Recent cover crop adoption is associated with small maize and soybean yield losses in the United States

Cover crops are gaining traction in many agricultural regions, partly driven by increased public subsidies and by private markets for ecosystem services. These payments are motivated by environmental benefits, including improved soil health, reduced erosion, and increased soil organic carbon. However, previous work based on experimental plots or crop modeling indicates cover crops may reduce crop yields. It remains unclear, though, how recent cover crop adoption has affected productivity in commercial agricultural systems. Here we perform the first large-scale, field-level analysis of observed yield impacts from cover cropping as implemented across the US Corn Belt. We use validated satellite data products at sub-field scales to analyze maize and soybean yield outcomes for over 90,000 fields in 2019–2020. Because we lack data on cover crop species or timing, we seek to quantify the yield impacts of cover cropping as currently practiced in aggregate. Using causal forests analysis, we estimate an average maize yield loss of 5.5% on fields where cover crops were used for 3 or more years, compared with fields that did not adopt cover cropping. Maize yield losses were larger on fields with better soil ratings, cooler mid-season temperatures, and lower spring rainfall. For soybeans, average yield losses were 3.5%, with larger impacts on fields with warmer June temperatures, lower spring and late-season rainfall, and, to a lesser extent, better soils. Estimated impacts are consistent with multiple mechanisms indicated by experimental and simulation-based studies, including the effects of cover crops on nitrogen dynamics, water consumption, and soil oxygen depletion. Our results suggest a need to improve cover crop management to reduce yield penalties, and a potential need to target subsidies based on likely yield impacts. Ultimately, avoiding substantial yield penalties is important for realizing widespread adoption and associated benefits for water quality, erosion, soil carbon, and greenhouse gas emissions.     

The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures

Infectious disease surveillance systems provide vital data for guiding disease prevention and control policies, yet the formalization of methods to optimize surveillance networks has largely been overlooked. Decisions surrounding surveillance design parameters—such as the number and placement of surveillance sites, target populations, and case definitions—are often determined by expert opinion or deference to operational considerations, without formal analysis of the influence of design parameters on surveillance objectives. Here we propose a simulation framework to guide evidence-based surveillance network design to better achieve specific surveillance goals with limited resources. We define evidence-based surveillance design as an optimization problem, acknowledging the many operational constraints under which surveillance systems operate, the many dimensions of surveillance system design, the multiple and competing goals of surveillance, and the complex and dynamic nature of disease systems. We describe an analytical framework—the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework—for the identification of optimal surveillance designs through mathematical representations of disease and surveillance processes, definition of objective functions, and numerical optimization. We then apply the framework to the problem of selecting candidate sites to expand an existing surveillance network under alternative objectives of: (1) improving spatial prediction of disease prevalence at unmonitored sites; or (2) estimating the observed effect of a risk factor on disease. Results of this demonstration illustrate how optimal designs are sensitive to both surveillance goals and the underlying spatial pattern of the target disease. The findings affirm the value of designing surveillance systems through quantitative and adaptive analysis of network characteristics and performance. The framework can be applied to the design of surveillance systems tailored to setting-specific disease transmission dynamics and surveillance needs, and can yield improved understanding of tradeoffs between network architectures.     

RNA methyltransferase METTL3 induces intrinsic resistance to gefitinib by combining with MET to regulate PI3K/AKT pathway in lung adenocarcinoma

Clinical research data show that gefitinib greatly improves the progression-free survival of patients, so it is used in advanced non-small cell lung cancer patients with EGFR mutation. However, some patients with EGFR sensitive mutations do not have good effects on initial gefitinib treatment, and this mechanism is rarely studied. METTL3, a part of N6-adenosine-methyltransferase, has been reported to play an important role in a variety of tumours. In this study, we found that METTL3 is up-regulated in gefitinib-resistant tissues compared to gefitinib-sensitive tissues. Cell function experiments have proved that under the treatment of gefitinib, METTL3 knockdown promotes apoptosis and inhibits proliferation of lung cancer cells. Mechanistic studies have shown that METTL3 combines with MET and causes the PI3K/AKT signalling pathway to be manipulated, which affects the sensitivity of lung cancer cells to gefitinib. Therefore, our research shows that METTL3 can be used as a molecular marker to predict the efficacy of EGFR-TKI therapy in patients, and METTL3 may be a potential therapeutic target.     

Carboxyl-Dominant Oxygen Rich Carbon for Improved Sodium Ion Storage: Synergistic Enhancement of Adsorption and Intercalation Mechanisms

Oxygen-containing groups in carbon materials have been shown to affect the carbon anode performance of sodium ion batteries; however, precise identification of the correlation between specific oxygen specie and Na⁺ storage behavior still remains challenging as various oxygen groups coexist in the carbon framework. Herein, a postengineering method via a mechanochemistry process is developed to achieve accurate doping of (20.12 at%) carboxyl groups in a carbon framework. The constructed carbon anode delivers all-round improvements in Na⁺ storage properties in terms of a large reversible capacity (382 mAg⁻¹ at 30 mA g⁻¹), an excellent rate capability (153 mAg⁻¹ at 2 A g⁻¹) as well as good cycling stability (141 mAg⁻¹ after 2000 cycles at 1.5 A g⁻¹). Control experiments, kinetic analysis, density functional theory calculations, and operando measurements collectively demonstrate that carboxyl groups not only act as active sites for Na⁺ capacitive adsorption through suitable electrostatic interactions, but also gradually expand d-spacing by inducing a repulsive force between carbon layers with Na⁺ preadsorbed, and hence facilitate diffusion-controlled Na⁺ insertion process. This work provides a new insight in the rational tunning of oxygen-containing groups in carbon for boosting reversible Na⁺ storage through a synergy of adsorption and intercalation processes.     

