ABC2A: A Straightforward and Fast Method for the Accurate Backmapping of RNA Coarse-Grained Models to All-Atom Structures.

RNAs play crucial roles in various essential biological functions, including catalysis and gene regulation. Despite the widespread use of coarse-grained (CG) models/simulations to study RNA 3D structures and dynamics, their direct application is challenging due to the lack of atomic detail. Therefore, the reconstruction of full atomic structures is desirable. In this study, we introduced a straightforward method called ABC2A for reconstructing all-atom structures from RNA CG models. ABC2A utilizes diverse nucleotide fragments from known structures to assemble full atomic structures based on the CG atoms. The diversification of assembly fragments beyond standard A-form ones, commonly used in other programs, combined with a highly simplified structure refinement process, ensures that ABC2A achieves both high accuracy and rapid speed. Tests on a recent large dataset of 361 RNA experimental structures (30-692 nt) indicate that ABC2A can reconstruct full atomic structures from three-bead CG models with a mean RMSD of ~0.34 Å from experimental structures and an average runtime of ~0.5 s (maximum runtime < 2.5 s). Compared to the state-of-the-art Arena, ABC2A achieves a ~25% improvement in accuracy and is five times faster in speed.

RNA 3D Structure Prediction: Progress and Perspective.

Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding.

DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures. This includes molecular dynamics simulations to analyze DNA dynamics, flexibility, and ion binding. We also explore various coarse-grained models used for DNA structure prediction or folding, along with fragment assembly methods for constructing DNA 3D structures. Furthermore, we also discuss the advantages and disadvantages of these methods and highlight their differences.

cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation.

Knowledge-based statistical potentials are very important for RNA 3-dimensional (3D) structure prediction and evaluation. In recent years, various coarse-grained (CG) and all-atom models have been developed for predicting RNA 3D structures, while there is still lack of reliable CG statistical potentials not only for CG structure evaluation but also for all-atom structure evaluation at high efficiency. In this work, we have developed a series of residue-separation-based CG statistical potentials at different CG levels for RNA 3D structure evaluation, namely cgRNASP, which is composed of long-ranged and short-ranged interactions by residue separation. Compared with the newly developed all-atom rsRNASP, the short-ranged interaction in cgRNASP was involved more subtly and completely. Our examinations show that, the performance of cgRNASP varies with CG levels and compared with rsRNASP, cgRNASP has similarly good performance for extensive types of test datasets and can have slightly better performance for the realistic dataset-RNA-Puzzles dataset. Furthermore, cgRNASP is strikingly more efficient than all-atom statistical potentials/scoring functions, and can be apparently superior to other all-atom statistical potentials and scoring functions trained from neural networks for the RNA-Puzzles dataset. cgRNASP is available at https://github.com/Tan-group/cgRNASP.

Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions.

RNA pseudoknots are a kind of important tertiary motif, and the structures and stabilities of pseudoknots are generally critical to the biological functions of RNAs with the motifs. In this work, we have carefully refined our previously developed coarse-grained model with salt effect through involving a new coarse-grained force field and a replica-exchange Monte Carlo algorithm, and employed the model to predict structures and stabilities of complex RNA pseudoknots in ion solutions beyond minimal H-type pseudoknots. Compared with available experimental data, the newly refined model can successfully predict 3D structures from sequences for the complex RNA pseudoknots including SARS-CoV-2 programming-1 ribosomal frameshifting element and Zika virus xrRNA, and can reliably predict the thermal stabilities of RNA pseudoknots with various sequences and lengths over broad ranges of monovalent/divalent salts. In addition, for complex pseudoknots including SARS-CoV-2 frameshifting element, our analyses show that their thermally unfolding pathways are mainly dependent on the relative stabilities of unfolded intermediate states, in analogy to those of minimal H-type pseudoknots.

Ab initio predictions for 3D structure and stability of single- and double-stranded DNAs in ion solutions.

