Microbial carbon use efficiency promotes global soil carbon storage.

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data.

Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.

Managing nitrogen to restore water quality in China.

The nitrogen cycle has been radically changed by human activities1. China consumes nearly one third of the world's nitrogen fertilizers. The excessive application of fertilizers2,3 and increased nitrogen discharge from livestock, domestic and industrial sources have resulted in pervasive water pollution. Quantifying a nitrogen 'boundary'4 in heterogeneous environments is important for the effective management of local water quality. Here we use a combination of water-quality observations and simulated nitrogen discharge from agricultural and other sources to estimate spatial patterns of nitrogen discharge into water bodies across China from 1955 to 2014. We find that the critical surface-water quality standard (1.0 milligrams of nitrogen per litre) was being exceeded in most provinces by the mid-1980s, and that current rates of anthropogenic nitrogen discharge (14.5 ± 3.1 megatonnes of nitrogen per year) to fresh water are about 2.7 times the estimated 'safe' nitrogen discharge threshold (5.2 ± 0.7 megatonnes of nitrogen per year). Current efforts to reduce pollution through wastewater treatment and by improving cropland nitrogen management can partially remedy this situation. Domestic wastewater treatment has helped to reduce net discharge by 0.7 ± 0.1 megatonnes in 2014, but at high monetary and energy costs. Improved cropland nitrogen management could remove another 2.3 ± 0.3 megatonnes of nitrogen per year-about 25 per cent of the excess discharge to fresh water. Successfully restoring a clean water environment in China will further require transformational changes to boost the national nutrient recycling rate from its current average of 36 per cent to about 87 per cent, which is a level typical of traditional Chinese agriculture. Although ambitious, such a high level of nitrogen recycling is technologically achievable at an estimated capital cost of approximately 100 billion US dollars and operating costs of 18-29 billion US dollars per year, and could provide co-benefits such as recycled wastewater for crop irrigation and improved environmental quality and ecosystem services.

quickBAM: a parallelized BAM file access API for high-throughput sequence analysis informatics.

MOTIVATION: In time-critical clinical settings, such as precision medicine, genomic data needs to be processed as fast as possible to arrive at data-informed treatment decisions in a timely fashion. While sequencing throughput has dramatically increased over the past decade, bioinformatics analysis throughput has not been able to keep up with the pace of computer hardware improvement, and consequently has now turned into the primary bottleneck. Modern computer hardware today is capable of much higher performance than current genomic informatics algorithms can typically utilize, therefore presenting opportunities for significant improvement of performance. Accessing the raw sequencing data from BAM files, e.g. is a necessary and time-consuming step in nearly all sequence analysis tools, however existing programming libraries for BAM access do not take full advantage of the parallel input/output capabilities of storage devices. RESULTS: In an effort to stimulate the development of a new generation of faster sequence analysis tools, we developed quickBAM, a software library to accelerate sequencing data access by exploiting the parallelism in commodity storage hardware currently widely available. We demonstrate that analysis software ported to quickBAM consistently outperforms their current versions, in some cases finishing an analysis in under 3 min while the original version took 1.5 h, using the same storage solution. AVAILABILITY AND IMPLEMENTATION: Open source and freely available at https://gitlab.com/yiq/quickbam/, we envision that quickBAM will enable a new generation of high-performance informatics tools, either directly boosting their performance if they are currently data-access bottlenecked, or allow data-access to keep up with further optimizations in algorithms and compute techniques.

Convergence in simulating global soil organic carbon by structurally different models after data assimilation.

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

Efficacy of growth factor gene-modified stem cells for motor function after spinal cord injury in rodents: a systematic review and meta­analysis.

