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The DNA double helix structure is stabilized by base-pairing and base-stacking interactions. However, a comprehensive understanding of dinucleotide base-stacking energetics is lacking. Here we combined multiplexed DNA-based point accumulation in nanoscale topography (DNA-PAINT) imaging with designer DNA nanostructures and measured the free energy of dinucleotide base stacking at the single-molecule level. Multiplexed imaging enabled us to extract the binding kinetics of an imager strand with and without additional dinucleotide stacking interactions. The DNA-PAINT data showed that a single additional dinucleotide base stacking results in up to 250-fold stabilization for the DNA duplex nanostructure. We found that the dinucleotide base-stacking energies vary from −0.95 ± 0.12 kcal mol −1 to −3.22 ± 0.04 kcal mol −1 for C|T and A|C base-stackings, respectively. We demonstrate the application of base-stacking energetics in designing DNA-PAINT probes for multiplexed super-resolution imaging, and efficient assembly of higher-order DNA nanostructures. Our results will aid in designing functional DNA nanostructures, and DNA and RNA aptamers, and facilitate better predictions of the local DNA structure. DNA-based point accumulation in nanoscale topography (DNA-PAINT) super-resolution imaging reveals the kinetics of the free energy of base-stacking interactions of all 16 dinucleotide combinations at the single-molecule level in patterned DNA-origami nanostructures.

Co-phase separation of RNAs and RNA-binding proteins drives the biogenesis of ribonucleoprotein granules. RNAs can also undergo phase transitions in the absence of proteins. However, the physicochemical driving forces of protein-free, RNA-driven phase transitions remain unclear. Here we report that various types of RNA undergo phase separation with system-specific lower critical solution temperatures. This entropically driven phase separation is an intrinsic feature of the phosphate backbone that requires Mg2+ ions and is modulated by RNA bases. RNA-only condensates can additionally undergo enthalpically favourable percolation transitions within dense phases. This is enabled by a combination of Mg2+-dependent bridging interactions between phosphate groups and RNA-specific base stacking and base pairing. Phase separation coupled to percolation can cause dynamic arrest of RNAs within condensates and suppress the catalytic activity of an RNase P ribozyme. Our work highlights the need to incorporate RNA-driven phase transitions into models for ribonucleoprotein granule biogenesis.The physicochemical driving forces of protein-free, RNA-driven phase transitions were previously unclear, but it is now shown that RNAs undergo entropically driven liquid–liquid phase separation upon heating in the presence of magnesium ions. In the condensed phase, RNAs can undergo an enthalpically favourable percolation transition that leads to arrested condensates.

Arrays of modular DNA units can relay information by transforming their internal shape in response to binding of DNA trigger strands. Song et al. synthesized rectangular arrays of double-stranded DNA (see the Perspective by Yang and Lin). Transient square configurations transform into two stable rectangular structures by pinching across a pair of opposing vertices. Binding of DNA trigger strands causes switching into the alternative stable configuration. The tiles thus create a cascade of transformations along a particular pathway, thereby transmitting information about where binding occurred. Science , this issue p. eaan3377 ; see also p. 352 Transformation of DNA arrays is guided by pathways of information propagation among the interconnected molecular units. Information relay at the molecular level is an essential phenomenon in numerous chemical and biological processes, such as intricate signaling cascades. One key challenge in synthetic molecular self-assembly is to construct artificial structures that imitate these complex behaviors in controllable systems. We demonstrated prescribed, long-range information relay in an artificial molecular array assembled from modular DNA structural units. The dynamic DNA molecular array exhibits transformations with programmable initiation, propagation, and regulation. The transformation of the array can be initiated at selected units and then propagated, without addition of extra triggers, to neighboring units and eventually the entire array. The specific information pathways by which this transformation occurs can be controlled by altering the design of individual units and the arrays.

Hydrophobic base stacking is a major contributor to DNA double-helix stability. We report the discovery of specific unstacking effects in certain semihydrophobic environments. Water-miscible ethylene glycol ethers are found to modify structure, dynamics, and reactivity of DNA bymechanisms possibly related to a biologically relevant hydrophobic catalysis. Spectroscopic data and optical tweezers experiments show that base-stacking energies are reduced while base-pair hydrogen bonds are strengthened. We propose that a modulated chemical potential of water can promote “longitudinal breathing” and the formation of unstacked holes while base unpairing is suppressed. Flow linear dichroism in 20% diglyme indicates a 20 to 30% decrease in persistence length of DNA, supported by an increased flexibility in single-molecule nanochannel experiments in poly(ethylene glycol). A limited (3 to 6%) hyperchromicity but unaffected circular dichroism is consistent with transient unstacking events while maintaining an overall average B-DNA conformation. Further information about unstacking dynamics is obtained from the binding kinetics of large thread-intercalating ruthenium complexes, indicating that the hydrophobic effect provides a 10 to 100 times increased DNA unstacking frequency and an “open hole” population on the order of 10−2 compared to 10−4 in normal aqueous solution. Spontaneous DNA strand exchange catalyzed by poly(ethylene glycol) makes us propose that hydrophobic residues in the L2 loop of recombination enzymes RecA and Rad51 may assist gene recombination via modulation of water activity near the DNA helix by hydrophobic interactions, in the manner described here. We speculate that such hydrophobic interactions may have catalytic roles also in other biological contexts, such as in polymerases.