The three-dimensional (3D) structure and stability of DNA are essential to understand/control their biological functions and aid the development of novel materials. In this work, we present a coarse-grained (CG) model for DNA based on the RNA CG model proposed by us, to predict 3D structures and stability for both dsDNA and ssDNA from the sequence. Combined with a Monte Carlo simulated annealing algorithm and CG force fields involving the sequence-dependent base-pairing/stacking interactions and an implicit electrostatic potential, the present model successfully folds 20 dsDNAs (≤52nt) and 20 ssDNAs (≤74nt) into the corresponding native-like structures just from their sequences, with an overall mean RMSD of 3.4Å from the experimental structures. For DNAs with various lengths and sequences, the present model can make reliable predictions on stability, e.g., for 27 dsDNAs with/without bulge/internal loops and 24 ssDNAs including pseudoknot, the mean deviation of predicted melting temperatures from the corresponding experimental data is only ~2.0°C. Furthermore, the model also quantificationally predicts the effects of monovalent or divalent ions on the structure stability of ssDNAs/dsDNAs.

FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures.

Knowledge of RNA three-dimensional (3D) structures is critical to understanding the important biological functions of RNAs. Although various structure prediction models have been developed, the high-accuracy predictions of RNA 3D structures are still limited to the RNAs with short lengths or with simple topology. In this work, we proposed a new model, namely FebRNA, for building RNA 3D structures through fragment assembly based on coarse-grained (CG) fragment ensembles. Specifically, FebRNA is composed of four processes: establishing the library of different types of non-redundant CG fragment ensembles regardless of the sequences, building CG 3D structure ensemble through fragment assembly, identifying top-scored CG structures through a specific CG scoring function, and rebuilding the all-atom structures from the top-scored CG ones. Extensive examination against different types of RNA structures indicates that FebRNA consistently gives the reliable predictions on RNA 3D structures, including pseudoknots, three-way junctions, four-way and five-way junctions, and RNAs in the RNA-Puzzles. FebRNA is available on the Web site: https://github.com/Tan-group/FebRNA.

Radiomics based on pretreatment MRI for predicting distant metastasis of nasopharyngeal carcinoma: A preliminary study.

Objective: To explore the feasibility of predicting distant metastasis (DM) of nasopharyngeal carcinoma (NPC) patients based on MRI radiomics model. Methods: A total of 146 patients with NPC pathologically confirmed, who did not exhibit DM before treatment, were retrospectively reviewed and followed up for at least one year to analyze the DM risk of the disease. The MRI images of these patients including T2WI and CE-T1WI sequences were extracted. The cases were randomly divided into training group (n=116) and validation group (n=30). The images were filtered before radiomics feature extraction. The least absolute shrinkage and selection operator (LASSO) regression was used to develop the dimension of texture parameters and the logistic regression was used to construct the prediction model. The ROC curve and calibration curve were used to evaluate the predictive performance of the model, and the area under curve (AUC), accuracy, sensitivity, and specificity were calculated. Results: 72 patients had DM and 74 patients had no DM. The AUC, accuracy, sensitivity and specificity of the model were 0. 80 (95% CI: 0.72~0. 88), 75.0%, 76.8%, 73.3%. and0.70 (95% CI: 0.51~0.90), 66.7%, 72.7%, 63.2% in training group and validation group, respectively. Conclusion: The radiomics model based on logistic regression algorithm has application potential for evaluating the DM risk of patients with NPC.

Receptor-like protein kinase BAK1 promotes K+ uptake by regulating H+-ATPase AHA2 under low potassium stress.

Potassium (K+) is one of the essential macronutrients for plant growth and development. However, the available K+ concentration in soil is relatively low. Plant roots can perceive low K+ (LK) stress, then enhance high-affinity K+ uptake by activating H+-ATPases in root cells, but the mechanisms are still unclear. Here, we identified the receptor-like protein kinase Brassinosteroid Insensitive 1-Associated Receptor Kinase 1 (BAK1) that is involved in LK response by regulating the Arabidopsis (Arabidopsis thaliana) plasma membrane H+-ATPase isoform 2 (AHA2). The bak1 mutant showed leaf chlorosis phenotype and reduced K+ content under LK conditions, which was due to the decline of K+ uptake capacity. BAK1 could directly interact with the AHA2 C terminus and phosphorylate T858 and T881, by which the H+ pump activity of AHA2 was enhanced. The bak1 aha2 double mutant also displayed a leaf chlorosis phenotype that was similar to their single mutants. The constitutively activated form AHA2Δ98 and phosphorylation-mimic form AHA2T858D or AHA2T881D could complement the LK sensitive phenotypes of both aha2 and bak1 mutants. Together, our data demonstrate that BAK1 phosphorylates AHA2 and enhances its activity, which subsequently promotes K+ uptake under LK conditions.

rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation.

Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.

The Influence of Personality Traits on School Bullying: A Moderated Mediation Model.

We recruited 1,631 middle and high school students to explore the relationship between personality traits and school bullying, and the moderated and mediating roles of self-concept and loneliness on this relationship. Results showed that (1) neuroticism had a significant positive predictive effect on being bullied, extroversion had a significant negative predictive effect on being bullied, and agreeableness had a significant negative predictive effect on bullying/being bullied; (2) loneliness played a mediating role between neuroticism and bullied behaviors, extroversion and bullying behaviors, and agreeableness and bullying/bullied behaviors; (3) self-concept played a moderating role on the mediation pathway of loneliness on neuroticism, extraversion, agreeableness and bullying behaviors. Therefore, to reduce the frequency of school bullying among adolescents, we should not only reduce their levels of loneliness but also improve their levels of self-concept.

Salt-Dependent RNA Pseudoknot Stability: Effect of Spatial Confinement.

Macromolecules, such as RNAs, reside in crowded cell environments, which could strongly affect the folded structures and stability of RNAs. The emergence of RNA-driven phase separation in biology further stresses the potential functional roles of molecular crowding. In this work, we employed the coarse-grained model that was previously developed by us to predict 3D structures and stability of the mouse mammary tumor virus (MMTV) pseudoknot under different spatial confinements over a wide range of salt concentrations. The results show that spatial confinements can not only enhance the compactness and stability of MMTV pseudoknot structures but also weaken the dependence of the RNA structure compactness and stability on salt concentration. Based on our microscopic analyses, we found that the effect of spatial confinement on the salt-dependent RNA pseudoknot stability mainly comes through the spatial suppression of extended conformations, which are prevalent in the partially/fully unfolded states, especially at low ion concentrations. Furthermore, our comprehensive analyses revealed that the thermally unfolding pathway of the pseudoknot can be significantly modulated by spatial confinements, since the intermediate states with more extended conformations would loss favor when spatial confinements are introduced.

A potassium-sensing niche in Arabidopsis roots orchestrates signaling and adaptation responses to maintain nutrient homeostasis.

Organismal homeostasis of the essential ion K+ requires sensing of its availability, efficient uptake, and defined distribution. Understanding plant K+ nutrition is essential to advance sustainable agriculture, but the mechanisms underlying K+ sensing and the orchestration of downstream responses have remained largely elusive. Here, we report where plants sense K+ deprivation and how this translates into spatially defined ROS signals to govern specific downstream responses. We define the organ-scale K+ pattern of roots and identify a postmeristematic K+-sensing niche (KSN) where rapid K+ decline and Ca2+ signals coincide. Moreover, we outline a bifurcating low-K+-signaling axis of CIF peptide-activated SGN3-LKS4/SGN1 receptor complexes that convey low-K+-triggered phosphorylation of the NADPH oxidases RBOHC, RBOHD, and RBOHF. The resulting ROS signals simultaneously convey HAK5 K+ uptake-transporter induction and accelerated Casparian strip maturation. Collectively, these mechanisms synchronize developmental differentiation and transcriptome reprogramming for maintaining K+ homeostasis and optimizing nutrient foraging by roots.

Different characteristics of infants diagnosed with congenital choledochal malformation prenatally or postnatally.

