bluuebunny/crossref_metadata_2025_split huggingface dataset:
Overcoming global inequality is critical for land-based mitigation in line with the Paris Agreement
Authors: Humpenöder Florian , Popp Alexander , Schleussner Carl-Friedrich , Orlov Anton , Windisch Michael Gregory , Menke Inga , Pongratz Julia , Havermann Felix , Thiery Wim , Luo Fei , v. Jeetze Patrick , Dietrich Jan Philipp , Lotze-Campen Hermann , Weindl Isabelle , Lejeune Quentin 
Published: December 2022

AbstractTransformation pathways for the land sector in line with the Paris Agreement depend on the assumption of globally implemented greenhouse gas (GHG) emission pricing, and in some cases also on inclusive socio-economic development and sustainable land-use practices. In such pathways, the majority of GHG emission reductions in the land system is expected to come from low- and middle-income countries, which currently account for a large share of emissions from agriculture, forestry and other land use (AFOLU). However, in low- and middle-income countries the economic, financial and institutional barriers for such transformative changes are high. Here, we show that if sustainable development in the land sector remained highly unequal and limited to high-income countries only, global AFOLU emissions would remain substantial throughout the 21st century. Our model-based projections highlight that overcoming global inequality is critical for land-based mitigation in line with the Paris Agreement. While also a scenario purely based on either global GHG emission pricing or on inclusive socio-economic development would achieve the stringent emissions reductions required, only the latter ensures major co-benefits for other Sustainable Development Goals, especially in low- and middle-income regions.
Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding
Authors: Melbaum Svenja , Russo Eleonora , Eriksson David , Schneider Artur , Durstewitz Daniel , Brox Thomas , Diester Ilka 
Published: December 2022

AbstractOur knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements that are strictly controlled in experimental settings. It remains unclear how results can be carried over to less constrained behavior like that of freely moving subjects. Toward this goal, we developed a self-paced behavioral paradigm that encouraged rats to engage in different movement types. We employed bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking. These techniques revealed behavioral coupling of neurons with lateralization and an anterior–posterior gradient from the premotor to the primary sensory cortex. The structure of population activity patterns was conserved across animals despite the severe under-sampling of the total number of neurons and variations in electrode positions across individuals. We demonstrated cross-subject and cross-session generalization in a decoding task through alignments of low-dimensional neural manifolds, providing evidence of a conserved neuronal code.
N6-methyladenosine RNA modification regulates photosynthesis during photodamage in plants
Authors: Zhang Man , Zeng Yunping , Peng Rong , Dong Jie , Lan Yelin , Duan Sujuan , Chang Zhenyi , Ren Jian , Luo Guanzheng , Liu Bing , Růžička Kamil , Zhao Kewei , Wang Hong-Bin , Jin Hong-Lei 
Published: December 2022

AbstractN6-methyladenosine (m6A) modification of mRNAs affects many biological processes. However, the function of m6A in plant photosynthesis remains unknown. Here, we demonstrate that m6A modification is crucial for photosynthesis during photodamage caused by high light stress in plants. The m6A modification levels of numerous photosynthesis-related transcripts are changed after high light stress. We determine that the Arabidopsis m6A writer VIRILIZER (VIR) positively regulates photosynthesis, as its genetic inactivation drastically lowers photosynthetic activity and photosystem protein abundance under high light conditions. The m6A levels of numerous photosynthesis-related transcripts decrease in vir mutants, extensively reducing their transcript and translation levels, as revealed by multi-omics analyses. We demonstrate that VIR associates with the transcripts of genes encoding proteins with functions related to photoprotection (such as HHL1, MPH1, and STN8) and their regulatory proteins (such as regulators of transcript stability and translation), promoting their m6A modification and maintaining their stability and translation efficiency. This study thus reveals an important mechanism for m6A-dependent maintenance of photosynthetic efficiency in plants under high light stress conditions.
A dish-like molecular architecture for dynamic ultralong room-temperature phosphorescence through reversible guest accommodation
Authors: Li Wenlang , Huang Qiuyi , Mao Zhu , He Xiaoyi , Ma Dongyu , Zhao Juan , Lam Jacky W. Y. , Zhang Yi , Tang Ben Zhong , Chi Zhenguo 
Published: December 2022