Inhibition of Syk promotes chemical reprogramming of fibroblasts via metabolic rewiring and H2S production

Chemical compounds have recently been introduced as alternative and non‐integrating inducers of pluripotent stem cell fate. However, chemical reprogramming is hampered by low efficiency and the molecular mechanisms remain poorly characterized. Here, we show that inhibition of spleen tyrosine kinase (Syk) by R406 significantly promotes mouse chemical reprogramming. Mechanistically, R406 alleviates Syk / calcineurin (Cn) / nuclear factor of activated T cells (NFAT) signaling‐mediated suppression of glycine, serine, and threonine metabolic genes and dependent metabolites. Syk inhibition upregulates glycine level and downstream transsulfuration cysteine biosynthesis, promoting cysteine metabolism and cellular hydrogen sulfide (H₂S) production. This metabolic rewiring decreased oxidative phosphorylation and ROS levels, enhancing chemical reprogramming. In sum, our study identifies Syk‐Cn‐NFAT signaling axis as a new barrier of chemical reprogramming and suggests metabolic rewiring and redox homeostasis as important opportunities for controlling cell fates.     

The fragility of a structurally diverse duplication block triggers recurrent genomic amplification

The human genome contains hundreds of large, structurally diverse blocks that are insufficiently represented in the reference genome and are thus not amenable to genomic analyses. Structural diversity in the human population suggests that these blocks are unstable in the germline; however, whether or not these blocks are also unstable in the cancer genome remains elusive. Here we report that the 500 kb block called KRTAP_region_1 (KRTAP-1) on 17q12–21 recurrently demarcates the amplicon of the ERBB2 (HER2) oncogene in breast tumors. KRTAP-1 carries numerous tandemly-duplicated segments that exhibit diversity within the human population. We evaluated the fragility of the block by cytogenetically measuring the distances between the flanking regions and found that spontaneous distance outliers (i.e DNA breaks) appear more frequently at KRTAP-1 than at the representative common fragile site (CFS) FRA16D. Unlike CFSs, KRTAP-1 is not sensitive to aphidicolin. The exonuclease activity of DNA repair protein Mre11 protects KRTAP-1 from breaks, whereas CtIP does not. Breaks at KRTAP-1 lead to the palindromic duplication of the ERBB2 locus and trigger Breakage-Fusion-Bridge cycles. Our results indicate that an insufficiently investigated area of the human genome is fragile and could play a crucial role in cancer genome evolution.     

NOD2 agonist murabutide alleviates radiation-induced injury through DNA damage response pathway mediated by ATR

Injury-induced by ionizing radiation (IR) severely reduces the quality of life of victims. The development of radiation protectors is regarded as one of the most resultful strategies to alleviate damages caused by IR exposure. In the present study, we investigated the radioprotective effects of the agonist of nucleotide-binding-oligomerization-domain-containing proteins 2 called murabutide (MBD) and clarified the potential mechanisms. Our results showed that the pretreatment with MBD effectively protected cultured cells and mice against IR-induced toxicity and the pretreatment with MBD in vitro and in vitro also inhibited apoptosis caused by IR exposure. The downregulation of γ-H2AX and the upregulation of ATR signaling pathways by MBD treatment indicated that the radioprotective effects of MBD were due to the stimulation of DNA damage response (DDR) pathway to repair DNA double-strand breaks caused by IR exposure. As the radioprotective effects of MBD were diminished by the ATR selective inhibitor rather than the ATM inhibitor, ATR pathway was confirmed to be a more crucial checkpoint pathway in mediating the stimulation of DDR pathway by MBD. Taken together, our data provide a novel and effective protector to relieve the injury induced by IR exposure.     

Minimum sample sizes for invasion genomics: Empirical investigation in an invasive whitefly

Analysis of population genetics provides insights into the evolutionary processes, among which the sample size choice is per se a crucial issue in the analysis. Genome‐wide high‐throughput techniques based on RADseq have been increasingly used in studies on the population genomics of invasive species. However, there is little information available regarding optimal sample sizes for analyzing population genomics of invasive species. In this study, we first use type IIB endonucleases restriction site‐associated DNA (2b‐RAD) to mine thousands of single nucleotide polymorphisms (SNPs) for native and introduced populations in Q1 clade (SPB and 17JN) and Q2 clade (ISQ and UAS0601) of the whitefly, Bemisia tabaci (Gennadius) MED (also known as B. tabaci biotype Q). Then, we used resampling techniques to create simulated populations with a random subset of individuals and 3,000 SNPs to determine how many individuals should be sampled for accurate estimates of intra‐ and interpopulation genetic diversity. We calculated the intrapopulation genetic diversity parameters (unbiased expected heterozygosity, observed heterozygosity, and the number of effect alleles) and pairwise genetic differentiation F_ST; finally, an ad hoc statistic, ΔK, was used to determine the optimal value. Our results showed that a sample size greater than four individuals (n ≥ 4) has little impact on estimates of genetic diversity within whitefly populations; moreover, precise estimate of F_ST can be easily achieved at a very small simple size (n = 3 or 4). Our results will provide in‐depth understanding of the optimization of sampling schemes in population genomics of invasive species.