The general condition, clinical and pathological characteristics, and treatment regimens of patients prenatally and postnatally diagnosed with congenital choledochal malformation (CM) were analyzed in order to investigate the clinical significance of early diagnosis, treatment, and intervention in CM. We retrospectively analyzed 33 children who were admitted to the Children's Hospital of Soochow University between 1 March 2010 and 31 May 2019, and their diagnosis of CM was confirmed by radiological, surgical and pathological findings. All the patients were under 36 months of age. The patients were divided into prenatally diagnosed and postnatally diagnosed groups. There were 16 and 17 CM patients in the prenatally and postnatally diagnosed groups, respectively, with a preponderance of females in both groups. Compared with the prenatally diagnosed group, the postnatally diagnosed group had a higher incidence of abdominal pain and vomiting (p < 0.05) and higher AST, GGT, and TB levels (p < 0.05). Although postoperative histopathological examination showed inflammation in both groups, congestion in the cyst walls and fibrous tissue hyperplasia were more significant in the postnatally diagnosed group (p < 0.05). In addition, operation time, length of time required to resume a normal diet after surgery, and total length of hospitalization differed between the 2 groups (p < 0.05), with the prenatally diagnosed group having a relatively longer operation time and taking longer to resume a normal diet after surgery. However, the total length of hospitalization in the prenatally diagnosed group was shorter than that in the postnatally diagnosed group. Compared with prenatally diagnosed CM patients, more symptoms, greater severity of symptoms, and more time to recovery after surgery were observed in postnatally diagnosed CM patients.

RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures.

The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

Predictors of chest drainage of pneumothorax in neonates.

This is a retrospective, single-center observational study to explore the predictors of chest drainage for neonatal pneumothorax. A total of 183 neonates (age ≤28 days) who presented to the Children's Hospital of Soochow University between January 1, 2015 and December 31, 2018 for pneumothorax or developed pneumothorax during a hospital stay were included. Demographic data, clinical presentation, and imaging characteristics of neonatal pneumothorax were collected and analyzed. We used univariate and multivariate logistic regression analyses to determine significant predictors of chest drainage of pneumothorax in neonates. Pneumothorax occurred within 24 h after birth in 131 (71.6%) cases, between 24 and 48 h after birth in 41 (22.4%) cases, and 48 h after birth in 11 (6.0%) cases. Univariate and multivariate logistic regression analyses revealed that lung collapse ≥1/3 on initial chest X-ray (OR 4.99, 95%CI 2.25-11.07), chest retractions (OR 8.12, 95%CI 2.88-22.89), cyanosis (OR 2.25, 95%CI 1.08-4.66), and frothing from mouth (OR 2.49, 95%CI 1.12-5.49) (P<0.05 for all) were significant predictors of the need for chest drainage due to pneumothorax. In conclusion, the thorough evaluation of the above predictive factors can guide treatment and improve patient outcome.

Predictors of chest drainage of pneumothorax in neonates

This is a retrospective, single-center observational study to explore the predictors of chest drainage for neonatal pneumothorax. A total of 183 neonates (age ≤28 days) who presented to the Children's Hospital of Soochow University between January 1, 2015 and December 31, 2018 for pneumothorax or developed pneumothorax during a hospital stay were included. Demographic data, clinical presentation, and imaging characteristics of neonatal pneumothorax were collected and analyzed. We used univariate and multivariate logistic regression analyses to determine significant predictors of chest drainage of pneumothorax in neonates. Pneumothorax occurred within 24 h after birth in 131 (71.6%) cases, between 24 and 48 h after birth in 41 (22.4%) cases, and 48 h after birth in 11 (6.0%) cases. Univariate and multivariate logistic regression analyses revealed that lung collapse ≥1/3 on initial chest X-ray (OR 4.99, 95%CI 2.25-11.07), chest retractions (OR 8.12, 95%CI 2.88-22.89), cyanosis (OR 2.25, 95%CI 1.08-4.66), and frothing from mouth (OR 2.49, 95%CI 1.12-5.49) (P<0.05 for all) were significant predictors of the need for chest drainage due to pneumothorax. In conclusion, the thorough evaluation of the above predictive factors can guide treatment and improve patient outcome.

