Overconfidence in climate overshoot.

Global emission reduction efforts continue to be insufficient to meet the temperature goal of the Paris Agreement1. This makes the systematic exploration of so-called overshoot pathways that temporarily exceed a targeted global warming limit before drawing temperatures back down to safer levels a priority for science and policy2-5. Here we show that global and regional climate change and associated risks after an overshoot are different from a world that avoids it. We find that achieving declining global temperatures can limit long-term climate risks compared with a mere stabilization of global warming, including for sea-level rise and cryosphere changes. However, the possibility that global warming could be reversed many decades into the future might be of limited relevance for adaptation planning today. Temperature reversal could be undercut by strong Earth-system feedbacks resulting in high near-term and continuous long-term warming6,7. To hedge and protect against high-risk outcomes, we identify the geophysical need for a preventive carbon dioxide removal capacity of several hundred gigatonnes. Yet, technical, economic and sustainability considerations may limit the realization of carbon dioxide removal deployment at such scales8,9. Therefore, we cannot be confident that temperature decline after overshoot is achievable within the timescales expected today. Only rapid near-term emission reductions are effective in reducing climate risks.

Decoding transcriptomic signatures of cysteine string protein alpha-mediated synapse maintenance.

Synapse maintenance is essential for generating functional circuitry, and decrement in this process is a hallmark of neurodegenerative disease. Yet, little is known about synapse maintenance in vivo. Cysteine string protein α (CSPα), encoded by the Dnajc5 gene, is a synaptic vesicle chaperone that is necessary for synapse maintenance and linked to neurodegeneration. To investigate the transcriptional changes associated with synapse maintenance, we performed single-nucleus transcriptomics on the cortex of young CSPα knockout (KO) mice and littermate controls. Through differential expression and gene ontology analysis, we observed that both neurons and glial cells exhibit unique signatures in the CSPα KO brain. Significantly, all neuronal classes in CSPα KO brains show strong signatures of repression in synaptic pathways, while up-regulating autophagy-related genes. Through visualization of synapses and autophagosomes by electron microscopy, we confirmed these alterations especially in inhibitory synapses. Glial responses varied by cell type, with microglia exhibiting activation. By imputing cell-cell interactions, we found that neuron-glia interactions were specifically increased in CSPα KO mice. This was mediated by synaptogenic adhesion molecules, with the classical Neurexin1-Neuroligin 1 pair being the most prominent, suggesting that communication of glial cells with neurons is strengthened in CSPα KO mice to preserve synapse maintenance. Together, this study provides a rich dataset of transcriptional changes in the CSPα KO cortex and reveals insights into synapse maintenance and neurodegeneration.

Exploring Appropriate Reference Intervals and Clinical Decision Limits for Glucose-6-Phosphate Dehydrogenase Activity in Individuals From Guangzhou, China.

Background: Quantitative detection of glucose-6-phosphate dehydrogenase (G6PD) is commonly done to screen for G6PD deficiency. However, current reference intervals (RIs) of G6PD are unsuitable for evaluating G6PD-activity levels with local populations or associating G6PD variants with hemolysis risk to aid clinical decision-making. We explored appropriate RIs and clinical decision limits (CDLs) for G6PD activity in individuals from Guangzhou, China. Methods: We enrolled 5,852 unrelated individuals between 2020 and 2022 and screened their samples in quantitative assays for G6PD activity. We conducted further investigations, including G6PD genotyping, thalassemia genotyping, follow-up analysis, and statistical analysis, for different groups. Results: In Guangzhou, the RIs for the G6PD activities were 11.20-20.04 U/g Hb in male and 12.29-23.16 U/g Hb in female. The adjusted male median and normal male median (NMM) values were 15.47 U/g Hb and 15.51 U/g Hb, respectively. A threshold of 45% of the NMM could be used as a CDL to estimate the probability of G6PD variants. Our results revealed high hemolysis-risk CDLs (male: <10% of the NMM, female: <30% of the NMM), medium hemolysis-risk CDLs (male: 10%-45% of the NMM, female: 30%-79% of the NMM), and low hemolysis-risk CDLs (male: ≥ 45% of the NMM, female: ≥ 79% of the NMM). Conclusions: Collectively, our findings contribute to a more accurate evaluation of G6PD-activity levels within the local population and provide valuable insights for clinical decision-making. Specifically, identifying threshold values for G6PD variants and hemolysis risk enables improved prediction and management of G6PD deficiency, ultimately enhancing patient care and treatment outcomes.