The efficacy of growth factor gene-modified stem cells in treating spinal cord injury (SCI) remains unclear. This study aims to evaluate the effectiveness of growth factor gene-modified stem cells in restoring motor function after SCI. Two reviewers searched four databases, including PubMed, Embase, Web of Science, and Scopus, to identify relevant records. Studies on rodents assessing the efficacy of transplanting growth factor gene-modified stem cells in restoring motor function after SCI were included. The results were reported using the standardized mean difference (SMD) with a 95% confidence interval (95% CI). Analyses showed that growth factor gene-modified stem cell transplantation improved motor function recovery in rodents with SCI compared to the untreated (SMD = 3.98, 95% CI 3.26-4.70, I2 = 86.8%, P < 0.0001) and stem cell (SMD = 2.53, 95% CI 1.93-3.13, I2 = 86.9%, P < 0.0001) groups. Using growth factor gene-modified neural stem/histone cells enhanced treatment efficacy. In addition, the effectiveness increased when viral vectors were employed for gene modification and high transplantation doses were administered during the subacute phase. Stem cells derived from the human umbilical cord exhibited an advantage in motor function recovery. However, the transplantation of growth factor gene-modified stem cells did not significantly improve motor function in male rodents (P = 0.136). Transplantation of growth factor gene-modified stem cells improved motor function in rodents after SCI, but claims of enhanced efficacy should be approached with caution. The safety of gene modification remains a significant concern, requiring additional efforts to enhance its clinical translatability.

Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography.

Background CT is helpful in guiding the revascularization of chronic total occlusion (CTO), but manual prediction scores of percutaneous coronary intervention (PCI) success have challenges. Deep learning (DL) is expected to predict success of PCI for CTO lesions more efficiently. Purpose To develop a DL model to predict guidewire crossing and PCI outcomes for CTO using coronary CT angiography (CCTA) and evaluate its performance compared with manual prediction scores. MATERIALS AND METHODS: Participants with CTO lesions were prospectively identified from one tertiary hospital between January 2018 and December 2021 as the training set to develop the DL prediction model for PCI of CTO, with fivefold cross validation. The algorithm was tested using an external test set prospectively enrolled from three tertiary hospitals between January 2021 and June 2022 with the same eligibility criteria. All participants underwent preprocedural CCTA within 1 month before PCI. The end points were guidewire crossing within 30 minutes and PCI success of CTO.Results A total of 534 participants (mean age, 57.7 years ± 10.8 [SD]; 417 [78.1%] men) with 565 CTO lesions were included. In the external test set (186 participants with 189 CTOs), the DL model saved 85.0% of the reconstruction and analysis time of manual scores (mean, 73.7 seconds vs 418.2-466.9 seconds) and had higher accuracy than manual scores in predicting guidewire crossing within 30 minutes (DL, 91.0%; CT Registry of Chronic Total Occlusion Revascularization, 61.9%; Korean Multicenter CTO CT Registry [KCCT], 68.3%; CCTA-derived Multicenter CTO Registry of Japan (J-CTO), 68.8%; P < .05) and PCI success (DL, 93.7%; KCCT, 74.6%; J-CTO, 75.1%; P < .05). For DL, the area under the receiver operating characteristic curve was 0.97 (95% CI: 0.89, 0.99) for the training test set and 0.96 (95% CI: 0.90, 0.98) for the external test set. Conclusion The DL prediction model accurately predicted the percutaneous recanalization outcomes of CTO lesions and increased the efficiency of noninvasively grading the difficulty of PCI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Pundziute-do Prado in this issue.

Heteronuclear Trimetallic MFe2 and M2 Fe (M=V, Nb, and Ta) Clusters for Dinitrogen Activation.

