IRX-type differentiated (IC1), IRX-type undifferentiated (IC2), HOXA-type MLLT1 (IC3), HOXA-type MLLT3 (IC4), and HOXA-type AFF1 (IC5). Importantly, our deep mutational analysis reveals that the number of RAS pathway mutations predicts prognosis and that the most refractory subgroup of IC2 possesses 100% frequency and the heaviest burden of RAS pathway mutations. Our findings highlight the previously under-appreciated intra- and inter-patient heterogeneity of KMT2A-rearranged infant ALL and provide a rationale for the future development of genomics-guided risk stratification and individualized therapy.
Deep learning for twelve hour precipitation forecasts
Authors: Espeholt Lasse , Agrawal Shreya , Sønderby Casper , Kumar Manoj , Heek Jonathan , Bromberg Carla , Gazen Cenk , Carver Rob , Andrychowicz Marcin , Hickey Jason , Bell Aaron , Kalchbrenner Nal 
Published: September 2022

AbstractExisting weather forecasting models are based on physics and use supercomputers to evolve the atmosphere into the future. Better physics-based forecasts require improved atmospheric models, which can be difficult to discover and develop, or increasing the resolution underlying the simulation, which can be computationally prohibitive. An emerging class of weather models based on neural networks overcome these limitations by learning the required transformations from data instead of relying on hand-coded physics and by running efficiently in parallel. Here we present a neural network capable of predicting precipitation at a high resolution up to 12 h ahead. The model predicts raw precipitation targets and outperforms for up to 12 h of lead time state-of-the-art physics-based models currently operating in the Continental United States. The results represent a substantial step towards validating the new class of neural weather models.
A compendium of 32,277 metagenome-assembled genomes and over 80 million genes from the early-life human gut microbiome
Authors: Zeng Shuqin , Patangia Dhrati , Almeida Alexandre , Zhou Zhemin , Mu Dezhi , Paul Ross R. , Stanton Catherine , Wang Shaopu 
Published: September 2022

AbstractAge-specific reference genomes of the human gut microbiome can provide higher resolution for metagenomic analyses including taxonomic classification, strain-level genomic investigation and functional characterization. We present the Early-Life Gut Genomes (ELGG) catalog with 32,277 genomes representing 2172 species from 6122 fecal metagenomes collected from children under 3 years old spanning delivery mode, gestational age, feeding pattern, and geography. The ELGG substantially expanded the phylogenetic diversity by 38% over the isolate microbial genomes, and the genomic landscape of the early-life microbiome by increasing recruitment of metagenomic reads to 82.8%. More than 60% of the ELGG species lack an isolate representative. The conspecific genomes of the most abundant species from children differed in gene diversity and functions compared to adults. The ELGG genomes encode over 80 million protein sequences, forming the Early-Life Gut Proteins (ELGP) catalog with over four million protein clusters, 29.5% of which lacked functional annotations. The ELGG and ELGP references provided new insights into the early-life human gut microbiome and will facilitate studies to understand the development and mechanisms of disturbances of the human gut microbiome in early life.
Critical ionic transport across an oxygen-vacancy ordering transition
Authors: Lim Ji Soo , Nahm Ho-Hyun , Campanini Marco , Lee Jounghee , Kim Yong-Jin , Park Heung-Sik , Suh Jeonghun , Jung Jun , Yang Yongsoo , Koo Tae Yeong , Rossell Marta D. , Kim Yong-Hyun , Yang Chan-Ho 
Published: September 2022

AbstractPhase transition points can be used to critically reduce the ionic migration activation energy, which is important for realizing high-performance electrolytes at low temperatures. Here, we demonstrate a route toward low-temperature thermionic conduction in solids, by exploiting the critically lowered activation energy associated with oxygen transport in Ca-substituted bismuth ferrite (Bi1-xCaxFeO3-δ) films. Our demonstration relies on the finding that a compositional phase transition occurs by varying Ca doping ratio across xCa ≃ 0.45 between two structural phases with oxygen-vacancy channel ordering along <100> or <110> crystal axis, respectively. Regardless of the atomic-scale irregularity in defect distribution at the doping ratio, the activation energy is largely suppressed to 0.43 eV, compared with ~0.9 eV measured in otherwise rigid phases. From first-principles calculations, we propose that the effective short-range attraction between two positively charged oxygen vacancies sharing lattice deformation not only forms the defect orders but also suppresses the activation energy through concerted hopping.
Bacteria loaded with glucose polymer and photosensitive ICG silicon-nanoparticles for glioblastoma photothermal immunotherapy
Authors: Sun Rong , Liu Mingzhu , Lu Jianping , Chu Binbin , Yang Yunmin , Song Bin , Wang Houyu , He Yao 
Published: September 2022