AbstractDeveloping dynamic organic ultralong room-temperature phosphorescent (URTP) materials is of practical importance in various applications but remains a challenge due to the difficulty in manipulating aggregate structures. Herein, we report a dish-like molecular architecture via a bottom-up way, featuring guest-responsive dynamic URTP. Through controlling local fragment motions in the molecular architecture, fascinating dynamic URTP performances can be achieved in response to reversible accommodation of various guests, including solvents, alkyl bromides and even carbon dioxide. Large-scale regulations of phosphorescence lifetime (100-fold) and intensity (10-fold) can be realized, presenting a maximum phosphorescence efficiency and lifetime of 78.8% and 483.1 ms, respectively. Moreover, such a dish-like molecular architecture is employed for temperature-dependent multiple information encryption and visual identification of linear alkyl bromides. This work can not only deepen our understanding to construct multifunctional organic aggregates, but also facilitate the design of high-performance dynamic URTP materials and enrich their practical applications.
Nascent peptide-induced translation discontinuation in eukaryotes impacts biased amino acid usage in proteomes
Authors: Ito Yosuke , Chadani Yuhei , Niwa Tatsuya , Yamakawa Ayako , Machida Kodai , Imataka Hiroaki , Taguchi Hideki 
Published: December 2022

AbstractRobust translation elongation of any given amino acid sequence is required to shape proteomes. Nevertheless, nascent peptides occasionally destabilize ribosomes, since consecutive negatively charged residues in bacterial nascent chains can stochastically induce discontinuation of translation, in a phenomenon termed intrinsic ribosome destabilization (IRD). Here, using budding yeast and a human factor-based reconstituted translation system, we show that IRD also occurs in eukaryotic translation. Nascent chains enriched in aspartic acid (D) or glutamic acid (E) in their N-terminal regions alter canonical ribosome dynamics, stochastically aborting translation. Although eukaryotic ribosomes are more robust to ensure uninterrupted translation, we find many endogenous D/E-rich peptidyl-tRNAs in the N-terminal regions in cells lacking a peptidyl-tRNA hydrolase, indicating that the translation of the N-terminal D/E-rich sequences poses an inherent risk of failure. Indeed, a bioinformatics analysis reveals that the N-terminal regions of ORFs lack D/E enrichment, implying that the translation defect partly restricts the overall amino acid usage in proteomes.
Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics
Authors: Wang Tianyu , Meng Jialin , Zhou Xufeng , Liu Yue , He Zhenyu , Han Qi , Li Qingxuan , Yu Jiajie , Li Zhenhai , Liu Yongkai , Zhu Hao , Sun Qingqing , Zhang David Wei , Chen Peining , Peng Huisheng , Chen Lin 
Published: December 2022

AbstractNeuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.
TRPC channels blockade abolishes endotoxemic cardiac dysfunction by hampering intracellular inflammation and Ca2+ leakage
Authors: Tang Na , Tian Wen , Ma Guang-Yuan , Xiao Xiong , Zhou Lei , Li Ze-Zhi , Liu Xiao-Xiao , Li Chong-Yao , Wu Ke-Han , Liu Wenjuan , Wang Xue-Ying , Gao Yuan-Yuan , Yang Xin , Qi Jianzhao , Li Ding , Liu Yang , Chen Wen-Sheng , Gao Jinming , Li Xiao-Qiang , Cao Wei 
Published: December 2022

AbstractIntracellular Ca2+ dysregulation is a key marker in septic cardiac dysfunction; however, regulation of the classic Ca2+ regulatory modules cannot successfully abolish this symptom. Here we show that the knockout of transient receptor potential canonical (TRPC) channel isoforms TRPC1 and TRPC6 can ameliorate LPS-challenged heart failure and prolong survival in mice. The LPS-triggered Ca2+ release from the endoplasmic reticulum both in cardiomyocytes and macrophages is significantly inhibited by Trpc1 or Trpc6 knockout. Meanwhile, TRPC’s molecular partner — calmodulin — is uncoupled during Trpc1 or Trpc6 deficiency and binds to TLR4’s Pococurante site and atypical isoleucine-glutamine-like motif to block the inflammation cascade. Blocking the C-terminal CaM/IP3R binding domain in TRPC with chemical inhibitor could obstruct the Ca2+ leak and TLR4-mediated inflammation burst, demonstrating a cardioprotective effect in endotoxemia and polymicrobial sepsis. Our findings provide insight into the pathogenesis of endotoxemic cardiac dysfunction and suggest a novel approach for its treatment.
Immunosuppressive biomaterial-based therapeutic vaccine to treat multiple sclerosis via re-establishing immune tolerance
Authors: Nguyen Thanh Loc , Choi Youngjin , Im Jihye , Shin Hyunsu , Phan Ngoc Man , Kim Min Kyung , Choi Seung Woo , Kim Jaeyun 
Published: December 2022