Chemoradiotherapy-induced ACKR2+ tumor cells drive CD8+ T cell senescence and cervical cancer recurrence.

Tumor recurrence after chemoradiotherapy is challenging to overcome, and approaches to predict the recurrence remain elusive. Here, human cervical cancer tissues before and after concurrent chemoradiotherapy (CCRT) analyzed by single-cell RNA sequencing reveal that CCRT specifically promotes CD8+ T cell senescence, driven by atypical chemokine receptor 2 (ACKR2)+ CCRT-resistant tumor cells. Mechanistically, ACKR2 expression is increased in response to CCRT and is also upregulated through the ligation of CC chemokines that are produced by activated myeloid and T cells. Subsequently, ACKR2+ tumor cells are induced to produce transforming growth factor ß to drive CD8+ T cell senescence, thereby compromising antitumor immunity. Moreover, retrospective analysis reveals that ACKR2 expression and CD8+ T cell senescence are enhanced in patients with cervical cancer who experienced recurrence after CCRT, indicating poor prognosis. Overall, we identify a subpopulation of CCRT-resistant ACKR2+ tumor cells driving CD8+ T cell senescence and tumor recurrence and highlight the prognostic value of ACKR2 and CD8+ T cell senescence for chemoradiotherapy recurrence.

Complete Mitochondrial Genome and Phylogenetic Analysis of Tarsiger indicus (Aves: Passeriformes: Muscicapidae).

Tarsiger indicus (Vieillot, 1817), the White-browed Bush Robin, is a small passerine bird widely distributed in Asian countries. Here, we successfully sequenced its mitogenome using the Illumina Novaseq 6000 platform (Illumina, San Diego, CA, USA) for PE 2 × 150 bp sequencing. Combined with other published mitogenomes, we conducted the first comprehensive comparative mitogenome analysis of Muscicapidae birds and reconstructed the phylogenetic relationships between Muscicapidae and related groups. The T. indicus mitogenome was 16,723 bp in size, and it possessed the typical avian mitogenome structure and organization. Most PCGs of T. indicus were initiated strictly with the typical start codon ATG, while COX1 and ND2 were started with GTG. RSCU statistics showed that CUA, CGA, and GCC were relatively high frequency in the T. indicus mitogenome. T. cyanurus and T. indicus shared very similar mitogenomic features. All 13 PCGs of Muscicapidae mitogenomes had experienced purifying selection. Specifically, ATP8 had the highest rate of evolution (0.13296), whereas COX1 had the lowest (0.01373). The monophylies of Muscicapidae, Turdidae, and Paradoxornithidae were strongly supported. The clade of ((Muscicapidae + Turdidae) + Sturnidae) in Passeriformes was supported by both Bayesian Inference and Maximum likelihood analyses. The latest taxonomic status of many passerine birds with complex taxonomic histories were also supported. For example, Monticola gularis, T. indicus, and T. cyanurus were allocated to Turdidae in other literature; our phylogenetic topologies clearly supported their membership in Muscicapidae; Paradoxornis heudei, Suthora webbiana, S. nipalensis, and S. fulvifrons were formerly classified into Muscicapidae; we supported their membership in Paradoxornithidae; Culicicapa ceylonensis was originally classified as a member of Muscicapidae; our results are consistent with a position in Stenostiridae. Our study enriches the genetic data of T. indicus and provides new insights into the molecular phylogeny and evolution of passerine birds.

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.

Decoding transcriptomic signatures of Cysteine String Protein alpha-mediated synapse maintenance.

