bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multidimensional responses of grassland stability to eutrophication
Authors: Chen Qingqing , Wang Shaopeng , Borer Elizabeth T. , Bakker Jonathan D. , Seabloom Eric W. , Harpole W. Stanley , Eisenhauer Nico , Lekberg Ylva , Buckley Yvonne M. , Catford Jane A. , Roscher Christiane , Donohue Ian , Power Sally A. , Daleo Pedro , Ebeling Anne , Knops Johannes M. H. , Martina Jason P. , Eskelinen Anu , Morgan John W. , Risch Anita C. , Caldeira Maria C. , Bugalho Miguel N. , Virtanen Risto , Barrio Isabel C. , Niu Yujie , Jentsch Anke , Stevens Carly J. , Gruner Daniel S. , MacDougall Andrew S. , Alberti Juan , Hautier Yann 
Published: October 2023

AbstractEutrophication usually impacts grassland biodiversity, community composition, and biomass production, but its impact on the stability of these community aspects is unclear. One challenge is that stability has many facets that can be tightly correlated (low dimensionality) or highly disparate (high dimensionality). Using standardized experiments in 55 grassland sites from a globally distributed experiment (NutNet), we quantify the effects of nutrient addition on five facets of stability (temporal invariability, resistance during dry and wet growing seasons, recovery after dry and wet growing seasons), measured on three community aspects (aboveground biomass, community composition, and species richness). Nutrient addition reduces the temporal invariability and resistance of species richness and community composition during dry and wet growing seasons, but does not affect those of biomass. Different stability measures are largely uncorrelated under both ambient and eutrophic conditions, indicating consistently high dimensionality. Harnessing the dimensionality of ecological stability provides insights for predicting grassland responses to global environmental change.
Enhanced joint impact of western hemispheric precursors increases extreme El Niño frequency under greenhouse warming
Authors: Jo Hyun-Su , Ham Yoo-Geun 
Published: October 2023

AbstractSea surface temperature variability over the north tropical Atlantic (NTA) and over the subtropical northeast Pacific (SNP), which is referred to as the North Pacific Meridional Mode, during the early boreal spring is known to trigger El Niño–Southern Oscillation (ENSO) events. The future changes of the influence of those northwestern hemispheric precursors on ENSO are usually examined separately, even though their joint impacts significantly differ from the individual impacts. Here, we show that the impacts of both NTA and SNP on ENSO significantly increase under greenhouse warming and that the degrees of enhancement are closely linked. The wetter mean state over the off-equatorial eastern Pacific is a single contributor that controls the impacts of both NTA and SNP on ENSO. The enhanced joint impacts of the northwestern hemispheric precursors on ENSO increase the occurrences of extreme El Niño events and the ENSO predictability under greenhouse warming.
Chirality-induced avalanche magnetization of magnetite by an RNA precursor
Authors: Ozturk S. Furkan , Bhowmick Deb Kumar , Kapon Yael , Sang Yutao , Kumar Anil , Paltiel Yossi , Naaman Ron , Sasselov Dimitar D. 
Published: October 2023

AbstractHomochirality is a hallmark of life on Earth. To achieve and maintain homochirality within a prebiotic network, the presence of an environmental factor acting as a chiral agent and providing a persistent chiral bias to prebiotic chemistry is highly advantageous. Magnetized surfaces are prebiotically plausible chiral agents due to the chiral-induced spin selectivity (CISS) effect, and they were utilized to attain homochiral ribose-aminooxazoline (RAO), an RNA precursor. However, natural magnetic minerals are typically weakly magnetized, necessitating mechanisms to enhance their magnetization for their use as effective chiral agents. Here, we report the magnetization of magnetic surfaces by crystallizing enantiopure RAO, whereby chiral molecules induce a uniform surface magnetization due to the CISS effect, which spreads across the magnetic surface akin to an avalanche. Chirality-induced avalanche magnetization enables a feedback between chiral molecules and magnetic surfaces, which can amplify a weak magnetization and allow for highly efficient spin-selective processes on magnetic minerals.
HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes
Authors: Gupta Sagar , Nerli Santrupti , Kutti Kandy Sreeja , Mersky Glenn L. , Sgourakis Nikolaos G. 
Published: October 2023