AbstractBacteria can bypass the blood-brain barrier (BBB), suggesting the possibility of employment of bacteria for combating central nervous system diseases. Herein, we develop a bacteria-based drug delivery system for glioblastoma (GBM) photothermal immunotherapy. The system, which we name as ‘Trojan bacteria’, consists of bacteria loaded with glucose polymer and photosensitive ICG silicon-nanoparticles. In an orthotopic GBM mouse model, we demonstrate that the intravenously injected bacteria bypass the BBB, targeting and penetrating GBM tissues. Upon 808 nm-laser irradiation, the photothermal effects produced by ICG allow the destruction of bacterial cells and the adjacent tumour cells. Furthermore, the bacterial debris as well as the tumour-associated antigens promote antitumor immune responses that prolong the survival of GBM-bearing mice. Moreover, we demonstrate the residual bacteria are effectively eliminated from the body, supporting the potential therapeutic use of this system.
Percolation transition prescribes protein size-specific barrier to passive transport through the nuclear pore complex
Authors: Winogradoff David , Chou Han-Yi , Maffeo Christopher , Aksimentiev Aleksei 
Published: September 2022

AbstractNuclear pore complexes (NPCs) control biomolecular transport in and out of the nucleus. Disordered nucleoporins in the complex’s pore form a permeation barrier, preventing unassisted transport of large biomolecules. Here, we combine coarse-grained simulations of experimentally derived NPC structures with a theoretical model to determine the microscopic mechanism of passive transport. Brute-force simulations of protein transport reveal telegraph-like behavior, where prolonged diffusion on one side of the NPC is interrupted by rapid crossings to the other. We rationalize this behavior using a theoretical model that reproduces the energetics and kinetics of permeation solely from statistics of transient voids within the disordered mesh. As the protein size increases, the mesh transforms from a soft to a hard barrier, enabling orders-of-magnitude reduction in permeation rate for proteins beyond the percolation size threshold. Our model enables exploration of alternative NPC architectures and sets the stage for uncovering molecular mechanisms of facilitated nuclear transport.
Determining the credibility of commitments in international climate policy
Authors: Victor David G. , Lumkowsky Marcel , Dannenberg Astrid 
Published: September 2022

AbstractThe Paris Agreement on climate change aims to improve cooperation by allowing governments to set their own commitments. Its success hinges on whether governments and investors believe those national commitments. To assess credibility, we interrogate a large novel sample of climate policy elites with decades of experience and well-placed to evaluate whether nations’ policy pledges are aligned with what they are politically and administratively able to implement. This expert assessment reveals that countries making the boldest pledges are also making the most credible pledges, contrasting theoretical warnings of a trade-off between ambition and credibility. We find that the quality of national political institutions is the largest explanator of the variation in credibility, and Europe’s credibility is exceptionally high. We also find that economic factors, such as the costs and benefits of controlling emissions, are statistically unimportant in explaining the credibility of national pledges to cooperate.
A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices
Authors: Gashi Shkurta , Min Chulhong , Montanari Alessandro , Santini Silvia , Kawsar Fahim 
Published: September 2022

AbstractWe present a multi-device and multi-modal dataset, called WEEE, collected from 17 participants while they were performing different physical activities. WEEE contains: (1) sensor data collected using seven wearable devices placed on four body locations (head, ear, chest, and wrist); (2) respiratory data collected with an indirect calorimeter serving as ground-truth information; (3) demographics and body composition data (e.g., fat percentage); (4) intensity level and type of physical activities, along with their corresponding metabolic equivalent of task (MET) values; and (5) answers to questionnaires about participants’ physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations, we believe that the dataset will enable the development of novel human energy expenditure (EE) estimation techniques for a diverse set of application scenarios. EE refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. A reliable estimate of people’s EE thus enables computing systems to make inferences about users’ physical activity and help them promoting a healthier lifestyle.
Carbon Monitor Cities near-real-time daily estimates of CO2 emissions from 1500 cities worldwide
Authors: Huo Da , Huang Xiaoting , Dou Xinyu , Ciais Philippe , Li Yun , Deng Zhu , Wang Yilong , Cui Duo , Benkhelifa Fouzi , Sun Taochun , Zhu Biqing , Roest Geoffrey , Gurney Kevin R. , Ke Piyu , Guo Rui , Lu Chenxi , Lin Xiaojuan , Lovell Arminel , Appleby Kyra , DeCola Philip L. , Davis Steven J. , Liu Zhu 
Published: September 2022

AbstractBuilding on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions, Carbon Monitor Cities, which provides daily estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP-ICLEI Track) were performed, and we estimate the overall annual uncertainty range to be ±21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries.
Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX
Authors: Pan Huaqin , Bakalov Vesselina , Cox Lisa , Engle Michelle L. , Erickson Stephen W. , Feolo Michael , Guo Yuelong , Huggins Wayne , Hwang Stephen , Kimura Masato , Krzyzanowski Michelle , Levy Josh , Phillips Michael , Qin Ying , Williams David , Ramos Erin M. , Hamilton Carol M. 
Published: September 2022

AbstractIdentifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.
Modulatory effect of Gracilaria gracilis on European seabass gut microbiota community and its functionality
Authors: Gonçalves Ana Teresa , Simões Marco , Costa Cátia , Passos Ricardo , Baptista Teresa 
Published: September 2022

AbstractSeaweeds are an important source of nutrients and bioactive compounds and have a high potential as health boosters in aquaculture. This study evaluated the effect of dietary inclusion of Gracilaria gracilis biomass or its extract on the European seabass (Dicentrarchus labrax) gut microbial community. Juvenile fish were fed a commercial-like diet with 2.5% or 5% seaweed biomass or 0.35% seaweed extract for 47 days. The gut microbiome was assessed by 16S rRNA amplicon sequencing, and its diversity was not altered by the seaweed supplementation. However, a reduction in Proteobacteria abundance was observed. Random forest analysis highlighted the genera Photobacterium, Staphylococcus, Acinetobacter, Micrococcus and Sphingomonas, and their abundances were reduced when fish were fed diets with algae. SparCC correlation network analysis suggested several mutualistic and other antagonistic relationships that could be related to the predicted altered functions. These pathways were mainly related to the metabolism and biosynthesis of protective compounds such as ectoine and were upregulated in fish fed diets supplemented with algae. This study shows the beneficial potential of Gracilaria as a functional ingredient through the modulation of the complex microbial network towards fish health improvement.
Variations of follicular fluid extracellular vesicles miRNAs content in relation to development stage and season in buffalo
Authors: Capra Emanuele , Kosior Michal Andrzej , Cocchia Natascia , Lazzari Barbara , Del Prete Chiara , Longobardi Valentina , Pizzi Flavia , Stella Alessandra , Frigerio Roberto , Cretich Marina , Consiglio Anna Lange , Gasparrini Bianca 
Published: September 2022

AbstractIn buffalo (Bubalus bubalis) reproductive seasonality, causing cycles of milk production, is one of the major factors affecting farming profitability. Follicular fluid (FF) contains extracellular vesicles (EVs) playing an important role in modulating oocyte developmental competence and carrying microRNAs (miRNAs) essential for in vitro fertilization outcomes. The aim of this work was to characterize the FF-EVs-miRNA cargo of antral (An) and preovulatory (pO) follicles collected in the breeding (BS) and non-breeding (NBS) seasons, to unravel the molecular causes of the reduced oocyte competence recorded in buffalo during the NBS. In total, 1335 miRNAs (538 known Bos taurus miRNAs, 324 homologous to known miRNAs from other species and 473 new candidate miRNAs) were found. We identified 413 differentially expressed miRNAs (DE-miRNAs) (FDR < 0.05) between An and pO groups. A subset of the most significant DE-miRNAs between An and pO groups targets genes which function is related to the lipid and steroid metabolism, response to glucocorticoid and oestradiol stimulus. Comparison between BS and NBS showed 14 and 12 DE-miRNAs in An-FF-EVs and pO-FF-EVs, which regulate IL6 release and cellular adhesion, respectively. In conclusion, these results demonstrated that the miRNA cargo of buffalo FF-EVs varies in relation to both follicular development and season.
Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system
Authors: Lee Jeong-Eun , Kim Ju Hwan , Bae Ji-Hwan , Song Inmyung , Shin Ju-Young 
Published: September 2022

AbstractThere has been a growing attention on using machine learning (ML) in pharmacovigilance. This study aimed to investigate the utility of supervised ML algorithms on timely detection of safety signals in the Korea Adverse Event Reporting System (KAERS), using infliximab as a case drug, between 2009 and 2018. Input data set for ML training was constructed based on the drug label information and spontaneous reports in the KAERS. Gold standard dataset containing known AEs was randomly divided into the training and test sets. Two supervised ML algorithms (gradient boosting machine [GBM], random forest [RF]) were fitted with hyperparameters tuned on the training set by using a fivefold validation. Then, we stratified the KAERS data by calendar year to create 10 cumulative yearly datasets, in which ML algorithms were applied to detect five pre-specified AEs of infliximab identified during post-marketing surveillance. Four AEs were detected by both GBM and RF in the first year they appeared in the KAERS and earlier than they were updated in the drug label of infliximab. We further applied our models to data retrieved from the US Food and Drug Administration Adverse Event Reporting System repository and found that they outperformed existing disproportionality methods. Both GBM and RF demonstrated reliable performance in detecting early safety signals and showed promise for applying such approaches to pharmacovigilance.
In vivo multiphoton multiparametric 3D quantification of human skin aging on forearm and face
Authors: Pena Ana-Maria , Baldeweck Thérèse , Decencière Etienne , Koudoro Serge , Victorin Steeve , Raynaud Edouard , Ngo Blandine , Bastien Philippe , Brizion Sébastien , Tancrède-Bohin Emmanuelle 
Published: September 2022

AbstractQuantifying skin aging changes and characterizing its 3D structure and function in a non-invasive way is still a challenging area of research, constantly evolving with the development of imaging methods and image analysis tools. In vivo multiphoton imaging offers means to assess skin constituents in 3D, however prior skin aging studies mostly focused on 2D analyses of dermal fibers through their signals’ intensities or densities. In this work, we designed and implemented multiphoton multiparametric 3D quantification tools for in vivo human skin pigmentation and aging characterization. We first demonstrated that despite the limited field of view of the technic, investigation of 2 regions of interest (ROIs) per zone per volunteer is a good compromise in assessing 3D skin constituents in both epidermis and superficial dermis. We then characterized skin aging on different UV exposed areas—ventral and dorsal forearms, face. The three major facts of aging that are epidermal atrophy, the dermal–epidermal junction (DEJ) flattening and dermal elastosis can be non-invasively quantified and compared. Epidermal morphological changes occur late and were only objectified between extreme age groups. Melanin accumulation in suprabasal layers with age and chronic exposure on ventral and dorsal forearms is less known and appears earlier. Superficial dermal aging changes are mainly elastin density increase, with no obvious change in collagen density, reflected by SHGto2PEF ratio and SAAID index decrease and ImbrN index increase on all skin areas. Analysis of the z-dermal distribution of these parameters highlighted the 2nd 20 µm thickness normalized dermal sub-layer, that follows the DEJ shape, as exhibiting the highest aging differences. Moreover, the 3D ImbrN index allows refining the share of photoaging in global aging on face and the 3D SAAID index on forearm, which elastin or fibrillar collagens densities alone do not allow. Photoaging of the temple area evolves as a function of chronic exposure with a more pronounced increase in elastin density, also structurally modified from thin and straight elastic fibers in young volunteers to dense and compact pattern in older ones. More generally, multiphoton multiparametric 3D skin quantification offers rich spatial information of interest in assessing normal human skin condition and its pathological, external environment or product induced changes.