Synapse maintenance is essential for generating functional circuitry and decrement in this process is a hallmark of neurodegenerative disease. While we are beginning to understand the basis of synapse formation, much less is known about synapse maintenance in vivo. Cysteine string protein α (CSPα), encoded by the Dnajc5 gene, is a synaptic vesicle chaperone that is necessary for synapse maintenance and linked to neurodegeneration. To investigate the transcriptional changes associated with synapse maintenance, we performed single nucleus transcriptomics on the cortex of young CSPα knockout (KO) mice and littermate controls. Through differential expression and gene ontology analysis, we observed that both neurons and glial cells exhibit unique signatures in CSPα KO brain. Significantly all neurons in CSPα KO brains show strong signatures of repression in synaptic pathways, while upregulating autophagy related genes. Through visualization of synapses and autophagosomes by electron microscopy, we confirmed these alterations especially in inhibitory synapses. By imputing cell-cell interactions, we found that neuron-glia interactions were specifically increased in CSPα KO mice. This was mediated by synaptogenic adhesion molecules, including the classical Neurexin1-Neuroligin 1 pair, suggesting that communication of glial cells with neurons is strengthened in CSPα KO mice in an attempt to achieve synapse maintenance. Together, this study reveals unique cellular and molecular transcriptional changes in CSPα KO cortex and provides new insights into synapse maintenance and neurodegeneration.

A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases.

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

An open protocol for modeling T Cell Clonotype repertoires using TCRß CDR3 sequences.

T cell receptor repertoires can be profiled using next generation sequencing (NGS) to measure and monitor adaptive dynamical changes in response to disease and other perturbations. Genomic DNA-based bulk sequencing is cost-effective but necessitates multiplex target amplification using multiple primer pairs with highly variable amplification efficiencies. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both our open protocol and a commercial solution, we show high concordance between bulk clonality metrics. This approach is an inexpensive and open-source alternative to commercial solutions.

Carbon Monitor Europe near-real-time daily CO2 emissions for 27 EU countries and the United Kingdom.

With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO2 emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO2 emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

CarbonMonitor-Power near-real-time monitoring of global power generation on hourly to daily scales.

We constructed a frequently updated, near-real-time global power generation dataset: CarbonMonitor-Power since January, 2016 at national levels with near-global coverage and hourly-to-daily time resolution. The data presented here are collected from 37 countries across all continents for eight source groups, including three types of fossil sources (coal, gas, and oil), nuclear energy and four groups of renewable energy sources (solar energy, wind energy, hydro energy and other renewables including biomass, geothermal, etc.). The global near-real-time power dataset shows the dynamics of the global power system, including its hourly, daily, weekly and seasonal patterns as influenced by daily periodical activities, weekends, seasonal cycles, regular and irregular events (i.e., holidays) and extreme events (i.e., the COVID-19 pandemic). The CarbonMonitor-Power dataset reveals that the COVID-19 pandemic caused strong disruptions in some countries (i.e., China and India), leading to a temporary or long-lasting shift to low carbon intensity, while it had only little impact in some other countries (i.e., Australia). This dataset offers a large range of opportunities for power-related scientific research and policy-making.

An open protocol for modeling T Cell Clonotype repertoires using TCRß CDR3 sequences.

T cell receptor repertoires can be profiled using next generation sequencing (NGS) to measure and monitor adaptive dynamical changes in response to disease and other perturbations. Genomic DNA-based bulk sequencing is cost-effective but necessitates multiplex target amplification using multiple primer pairs with highly variable amplification efficiencies. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both our open protocol and a commercial solution, we show high concordance between bulk clonality metrics. This approach is an inexpensive and open-source alternative to commercial solutions.

Near-real-time global gridded daily CO2 emissions 2021.

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies' effectiveness and make adjustments quickly.

Shift in the Mode of Carbon Cycling Recorded by Biomarkers and Carbon Isotopic Compositions in the Yanchang Formation, Ordos Basin: Autotrophy vs Heterotrophy.