Catalysts with heteronuclear metal active sites may have high performance in the nitrogen reduction reaction (NRR), and the in-depth understanding of the reaction mechanisms is crucial for the design of related catalysts. In this work, the dissociative adsorption of N2 on heteronuclear trimetallic MFe2 and M2 Fe (M=V, Nb, and Ta) clusters was studied with density functional theory calculations. For each cluster, two reaction paths were studied with N2 initially on M and Fe atoms, respectively. Mayer bond order analysis provides more information on the activation of N-N bonds. M2 Fe is generally more reactive than MFe2 . The coordination mode of N2 on three metal atoms can be end-on: end-on: side-on (EES) for both MFe2 and M2 Fe. In addition, a unique end-on: side-on: side-on (ESS) coordination mode was found for M2 Fe, which leads to a higher degree of N-N bond activation. Nb2 Fe has the highest reactivity towards N2 when both the transfer of N2 and the dissociation of N-N bonds are taken into account, while Ta-containing clusters have a superior ability to activate the N-N bond. These results indicate that it is possible to improve the performance of iron-based catalysts by doping with vanadium group metals.

Derivation and Validation of a Risk Score for Methicillin-Resistant Staphylococcus aureus Bloodstream Infection.

BACKGROUND: Compared with methicillin sensitive Staphylococcus aureus (MSSA), the prognosis of patients with methicillin resistant Staphylococcus aureus (MRSA) bloodstream infection is poor. Therefore, the construction of MRSA and MSSA identification model has certain value for the selection of antibiotics and treatment outcome control. This study aimed to derive and validate a simple risk prediction model for MRSA bloodstream infection in Chinese patients. METHODS: Three hundred and thirty-five patients with Staphylococcus aureus (S. aureus) bloodstream infection were retrospectively analyzed and divided into three groups. The first group was used for the risk score derivation (n = 163), the second group was used for internal validation (n = 80), and the third group was used for external validation (n = 92). According to the odds ratio (OR) obtained from multivariate logistic regression, the risk prediction model for MRSA bloodstream infection was established, and the prediction efficiency of the model in three cohorts were evaluated. RESULTS: Hospital stay before BSI ≥ 7 days, hospital acquired BSI, infection source ≥ 2 sites, indwelling gastric tube before BSI and carbapenems used before BSI and after admission were independent influencing factors of MRSA in the derivation group, the above influencing factors were scored 3, 5, 4, 3, and 3, respectively. The derivation, internal and external validation groups showed adequate discrimination (the AUCs were 0.788, 0.780, and 0.742, respectively) and good calibration (H-L tests were χ2 = 3.896, p = 0.306; χ2 = 4.221, p = 0.298; and χ2 = 3.974, p = 0.352, respectively). The risk scores were further divided into very low-risk (score 0 - 3), low-risk (score 4 - 7), high-risk (score 8 - 12), and very high-risk (score ≥ 13) layers. CONCLUSIONS: The simple risk score model for predicting MRSA bloodstream infection has good predictive effect, high predictive accuracy, and good clinical applicability, which can help clinicians choose sensitive antibiotics and reduce the adverse prognosis of patients.

ROR1-targeted delivery of miR-29b induces cell cycle arrest and therapeutic benefit in vivo in a CLL mouse model.

Chronic lymphocytic leukemia (CLL) occurs in 2 major forms: aggressive and indolent. Low miR-29b expression in aggressive CLL is associated with poor prognosis. Indiscriminate miR-29b overexpression in the B-lineage of mice causes aberrance, thus warranting the need for selective introduction of miR-29b into B-CLL cells for therapeutic benefit. The oncofetal antigen receptor tyrosine kinase orphan receptor 1 (ROR1) is expressed on malignant B-CLL cells, but not normal B cells, encouraging us with ROR1-targeted delivery for therapeutic miRs. Here, we describe targeted delivery of miR-29b to ROR1+ CLL cells leading to downregulation of DNMT1 and DNMT3A, modulation of global DNA methylation, decreased SP1, and increased p21 expression in cell lines and primary CLL cells in vitro. Furthermore, using an Eµ-TCL1 mouse model expressing human ROR1, we report the therapeutic benefit of enhanced survival via cellular reprograming by downregulation of DNMT1 and DNMT3A in vivo. Gene expression profiling of engrafted murine leukemia identified reprogramming of cell cycle regulators with decreased SP1 and increased p21 expression after targeted miR-29b treatment. This finding was confirmed by protein modulation, leading to cell cycle arrest and survival benefit in vivo. Importantly, SP1 knockdown results in p21-dependent compensation of the miR-29b effect on cell cycle arrest. These studies form a basis for leukemic cell-targeted delivery of miR-29b as a promising therapeutic approach for CLL and other ROR1+ B-cell malignancies.

Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion.

Air pollution has altered the Earth's radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn/) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R2 of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM2.5 data allow us to track the day-to-day variations in PM2.5 concentrations over China in a timely manner. The long-term records of PM2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM2.5 data are publicly available through our website for sharing with the research and policy communities.

Function of berberine on porcine in vitro fertilization embryo development and differential expression analysis of microRNAs.

The effect of berberine (Ber) on in vitro fertilization (IVF) embryo development in pigs and the associated differential expression of microRNAs (miRNAs) in the embryo were investigated. NCSU-23 embryonic culture medium was used for a control group, while NCSU-23 embryonic culture medium added with Ber was used for a Ber group. The embryo development rates in these groups were determined, and the zygotes, 4- and 8-cell embryos, and blastocysts were collected for cDNA microarray analysis. The development rates of 2-, 4-, 8-cell embryos and blastocysts were significantly higher in the Ber group than those in the control group (p < 0.01). The differentially expressed miRNAs in the 8-cell versus the 4-cell stage in control group as well as in the 8-cell Ber group versus the 8-cell control group overlapped, and it was found that nine miRNAs were commonly upregulated and two of them were downregulated, while there was no overlap among the other groups. The target genes of Ber-regulated miRNAs at the 8-cell stage were mainly associated with the molecular pathway of nucleic acid and protein synthesis. These findings suggest that Ber may regulate the expression of miRNAs at the 8-cell stage, which is beneficial to provide material reserves for the maternal to zygote transition of porcine embryos, thereby increasing the porcine IVF embryo development rate.

Induced Apoptosis Investigation in Wild-type and FLT3-ITD Acute Myeloid Leukemia Cells by Nanochannel Electroporation and Single-cell qRT-PCR.

Nanochannel electroporation (NEP) was applied to deliver precise dosages of myeloid cell leukemia-1 (Mcl-1)-specific siRNA and molecular beacons to two types of acute myeloid leukemia (AML) cells, FMS-like tyrosine kinase-3 wild-type (WT) and internal tandem duplications (ITD) type at the single-cell level. NEP, together with single-cell quantitative reverse transcription PCR, led to an observation showing nearly 20-folds more Mcl-1 siRNA than MCL1 mRNA were required to induce cell death for both cell lines and patient blasts, i.e., ~8,800 siRNAs for ~500 ± 50 mRNAs in ITD cells and ~6,000 siRNAs for ~300 ± 50 mRNAs in WT cells. A time-lapse study revealed that >75% MCL1 mRNA was downregulated within 1 hour after delivery of a small amount of siRNA. However, additional siRNA was required to inhibit the newly transcribed mRNA for >12 hours until the cell lost its ability of self-protection recovery. A multidelivery strategy of low doses and short delivery interval, which require 77% less siRNA and has the potential of lower side effects and clinical cost, was as effective as a single high-dose siRNA delivery. Our method provides a viable analytical tool to investigate gene silencing at the single-cell level for oligonucleotide-based therapy.

NRAS isoforms differentially affect downstream pathways, cell growth, and cell transformation.

Neuroblastoma rat sarcoma (RAS) viral oncogene homolog (NRAS), a small GTPase, is one of the most thoroughly studied oncogenes that controls cell growth, differentiation, and survival by facilitating signal transduction. Here, we identify four novel naturally occurring NRAS isoforms (isoforms 2-5) in addition to the canonical isoform (isoform 1). Expression analyses performed on a panel of several different human malignancies and matching normal tissue revealed distinct isoform expression patterns. Two of the novel isoforms were found in the nucleus and cytoplasm, whereas the others were exclusively cytoplasmic. The isoforms varied in their binding affinities to known downstream targets and differentially regulated the RAS signaling pathway. Strikingly, forced expression of isoform 5, which encodes only a 20-aa peptide, led to increased cell proliferation and to transformation by activation of known NRAS targets. These discoveries open new avenues in the study of NRAS.