AbstractCurrent therapies for autoimmune diseases, such as multiple sclerosis (MS), induce broad suppression of the immune system, potentially promoting opportunistic infections. Here, we report an immunosuppressive biomaterial-based therapeutic vaccine carrying self-antigen and tolerance-inducing inorganic nanoparticles to treat experimental autoimmune encephalomyelitis (EAE), a mouse model mimicking human MS. Immunization with self-antigen-loaded mesoporous nanoparticles generates Foxp3+regulatory T-cells in spleen and systemic immune tolerance in EAE mice, reducing central nervous system-infiltrating antigen-presenting cells (APCs) and autoreactive CD4+T-cells. Introducing reactive oxygen species (ROS)-scavenging cerium oxide nanoparticles (CeNP) to self-antigen-loaded nanovaccine additionally suppresses activation of APCs and enhances antigen-specific immune tolerance, inducing recovery in mice from complete paralysis at the late, chronic stage of EAE, which shows similarity to chronic human MS. This study clearly shows that the ROS-scavenging capability of catalytic inorganic nanoparticles could be utilized to enhance tolerogenic features in APCs, leading to antigen-specific immune tolerance, which could be exploited in treating MS.
Photo-induced stress relaxation in reconfigurable disulfide-crosslinked supramolecular films visualized by dynamic wrinkling
Authors: Yan Shuzhen , Hu Kaiming , Chen Shuai , Li Tiantian , Zhang Wenming , Yin Jie , Jiang Xuesong 
Published: December 2022

AbstractStress relaxation in reconfigurable supramolecular polymer networks is strongly related to intermolecular behavior. However, the relationship between molecular motion and macroscopic mechanics is usually vague, and the visualization of internal stress reflecting precise regulation of molecules remains challenging. Here, we present a strategy for visualizing photo-driven stress relaxation induced by infinitesimal perturbations in the intermolecular exchange reaction via reprogrammable wrinkle patterns. The supramolecular films exhibit visible changes in microscopic wrinkle topography through ultraviolet (UV)-induced dynamic disulfide exchange reaction. In accordance with the trans-scale theoretical models, which quantitatively evaluate the chemical-dependent mechanical stresses in the supramolecular network, the unexposed disordered wrinkles evolved into highly oriented patterns and underwent subsequent mutations after thermal treatment. The stress-sensitive wrinkle macro-patterns can be repetitively written/erased through network topology rearrangement using different stimuli. This strategy provides an approach for visualizing and understanding the molecular behavior from dynamic chemistry to mechanical changes, and directly programming wrinkle patterns with regulated structures.
China’s Gridded Manufacturing Dataset
Authors: Fan Chenjing , Huang Xinran , Zhou Lin , Gai Zhenyu , Zhu Chaoyang , Zhang Haole 
Published: December 2022

AbstractThe growth of the manufacturing industry is the engine of rapid economic growth in developing regions. Characterizing the geographical distribution of manufacturing firms is critically important for scientists and policymakers. However, data on the manufacturing industry used in previous studies either have a low spatial resolution (or fuzzy classification) or high-resolution information is lacking. Here, we propose a map point-of-interest classification method based on machine learning technology and build a dataset of the distribution of Chinese manufacturing firms called the Gridded Manufacturing Dataset. This dataset includes the number and type of manufacturing firms at a 0.01° latitude by 0.01° longitude scale. It includes all manufacturing firms (classified into seven categories) in China in 2015 (4.56 million) and 2019 (6.19 million). This dataset can be used to characterize temporal and spatial patterns in the distribution of manufacturing firms as well as reveal the mechanisms underlying the development of the manufacturing industry and changes in regional economic policies.
Probable COVID-19 infection is associated with subsequent poorer mental health and greater loneliness in the UK COVID-19 Mental Health and Wellbeing study
Authors: Wilding Sarah , O’Connor Daryl B. , Ferguson Eamonn , Cleare Seonaid , Wetherall Karen , O’Carroll Ronan E. , Robb Kathryn A. , O’Connor Rory C. 
Published: December 2022

AbstractThe COVID-19 pandemic has been associated with psychological distress. In addition to physical effects including fatigue and cognitive impairment, contracting COVID-19 itself may also be related to subsequent negative mental health outcomes. The present study reports data from a longitudinal, national survey of the UK adult population investigating whether contracting suspected or confirmed COVID-19 at the early stages of the pandemic (March–May 2020) was associated with poorer mental health outcomes in May/June 2020, October/November 2020 and June/July 2021. A quota survey design and a sampling frame that permitted recruitment of a national sample (n = 3077) were utilised. Experience of contracting COVID-19 during the first UK lockdown was assessed along with levels of depression, anxiety, mental wellbeing and loneliness. Around 9% of participants reported contracting COVID-19 in March/May 2020 (waves 1–3) with just under 13% of the overall sample reporting COVID-19 at any one of the first three time points. Compared to those without probable COVID-19 infection, participants with probable COVID-19 had poorer mental health outcomes at follow-up with these effects lasting up to 13 months (e.g., May/June 2020:ORdepression = 1.70, p < 0.001; ORanxiety = 1.61, p = 0.002; Oct/Nov 2020, ORdepression = 1.82, p < 0.001; ORanxiety 1.56, p = 0.013; June/July 2021, ORdepression = 2.01, p < 0.001; ORanxiety = 1.67, p = 0.008). Having a pre-existing mental health condition was also associated with greater odds of having probable COVID-19 during the study (OR = 1.31, p = 0.016). The current study demonstrates that contracting probable COVID-19 at the early stage of the pandemic was related to long-lasting associations with mental health and the relationship between mental health status and probable COVID-19 is bidirectional.
Alternative stable ecological states observed after a biological invasion
Authors: Garcia Adriano G. , Mesquita Filho Walter , Flechtmann Carlos A. H. , Lockwood Julie L. , Bonachela Juan A. 
Published: December 2022

AbstractAlthough biological invasions play an important role in ecosystem change worldwide, little is known about how invasions are influenced by local abiotic stressors. Broadly, abiotic stressors can cause large-scale community changes in an ecosystem that influence its resilience. The possibility for these stressors to increase as global changes intensify highlights the pressing need to understand and characterize the effects that abiotic drivers may have on the dynamics and composition of a community. Here, we analyzed 26 years of weekly abundance data using the theory of regime shifts to understand how the structure of a resident community of dung beetles (composed of dweller and tunneler functional groups) responds to climatic changes in the presence of the invasive tunneler Digitonthophagus gazella. Although the community showed an initial dominance by the invader that decreased over time, the theory of regime shifts reveals the possibility of an ecological transition driven by climate factors (summarized here in a climatic index that combines minimum temperature and relative humidity). Mid and low values of the driver led to the existence of two alternative stable states for the community structure (i.e. dominance of either dwellers or tunnelers for similar values of the climatic driver), whereas large values of the driver led to the single dominance by tunnelers. We also quantified the stability of these states against climatic changes (resilience), which provides insight on the conditions under which the success of an invasion and/or the recovery of the previous status quo for the ecosystem are expected. Our approach can help understand the role of climatic changes in community responses, and improve our capacity to deal with regime shifts caused by the introduction of exotic species in new ecosystems.
Titration of 124 antibodies using CITE-Seq on human PBMCs
Authors: Nettersheim Felix Sebastian , Armstrong Sujit Silas , Durant Christopher , Blanco-Dominguez Rafael , Roy Payel , Orecchioni Marco , Suryawanshi Vasantika , Ley Klaus 
Published: December 2022

AbstractSingle-cell RNA-sequencing (scRNA-Seq) is widely used to characterize immune cell populations. However, mRNA levels correlate poorly with expression of surface proteins, which are well established to define immune cell types. CITE-Seq (cellular indexing of transcriptomes and epitopes by sequencing) utilizes oligonucleotide-tagged antibodies to simultaneously analyze surface phenotypes and transcriptomes. Considering the high costs of adding surface phenotyping to scRNA-Seq, we aimed to determine which of 188 tested CITE-Seq antibodies can detect their antigens on human peripheral blood mononuclear cells (PBMCs), a commonly interrogated cell population in immunology, and find the optimal concentration for staining. The recommended concentration was optimal for 76 antibodies, whereas staining quality of 7 antibodies improved when the concentration was doubled. 33 and 8 antibodies still worked well when the concentration was reduced to 1/5 or 1/25, respectively. 64 antigens were not detected at any antibody concentration. Optimizing the antibody panel by removing antibodies not able to detect their target antigens and adjusting concentrations of the remaining antibodies will improve the analysis and may reduce costs. In conclusion, our data are a resource for building an informative and cost-effective panel of CITE-Seq antibodies and use them at their optimal concentrations in future CITE-seq experiments on human PBMCs.
Application of machine learning in the diagnosis of vestibular disease
Authors: Anh Do Tram , Takakura Hiromasa , Asai Masatsugu , Ueda Naoko , Shojaku Hideo 
Published: December 2022

AbstractMachine learning is considered a potential aid to support human decision making in disease prediction. In this study, we determined the utility of various machine learning algorithms in classifying peripheral vestibular (PV) and non-PV diseases based on the results of equilibrium function tests. A total of 1009 patients who had undergone our standardized neuro-otological examinations were recruited. We applied five supervised machine learning algorithms (random forest, adaboost, gradient boosting, support vector machine, and logistic regression). After preprocessing the data, optimizing the hyperparameters using GridSearchCV, and performing a final evaluation on the test set using scikit-learn, we evaluated the predictive capability using various performance metrics, namely, accuracy, F1-score, area under the receiver operating characteristic curve, precision, recall, and Matthews correlation coefficient (MCC). All five machine learning algorithms yielded satisfactory results; the accuracy of the algorithms ranged from 76 to 79%, with the support vector machine classifier having the highest accuracy. In cases where the predictions of the five models were consistent, the accuracy of the PV diagnostic results was improved to 83%, whereas it increased to 85% for the non-PV diagnostic results. Future research should increase the number of patients and optimize the classification methods to obtain the highest diagnostic accuracy.