Organic-rich shales and mudstones have long been investigated regarding the control of source, environment, climate, etc. on the enrichment of organic carbon. However, little is documented about how autotrophy and heterotrophy influence organic carbon cycling/export. Here, we show molecular and carbon isotopic compositional changes of the shale or mudstone source rocks from the Chang 3 to 7 members of the Yanchang Formation. The Chang 7 member source rocks have higher quality (480-500 mg/g) and total organic carbon (TOC) (15.3% on average) than other member source rocks; the sterane/hopane ratio and the δ13C of organic carbon and kerogen decrease from the Chang 3 to 7 members, but Δδ ([average δ13C of n-C17 + n-C18] - [average δ13C of pristane + phytane]) increases, and no aryl isoprenoids and C40 aromatic carotenoids (e.g., isorenieratane) were observed. These low maturity biomarker features suggest that there were no water stratification, photic zone euxinia (PZE), and no obvious change in the organic matter source, and the water column is generally anoxic. A comparison of the δ13C of Pr and Ph with the δ13C of the n-C17 and n-C18 alkanes reveals a shift in the mode of carbon cycling/export (autotrophy versus heterotrophy) in the Yanchang Formation and that there was dominant heterotrophic bacterial activity or bacterial biomass in the Chang 7 member. The TOC spike in the Chang 7 member may result from boosted carbon cycling/export that improves organic carbon preservation than other members. Possible external forcings on the shift are abundant hydrothermal- or volcanic-derived metal salts as electron acceptors in the palaeowater, which is a reasonable explanation for enhanced heterotrophic bacterial activity. This finding improves our understanding of heterotrophic bacterial activity control on organic matter (OM) preservation and may be a significant supplement for understanding the ecological or environmental forcings in the Yanchang Formation, Ordos Basin.

Clustering high-dimensional data via feature selection.

High-dimensional clustering analysis is a challenging problem in statistics and machine learning, with broad applications such as the analysis of microarray data and RNA-seq data. In this paper, we propose a new clustering procedure called spectral clustering with feature selection (SC-FS), where we first obtain an initial estimate of labels via spectral clustering, then select a small fraction of features with the largest R-squared with these labels, that is, the proportion of variation explained by group labels, and conduct clustering again using selected features. Under mild conditions, we prove that the proposed method identifies all informative features with high probability and achieves the minimax optimal clustering error rate for the sparse Gaussian mixture model. Applications of SC-FS to four real-world datasets demonstrate its usefulness in clustering high-dimensional data.

Near-real-time daily estimates of fossil fuel CO2 emissions from major high-emission cities in China.

Cities in China are on the frontline of low-carbon transition which requires monitoring city-level emissions with low-latency to support timely climate actions. Most existing CO2 emission inventories lag reality by more than one year and only provide annual totals. To improve the timeliness and temporal resolution of city-level emission inventories, we present Carbon Monitor Cities-China (CMCC), a near-real-time dataset of daily CO2 emissions from fossil fuel and cement production for 48 major high-emission cities in China. This dataset provides territory-based emission estimates from 2020-01-01 to 2021-12-31 for five sectors: power generation, residential (buildings and services), industry, ground transportation, and aviation. CMCC is developed based on an innovative framework that integrates bottom-up inventory construction and daily emission estimates from sectoral activities and models. Annual emissions show reasonable agreement with other datasets, and uncertainty ranges are estimated for each city and sector. CMCC provides valuable daily emission estimates that enable low-latency mitigation monitoring for cities in China.

Carbon Monitor Cities near-real-time daily estimates of CO2 emissions from 1500 cities worldwide.

Building 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.

Multiplexed LNP-mRNA vaccination against pathogenic coronavirus species.