Base stacking interactions between adjacent bases in DNA and RNA are important for many biological processes and in biotechnology applications. Previous work has estimated stacking energies between pairs of bases, but contributions of individual bases has remained unknown. Here, we use a Centrifuge Force Microscope for high-throughput single molecule experiments to measure stacking energies between adjacent bases. We found stacking energies strongest between purines (G|A at −2.3 ± 0.2 kcal/mol) and weakest between pyrimidines (C|T at −0.5 ± 0.1 kcal/mol). Hybrid stacking with phosphorylated, methylated, and RNA nucleotides had no measurable effect, but a fluorophore modification reduced stacking energy. We experimentally show that base stacking can influence stability of a DNA nanostructure, modulate kinetics of enzymatic ligation, and assess accuracy of force fields in molecular dynamics simulations. Our results provide insights into fundamental DNA interactions that are critical in biology and can inform design in biotechnology applications. In this work, the authors use a centrifuge force microscope for high-throughput single-molecule experiments to elucidate stacking energies between individual bases of DNA.

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m 3 /m 3 and a coefficient of determination (R 2 ) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies. The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization. The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at \"http://rna.physics.missouri.edu\".

Accurate knowledge of RNA hybridization is essential for understanding RNA structure and function. Here we mechanically unzip and rezip a 2-kbp RNA hairpin and derive the 10 nearest-neighbor base pair (NNBP) RNA free energies in sodium and magnesium with 0.1 kcal/mol precision using optical tweezers. Notably, force–distance curves (FDCs) exhibit strong irreversible effects with hysteresis and several intermediates, precluding the extraction of the NNBP energies with currently available methods. The combination of a suitable RNA synthesis with a tailored pulling protocol allowed us to obtain the fully reversible FDCs necessary to derive the NNBP energies. We demonstrate the equivalence of sodium and magnesium free-energy salt corrections at the level of individual NNBP. To characterize the irreversibility of the unzipping–rezipping process, we introduce a barrier energy landscape of the stem–loop structures forming along the complementary strands, which compete against the formation of the native hairpin. This landscape correlates with the hysteresis observed along the FDCs. RNA sequence analysis shows that base stacking and base pairing stabilize the stem–loops that kinetically trap the long-lived intermediates observed in the FDC. Stem–loops formation appears as a general mechanism to explain a wide range of behaviors observed in RNA folding.

Forecasting the weather in an area characterized by erratic weather patterns and unpredictable climate change is a challenging endeavour. The weather is classified as a non-linear system since it is influenced by various factors that contribute to climate change, such as humidity, average temperature, sea level pressure, and rainfall. A reliable forecasting system is crucial in several industries, including transportation, agriculture, tourism, & development. This study showcases the effectiveness of data mining, meteorological analysis, and machine learning techniques such as RNN-LSTM, TensorFlow Decision Forest (TFDF), and model stacking (including ElasticNet, GradientBoost, KRR, and Lasso) in improving the precision and dependability of weather forecasting. The stacking model strategy entails aggregating multiple base models into a meta-model to address issues of overfitting and underfitting, hence improving the accuracy of the prediction model. To carry out the study, a comprehensive 60-year meteorological record from Bangladesh was gathered, encompassing data on rainfall, humidity, average temperature, and sea level pressure. The results of this study suggest that the stacking average model outperforms the TFDF and RNN-LSTM models in predicting average temperature. The stacking average model achieves an RMSLE of 1.3002, which is a 10.906% improvement compared to the TFDF model. It is worth noting that the TFDF model had previously outperformed the RNN-LSTM model. The performance of the individual stacking model is not as impressive as that of the average model, with the validation results being better in TFDF.

The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost algorithms, a stacking ensemble model was developed to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features. Multiple evaluation metrics, including the specificity, sensitivity, negative predictive value, positive predictive value, accuracy, Matthews correlation coefficient, and the Youden index, were used to assess the model’s performance. The stacked ensemble model integrated the strengths of the nine base models and consistently outperformed individual base models in classifying GTV (area under the curve [AUC] = 0.928), brainstem (AUC = 0.932), and normal brain tissue (AUC = 0.942). Among the base models, the support vector machine model demonstrated the best performance in the three classifications (AUC = 0.922, 0.909, and 0.928). The higher performance of the stacked ensemble model highlighted the low performance of other models, including the decision tree (AUC = 0.709, 0.706, 0.804) and k-nearest neighbors (AUC = 0.721, 0.663, 0.729) models in certain contexts, such as when faced with high-dimensional feature spaces. While machine learning shows significant promise in medical image analysis, relying solely on a single model may lead to suboptimal results. By combining the strengths of various algorithms, the stacking ensemble model offers a better solution for the classification of brain metastases based on radiomic features.