Structure folding of RNA kissing complexes in salt solutions: predicting 3D structure, stability, and folding pathway.

RNA kissing complexes are essential for genomic RNA dimerization and regulation of gene expression, and their structures and stability are critical to their biological functions. In this work, we used our previously developed coarse-grained model with an implicit structure-based electrostatic potential to predict three-dimensional (3D) structures and stability of RNA kissing complexes in salt solutions. For extensive RNA kissing complexes, our model shows great reliability in predicting 3D structures from their sequences, and our additional predictions indicate that the model can capture the dependence of 3D structures of RNA kissing complexes on monovalent/divalent ion concentrations. Moreover, the comparisons with extensive experimental data show that the model can make reliable predictions on the stability for various RNA kissing complexes over wide ranges of monovalent/divalent ion concentrations. Notably, for RNA kissing complexes, our further analyses show the important contribution of coaxial stacking to the 3D structures and stronger stability than the corresponding kissing-interface duplexes at high salts. Furthermore, our comprehensive analyses for RNA kissing complexes reveal that the thermally folding pathway for a complex sequence is mainly determined by the relative stability of two possible folded states of kissing complex and extended duplex, which can be significantly modulated by its sequence.

What is the best reference state for building statistical potentials in RNA 3D structure evaluation?

Knowledge-based statistical potentials have been shown to be efficient in protein structure evaluation/prediction, and the core difference between various statistical potentials is attributed to the choice of reference states. However, for RNA 3D structure evaluation, a comprehensive examination on reference states is still lacking. In this work, we built six statistical potentials based on six reference states widely used in protein structure evaluation, including averaging, quasi-chemical approximation, atom-shuffled, finite-ideal-gas, spherical-noninteracting, and random-walk-chain reference states, and we examined the six reference states against three RNA test sets including six subsets. Our extensive examinations show that, overall, for identifying native structures and ranking decoy structures, the finite-ideal-gas and random-walk-chain reference states are slightly superior to others, while for identifying near-native structures, there is only a slight difference between these reference states. Our further analyses show that the performance of a statistical potential is apparently dependent on the quality of the training set. Furthermore, we found that the performance of a statistical potential is closely related to the origin of test sets, and for the three realistic test subsets, the six statistical potentials have overall unsatisfactory performance. This work presents a comprehensive examination on the existing reference states and statistical potentials for RNA 3D structure evaluation.

Forecasting and predicting intussusception in children younger than 48 months in Suzhou using a seasonal autoregressive integrated moving average model.

OBJECTIVE: The aims of this study were to highlight some epidemiological aspects of intussusception cases younger than 48 months and to develop a forecasting model for the occurrence of intussusception in children younger than 48 months in Suzhou. DESIGN: A retrospective study of intussusception cases that occurred between January 2007 and December 2017. SETTING: Retrospective chart reviews of intussusception paediatric patients in a large Children's hospital in South-East China were performed. PARTICIPANTS: The hospital records of 13 887 intussusception cases in patients younger than 48 months were included in this study. INTERVENTIONS: The modelling process was conducted using the appropriate module in SPSS V.23.0. METHODS: The Box-Jenkins approach was used to fit a seasonal autoregressive integrated moving average (ARIMA) model to the monthly recorded intussusception cases in patients younger than 48 months in Suzhou from 2007 to 2016. RESULTS: Epidemiological analysis revealed that intussusception younger than 48 months was reported continuously throughout the year, with peaks in the late spring and early summer months. The most affected age group was younger than 36 months. The time-series analysis showed that an ARIMA (1,0,1 1,1,1)12 model offered the best fit for surveillance data of intussusception younger than 48 months. This model was used to predict intussusception younger than 48 months for the year 2017, and the fitted data showed considerable agreement with the actual data. CONCLUSION: ARIMA models are useful for monitoring intussusception in patients younger than 48 months and provide an estimate of the variability to be expected in future cases in Suzhou. The models are helpful for predicting intussusception cases in Suzhou and could be useful for developing early warning systems. They may also play a key role in early detection, timely treatment and prevention of serious complications in cases of intussusception.