Although COVID-19 vaccines have been developed, multiple pathogenic coronavirus species exist, urging on development of multispecies coronavirus vaccines. Here we develop prototype lipid nanoparticle (LNP)-mRNA vaccine candidates against SARS-CoV-2 Delta, SARS-CoV, and MERS-CoV, and we test how multiplexing LNP-mRNAs can induce effective immune responses in animal models. Triplex and duplex LNP-mRNA vaccinations induce antigen-specific antibody responses against SARS-CoV-2, SARS-CoV, and MERS-CoV. Single-cell RNA sequencing profiles the global systemic immune repertoires and respective transcriptome signatures of vaccinated animals, revealing a systemic increase in activated B cells and differential gene expression across major adaptive immune cells. Sequential vaccination shows potent antibody responses against all three species, significantly stronger than simultaneous vaccination in mixture. These data demonstrate the feasibility, antibody responses, and single-cell immune profiles of multispecies coronavirus vaccination. The direct comparison between simultaneous and sequential vaccination offers insights into optimization of vaccination schedules to provide broad and potent antibody immunity against three major pathogenic coronavirus species.

Scenarios of demographic distributional aspects of health co-benefits from decarbonising urban transport.

BACKGROUND: There is limited knowledge on the distribution of the health co-benefits of reduced air pollutants and carbon emissions in the transport sector across populations. METHODS: This Article describes a health impact assessment used to estimate the health co-benefits of alternative land passenger transport scenarios for the city of Beijing, China, testing the effect of five transport-based scenarios from 2020 to 2050 on health outcomes. New potential scenarios range from implementing a green transport infrastructure, to scenarios primarily based on the electrification of vehicle fleets and a deep decarbonisation scenario with near zero carbon emissions by 2050. The health co-benefits are disaggregated by age and sex and estimated in monetary terms. FINDINGS: The results show that all the alternative mitigation scenarios result in reduced PM2·5 and CO2 emissions compared to a business-as-usual scenario during 2020-50. The near zero scenario achieves the largest health co-benefits and economic benefits annually relative to the sole mitigation strategy, preventing 300 (95% CI 229-450) deaths, with health co-benefits and CO2 cost-saving an equivalent of 0·01% (0·00-0·03%) of Beijing's Gross domestic product in 2015 by 2050. Given Beijing's ageing population and higher mortality rate, individuals aged 50 years and older experience the greatest benefit from the mitigation scenarios. Regarding sex, the greatest health benefits occur in men. INTERPRETATION: This assessment provides estimates of the demographic distribution of benefits from the effects of combinations of green transport and decarbonising vehicles in transport futures. The results show that there are substantial positive health outcomes from decarbonising transport in Beijing. Policies aimed at encouraging active travel and use of public transport, increasing the safety of active travel, improving public transport infrastructure, and decarbonising vehicles lead to differential benefits. In addition, disaggregation by age and sex shows that the health impacts related to transport pollution disproportionately influence different age cohorts and genders. FUNDING: National Natural Science Foundation of China and FRIEND Project (through the National Research Foundation of Korea, funded by the Ministry of Science and ICT).

Massive prolapsed cervical lumps in cervical cancer treated with three-dimensional implant brachytherapy: a case report.

Background: Cervical cancer is one of the most common gynecological malignancies in developing countries. But the cervical cancer patients with tumor prolapse are very rare. The treatment principle of cervical cancer has been written into the guide, while the management of cervical lumps prolapse associated with cervical cancer is not standardized. Every doctor has different opinions on treatment strategies. Herein, we describe the three-dimensional brachytherapy treatment for massive prolapsed cervical lumps. Case Description: A 48-year-old woman diagnosed with cervical cancer developed a huge cervical mass prolapsed after defecation on the second day of chemotherapy. The mass surface was continuously bleeding and unable to return to the vagina. Therefore, uterine artery embolization interventional hemostasis was performed and then three-dimensional brachytherapy treatment was applied. The mass was necrotic and shedding and then retracted into the vagina on the 7th day after implantation treatment. Finally, the patient successfully received radical radiotherapy [pelvic and abdominal cavity external beam radiotherapy-PCTV 50.4 Gy/28 F, pelvic metastatic lymph nodes PGTVn 61.0 Gy/28 F, plus vaginal three-dimensional brachytherapy-HRCTV (D90) 27.25 Gy/4 F]. Conclusions: If cervical cancer combined with tumor prolapse is inoperable, three-dimensional implants brachytherapy seems to be an adequate therapeutic option.