Controllable Large-Scale Transfection of Primary Mammalian Cardiomyocytes on a Nanochannel Array Platform.

While electroporation has been widely used as a physical method for gene transfection in vitro and in vivo, its application in gene therapy of cardiovascular cells remains challenging. Due to the high concentration of ion-transport proteins in the sarcolemma, conventional electroporation of primary cardiomyocytes tends to cause ion-channel activation and abnormal ion flux, resulting in low transfection efficiency and high mortality. In this work, a high-throughput nanoelectroporation technique based on a nanochannel array platform is reported, which enables massively parallel delivery of genetic cargo (microRNA, plasmids) into mouse primary cardiomyocytes in a controllable, highly efficient, and benign manner. A simple "dipping-trap" approach was implemented to precisely position a large number of cells on the nanoelectroporation platform. With dosage control, our device precisely titrates the level of miR-29, a potential therapeutic agent for cardiac fibrosis, and determines the minimum concentration of miR-29 causing side effects in mouse primary cardiomyocytes. Moreover, the dose-dependent effect of miR-29 on mitochondrial potential and homeostasis is monitored. Altogether, our nanochannel array platform provides efficient trapping and transfection of primary mouse cardiomyocyte, which can improve the quality control for future microRNA therapy in heart diseases.

Implications of the miR-10 family in chemotherapy response of NPM1-mutated AML.

Nucleophosmin-mutated acute myeloid leukemia (NPM1mut-AML) patients have a high rate of complete remission (CR) to induction chemotherapy. However, the mechanisms responsible for such effects are unknown. Because miR-10 family members are expressed at high levels in NPM1mut-AML, we evaluated whether these microRNAs could predict chemotherapy response in AML. We found that high baseline miR-10 family expression in 54 untreated cytogenetically heterogeneous AML patients was associated with achieving CR. However, when we included NPM1 mutation status in the multivariable model, there was a significant interaction effect between miR-10a-5p expression and NPM1 mutation status. Similar results were observed when using a second cohort of 183 cytogenetically normal older (age ≥ 60 years) AML patients. Loss- and gain-of-function experiments using miR-10a-5p in cell lines and primary blasts did not demonstrate any effect in apoptosis or cell proliferation at baseline or after chemotherapy. These data support a bystander role for the miR-10 family in NPM1mut-AML.

The impact of chatbots based on large language models on second language vocabulary acquisition.

In recent years, the integration of artificial intelligence (AI) and machine learning (ML) into education, particularly for Personalized Language Learning (PLL), has garnered significant attention. This approach tailors interventions to address the unique challenges faced by individual learners. Large Language Models (LLMs), including Chatbots, have demonstrated a substantial potential in automating and enhancing educational tasks, effectively capturing the complexity and diversity of human language. In this study, 52 foreign language students were randomly divided into two groups: one with the assistance of a Chatbot based on LLMs and one without. Both groups learned the same series of target words over eight weeks. Post-treatment assessments, including systematic observation and quantitative tests assessing both receptive and productive vocabulary knowledge, were conducted immediately after the study and again two weeks later. The findings demonstrate that employing an AI Chatbot based on LLMs significantly aids students in acquiring both receptive and productive vocabulary knowledge during their second language learning journey. Notably, Chatbots contribute to the long-term retention of productive vocabulary and facilitate incidental vocabulary learning. This study offers valuable insights into the practical benefits of LLM-based tools in language learning, with a specific emphasis on vocabulary development. Chatbots utilizing LLMs emerge as effective language learning aids. It emphasizes the importance of educators understanding the potential of these technologies in L2 vocabulary instruction and encourages the adoption of strategic teaching methods incorporating such tools.