AbstractThe class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within our curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer pHLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work may be applied towards predicting antigen immunogenicity, and receptor cross-reactivity.
Visible light-triggered selective C(sp2)-H/C(sp3)-H coupling of benzenes with aliphatic hydrocarbons
Authors: Li Qian-Yu , Cheng Shiyan , Ye Ziqi , Huang Tao , Yang Fuxing , Lin Yu-Mei , Gong Lei 
Published: October 2023

AbstractThe direct and selective coupling of benzenes with aliphatic hydrocarbons is a promising strategy for C(sp2)-C(sp3) bond formation using readily available starting materials, yet it remains a significant challenge. In this study, we have developed a simplified photochemical system that incorporates catalytic amounts of iron(III) halides as multifunctional reagents and air as a green oxidant to address this synthetic problem. Under mild conditions, the reaction between a strong C(sp2)-H bond and a robust C(sp3)-H bond has been achieved, affording a broad range of cross-coupling products with high yields and commendable chemo-, site-selectivity. The iron halide acts as a multifunctional reagent that responds to visible light, initiates C-centered radicals, induces single-electron oxidation to carbocations, and participates in a subsequent Friedel-Crafts-type process. The gradual release of radical species and carbocation intermediates appears to be critical for achieving desirable reactivity and selectivity. This eco-friendly, cost-efficient approach offers access to various building blocks from abundant hydrocarbon feedstocks, and demonstrates the potential of iron halides in sustainable synthesis.
Symmetric carbon tetramers forming spin qubits in hexagonal boron nitride
Authors: Benedek Zsolt , Babar Rohit , Ganyecz Ádám , Szilvási Tibor , Legeza Örs , Barcza Gergely , Ivády Viktor 
Published: October 2023

AbstractPoint defect quantum bits in semiconductors have the potential to revolutionize sensing at atomic scales. Currently, vacancy-related defects are at the forefront of high spatial resolution and low-dimensional sensing. On the other hand, it is expected that impurity-related defect structures may give rise to new features that could further advance quantum sensing in low dimensions. Here, we study the symmetric carbon tetramer clusters in hexagonal boron nitride and propose them as spin qubits for sensing. We utilize periodic-DFT and quantum chemistry approaches to reliably and accurately predict the electronic, optical, and spin properties of the studied defect. We show that the nitrogen-centered symmetric carbon tetramer gives rise to spin state-dependent optical signals with strain-sensitive intersystem crossing rates. Furthermore, the weak hyperfine coupling of the defect to their spin environments results in a reduced electron spin resonance linewidth that can enhance sensitivity.
Comprehensive analysis of circRNA expression profile and circRNA-miRNA-mRNA network susceptibility to very early-onset schizophrenia
Authors: Huang Huanhuan , Luo Jie , Qi Yanjie , Wu Yuanzhen , Qi Junhui , Yan Xiuping , Xu Gaoyang , He Fan , Zheng Yi 
Published: October 2023

AbstractTo explore the potential role of circular RNAs (circRNAs) in children developing very early-onset schizophrenia (VEOS). Total RNA was extracted from the plasma samples of 10 VEOS patients and eight healthy controls. Expression profiles of circRNAs, micro RNAs (miRNAs), and messenger RNAs (mRNAs) were analyzed using RNA-seq. The interaction networks between miRNAs and targets were predicted using the miRanda tool. A differentially expressed circRNA-miRNA-mRNA (ceRNA) network was further constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the target mRNAs in the ceRNA network were performed to predict the potential functions of their host genes. The patient group and the control group were also compared on the regulatory patterns of circRNAs on mRNAs. 1934 circRNAs were identified from the samples and reported for the first time in schizophrenia. The circRNA expression levels were lower in the VEOS group than in the healthy control group, and 1889 circRNAs were expressed only in the control group. Differential expression analysis (i.e., log2fold change > 1.5, p 0.05) identified 235 circRNAs (1 up-regulated, 234 down-regulated), 11 miRNAs (7 up-regulated, 4 down-regulated), and 2,308 mRNAs (1906 up-regulated, 402 down-regulated) respectively. In VEOS, a ceRNA network with 10 down-regulated circRNA targets, 6 up-regulated miRNAs, and 47 down-regulated mRNAs was constructed. The target genes were involved in the membrane, the signal transduction, and the cytoskeleton and transport pathways. Finally, different expression correlation patterns of circRNA and mRNA in the network were observed between the patient group and the control group. The current research is the first to reveal the differentially expressed circRNAs in the plasma of VEOS patients. A circRNA-miRNA-mRNA network was also conducted in this study. It may be implied that the circRNAs in this network are potential diagnostic biomarkers for VEOS and they play an important role in the onset and development of VEOS symptoms.
The 10-m cotton maps in Xinjiang, China during 2018–2021
Authors: Kang Xiaoyan , Huang Changping , Chen Jing M. , Lv Xin , Wang Jin , Zhong Tao , Wang Huihan , Fan Xianglong , Ma Yiru , Yi Xiang , Zhang Ze , Zhang Lifu , Tong Qingxi 
Published: October 2023

AbstractCotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e., cropland mapping followed by cotton extraction, was employed to improve the accuracy and efficiency of cotton mapping for a large region of about 1.66 million km2with high heterogeneity. Additionally, the time-series satellite data related to spectral, textural, structural, and phenological features were combined and used in a supervised random forest classifier. The cotton/non-cotton classification model achieved overall accuracies of about 95% and 90% on the test samples of the same and adjacent years, respectively. The proposed two-step cotton mapping strategy proved promising and effective in producing multi-year and consistent cotton maps. XJ_COTTON10 agreed well with the statistical areas of cotton at the county level (R2 = 0.84–0.94). This is the first cotton mapping for the entire Xinjiang at 10-meter resolution, which can provide a basis for high-precision cotton monitoring and policymaking in China.
Measurements of aerosol microphysical and chemical properties in the central Arctic atmosphere during MOSAiC
Authors: Heutte Benjamin , Bergner Nora , Beck Ivo , Angot Hélène , Dada Lubna , Quéléver Lauriane L. J. , Laurila Tiia , Boyer Matthew , Brasseur Zoé , Daellenbach Kaspar R. , Henning Silvia , Kuang Chongai , Kulmala Markku , Lampilahti Janne , Lampimäki Markus , Petäjä Tuukka , Shupe Matthew D. , Sipilä Mikko , Uin Janek , Jokinen Tuija , Schmale Julia 
Published: October 2023

AbstractThe Arctic environment is transforming rapidly due to climate change. Aerosols’ abundance and physicochemical characteristics play a crucial, yet uncertain, role in these changes due to their influence on the surface energy budget through direct interaction with solar radiation and indirectly via cloud formation. Importantly, Arctic aerosol properties are also changing in response to climate change. Despite their importance, year-round measurements of their characteristics are sparse in the Arctic and often confined to lower latitudes at Arctic land-based stations and/or short high-latitude summertime campaigns. Here, we present unique aerosol microphysics and chemical composition datasets collected during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, in the central Arctic. These datasets, which include aerosol particle number concentrations, size distributions, cloud condensation nuclei concentrations, fluorescent aerosol concentrations and properties, and aerosol bulk chemical composition (black carbon, sulfate, nitrate, ammonium, chloride, and organics) will serve to improve our understanding of high-Arctic aerosol processes, with relevance towards improved modelling of the future Arctic (and global) climate.
HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data
Authors: Tran Khuong H. , Zhang Xiaoyang , Ye Yongchang , Shen Yu , Gao Shuai , Liu Yuxia , Richardson Andrew 
Published: October 2023

AbstractLand surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.
Thermally-robust spatiotemporal parallel reservoir computing by frequency filtering in frustrated magnets
Authors: Kobayashi Kaito , Motome Yukitoshi 
Published: October 2023

AbstractPhysical reservoir computing is a framework for brain-inspired information processing that utilizes nonlinear and high-dimensional dynamics in non-von-Neumann systems. In recent years, spintronic devices have been proposed for use as physical reservoirs, but their practical application remains a major challenge, mainly because thermal noise prevents them from retaining short-term memory, the essence of neuromorphic computing. Here, we propose a framework for spintronic physical reservoirs that exploits frequency domain dynamics in interacting spins. Through the effective use of frequency filters, we demonstrate, for a model of frustrated magnets, both robustness to thermal fluctuations and feasibility of frequency division multiplexing. This scheme can be coupled with parallelization in spatial domain even down to the level of a single spin, yielding a vast number of spatiotemporal computational units. Furthermore, the nonlinearity via the exchange interaction allows information processing among different frequency threads. Our findings establish a design principle for high-performance spintronic reservoirs with the potential for highly integrated devices.
Potential chemopreventive effects of Broccoli extract supplementation against 7, 12 dimethyl Benz(a)anthracene (DMBA) -induced toxicity in female rats
Authors: Allam Aya M. , AbuBakr Huda O. , Yassin Aya M. , Abdel-Razek Ahmed S. , Khattab Marwa S. , Gouda Eman M. , Mousa Said Z. 
Published: October 2023

AbstractDietary components have recently received rapidly expanding attention for their potential to halt or reverse the development of many oxidative stress-mediated diseases after exposure to environmental toxicants. 7, 12 dimethylbenz(a)anthracene (DMBA) is one of the most common environmental pollutants. The present study aimed to evaluate the chemo-preventive effects of broccoli as a nutritional component against DMBA intoxication in rats. A daily dose of aqueous (1 ml/rat) and methanolic (150 mg/kg) broccoli extracts, respectively, was given to 50-day-old female rats for 26 successive weeks after carcinogen intoxication with a single dose of 20 mg/ml of DMBA. DMBA intoxication resulted in a redox imbalance (a decreased GSH level and an increased MDA level) and increased DNA fragmentation in the liver, kidney, and brain. Besides, it affected the level of expression of the bcl2 gene in the liver, kidney, and brain tissue but didn’t affect cfos gene expression accompanied by histopathological changes. The aqueous and methanolic broccoli extract supplements ameliorated the adverse effects by increasing the level of GSH, decreasing the MDA level, and reducing DNA fragmentation. Besides, broccoli extracts decreased the expression of bcl2 in the liver and brain and up-regulated bcl2 expression in the kidney, accompanied by lowering NF-κβ 65 expression in the liver and brain and γ-catenin expression in the liver and kidney. In conclusion, broccoli as a dietary component had a strong chemoprotective effect against oxidative stress, DNA damage, and genotoxicity induced by DMBA intoxication in rats.
Photodynamic reactions using high-intensity red LED promotes gingival wound healing by ROS induction
Authors: Minagawa Emika , Yamauchi Nobuhiro , Taguchi Yoichiro , Umeda Makoto 
Published: October 2023

AbstractPhotodynamic therapy is a treatment that combines a light source with a photosensitizer. LEDs have attracted considerable attention in clinical dentistry because they are inexpensive and safe to use. Although the interaction between photosensitizers and LEDs in dental practice is effective for treating periodontal disease by killing periodontopathic bacteria, little is known about the effects of LEDs on human gingival fibroblasts (HGnFs), which play an important role in gingival wound healing. In this study, we investigated the effects of high-intensity red LED irradiation on HGnFs after the addition of methylene blue (MB), one of the least harmful photosensitizers, on wound healing and reactive oxygen species (ROS) production induced by photodynamic reactions. We found that irradiation of MB with high-intensity red LED at controlled energy levels promoted cell proliferation, migration, and production of wound healing factors. Furthermore, ROS production by a photodynamic reaction enabled the translocation of phosphorylated Grb2-associated binder-1, activating Extracellular signal-regulated kinase 1/2 and c-Jun N-terminal kinase signals. Our findings suggest that proper control of ROS production has a beneficial effect on gingival fibroblasts, which constitute periodontal tissue, from the perspective of gingival wound healing.
Interacting innovation processes
Authors: Aletti Giacomo , Crimaldi Irene , Ghiglietti Andrea 
Published: October 2023

AbstractIn this work, we introduce a general model for a collection of innovation processes in order to model and analyze the interaction among them. We provide theoretical results, analytically proven, and we show how the proposed model fits the behaviors observed in some real data sets (from Reddit and Gutenberg). It is worth mentioning that the given applications are only examples of the potentialities of the proposed model and related results: due to its abstractness and generality, it can be applied to many interacting innovation processes.
Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits
Authors: Dort E. N. , Layne E. , Feau N. , Butyaev A. , Henrissat B. , Martin F. M. , Haridas S. , Salamov A. , Grigoriev I. V. , Blanchette M. , Hamelin R. C. 
Published: October 2023

AbstractInvasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities.