Multiple sequence alignment-based RNA language model and its application to structural inference.

Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives.

Characterizing RNA structures and functions have mostly been focused on 2D, secondary and 3D, tertiary structures. Recent advances in experimental and computational techniques for probing or predicting RNA solvent accessibility make this 1D representation of tertiary structures an increasingly attractive feature to explore. Here, we provide a survey of these recent developments, which indicate the emergence of solvent accessibility as a simple 1D property, adding to secondary and tertiary structures for investigating complex structure-function relations of RNAs.

Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.

MOTIVATION: Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent advancement of protein structure prediction is the highly accurate prediction of distance-based contact maps of proteins. RESULTS: Here, we showed that by integrated deep learning with physics-inferred secondary structures, co-evolutionary information and multiple sequence-alignment sampling, we can achieve RNA contact-map prediction at a level of accuracy similar to that in protein contact-map prediction. More importantly, highly accurate prediction for top L long-range contacts can be assured for those RNAs with a high effective number of homologous sequences (Neff > 50). The initial use of the predicted contact map as distance-based restraints confirmed its usefulness in 3D structure prediction. AVAILABILITY AND IMPLEMENTATION: SPOT-RNA-2D is available as a web server at https://sparks-lab.org/server/spot-rna-2d/ and as a standalone program at https://github.com/jaswindersingh2/SPOT-RNA-2D. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model.

MOTIVATION: Accurate prediction of protein contact-map is essential for accurate protein structure and function prediction. As a result, many methods have been developed for protein contact map prediction. However, most methods rely on protein-sequence-evolutionary information, which may not exist for many proteins due to lack of naturally occurring homologous sequences. Moreover, generating evolutionary profiles is computationally intensive. Here, we developed a contact-map predictor utilizing the output of a pre-trained language model ESM-1b as an input along with a large training set and an ensemble of residual neural networks. RESULTS: We showed that the proposed method makes a significant improvement over a single-sequence-based predictor SSCpred with 15% improvement in the F1-score for the independent CASP14-FM test set. It also outperforms evolutionary-profile-based methods trRosetta and SPOT-Contact with 48.7% and 48.5% respective improvement in the F1-score on the proteins without homologs (Neff = 1) in the independent SPOT-2018 set. The new method provides a much faster and reasonably accurate alternative to evolution-based methods, useful for large-scale prediction. AVAILABILITY AND IMPLEMENTATION: Stand-alone-version of SPOT-Contact-LM is available at https://github.com/jas-preet/SPOT-Contact-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-contact-single. The datasets used in this research can also be downloaded from the GitHub. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Efficient Turn-On Zr Based Metal Organic Framework Fluorescent Sensor for Ultrafast Detection of Danofloxacin in Milk Samples.

Metal organic framework, UiO-67 was synthesized by coordinating Zr(IV) with 4,4'-biphenyldicarboxylic acid (BPDC) ligand. Morphology and crystallinity of MOF was confirmed with FE-SEM and PXRD procedure. Danofloxacin (DANO), a veterinary fluoroquinolone antibiotic, was detected in milk by employing UiO-67 as "turn-on" fluorescent sensor. Original photoluminescent (PL) efficiency of UiO-67 sensor was enhanced on its electronic interaction with DANO molecule. Significant PL efficiency enhancement, lower detection limit 0.49 ng/mL (1.37 nM), swift detection (time < 1 min), and excellent linear correlation (R2 = 0.9988) indicated extraordinary sensitivity of developed UiO-67 sensor for DANO. Selectivity and performance of sensor was unaltered in presence of interfering species and detection results were obtained under permissible variation limits. Method applied successfully for ultra-trace detection of DANO residues in milk samples.

Physiological and behavioral assessment of Metaphire posthuma in response to clothianidin insecticide: Insights from molecular and biochemical analysis.

In the present study, Clothianidin [(E) - 1-(2 - chloro-1,3 - thiazol - 5-ylmethyl) - 3-methyl - 2- nitroguanidine] (CLO) was selected as a soil pollutant and earthworm was employed as a test organism. The various responses like biochemical and detoxification process of earthworm Metaphire posthuma towards Clothianidin at lethal and sublethal doses were studied using OECD-standardized toxicological guidelines. The present study examined the toxicity of CLO to earthworms after 28 days of exposure at conc. 0, 1.5, 3, 6, 12 and 24 mg kg-1 in a soil mixture. Biochemical markers including Guaiacol peroxidase (POD), Superoxide dismutase (SOD), Catalase (CAT), Glutathione S-transferase (GST) and content of Malondialdehyde (MDA) in earthworms were measured. Acute toxicity tests revealed that CLO caused a concentration-dependent increase in mortality with LC50 (Lethal concentration) values of 10.960 and 8.201 mg kg-1 for 7th and 14th day respectively. The earthworms were exposed to CLO contaminated soil for 56 days and reflecting the significant decrease in earthworm growth, cocoon and hatchling production. Moreover, enzyme activities such as CAT, SOD, POD and MDA content were significantly enhanced with the increased concentration and exposure period of CLO. Molecular docking studies indicated that CLO primarily interacts to the junction site of SOD and in active centres of CAT, POD and GST. As a result, the current findings imply that the sub chronic CLO exposure can induce variations in physiology and avoidance behaviour of earthworms, oxidative stress as well as alterations in enzyme activities.

Agronomic biofortification of forage crops with zinc and copper for enhancing nutritive potential: a systematic review.

Many developing countries are facing a silent increase in deficiency of micronutrients in forage crops that results in decreased levels of essential nutrients in animals. Micronutrients are essential not only for basic metabolic processes of forage crops but also for sustaining animal health. Forage productivity and quality are severely affected by soil micronutrients deficiencies, especially zinc and copper. This review summarizes the literature highlighting the significance of different methodologies used to increase the biomass and quality of forage so as to enhance the micronutrient content of the forage crops through biofortification. Biofortification is a promising and sustainable agriculture-based strategy to reduce micronutrient deficiency in crops. The experiments and trials conducted at different locations of the world showed that copper and zinc concentrations in animal fodders can be enhanced through the process of foliar application. Additionally, agronomic biofortification showed more promising results, and thus is an outstanding, fast, and cost-effective technique for the immediate enrichment of forage in order to overcome malnutrition in animals. © 2022 Society of Chemical Industry.

Artificial intelligence inspired multilanguage framework for note-taking and qualitative content-based analysis of lectures.

With the advent of technology and digitization, the use of Information and Communication Technology (ICT) and its tools for the imperative dissemination of information to learners are gaining more ground. During the process of the conveyance of lectures, it is mostly observed that students (learners) are supposed to take notes (minutes) of the subject matter being delivered to them. The existence of different factors like disturbance (noise) from the environment, learner's lack of interest, problems with the tutor's voice, and pronunciation, or others, may hinder the practice of preparing (or taking) lecture notes effectively. To tackle such an issue, we propose an artificial intelligence-inspired multilanguage framework for the generation of the lecture script (of complete) and minutes (only important contents) of the lecture (or speech). We also aimed to perform a qualitative content-based analysis of the lecture's content. Furthermore, we have validated the performance(accuracy) of the proposed framework with that of the manual note-taking method. The proposed framework outperforms its counterpart in terms of note-taking and performing the qualitative content-based analysis. In particular, this framework will assist the tutors in getting insights into their lecture delivery methods and materials. It will also help them improvise to a better approach in the future. The students will be benefited from the outcomes as they do not have to invest valuable time in note-taking/preparation.

Sustainable Development Goal for Quality Education (SDG 4): A study on SDG 4 to extract the pattern of association among the indicators of SDG 4 employing a genetic algorithm.

Sustainable Development Goals (SDG) are at the forefront of government initiatives across the world. The SDGs are primarily concerned with promoting sustainable growth via ensuring wellbeing, economic growth, environmental legislation, and academic advancement. One of the most prominent goals of the SDG is to provide learners with high-quality education (SDG 4). This paper aims to look at the perspectives of the Sustainable Development Goals improvised to provide quality education. We also analyze the existing state of multiple initiatives implemented by the Indian government in the pathway to achieving objectives of quality education (SDG 4). Additionally, a case study is considered for understanding the association among the observed indicators of SDG4. For this purpose, exploratory data analysis, and numerical association rule mining in combination with QuantMiner genetic algorithm approaches have been applied. The outcomes reveal the presence of a significant degree of association among these parameters pointing out the fact that understanding the impact of one (or more) indicator on other related indicators is critical for achieving SDG 4 goals (or factors). These findings will assist governing bodies in taking preventive measures while modifying existing policies and ensuring the effective enactment of SDG 4 goals, which also will subsequently aid in the resolution of issues related to other SDGs.

Toward the development of Ac/Ds transposon-mediated gene tagging system for functional genomics in oat (Avena sativa L.).

Cultivated oat (Avena sativa L.) is an important cereal grown worldwide due to its multifunctional uses for animal feed and human food. Oat has lagged behind other cereals in the genetic and genomic studies attributed to its large and complex genomes. Transposon-based genome characterization has been utilized successfully for identifying and determining gene function in large genome cereals. To develop gene tagging and gene-editing resources for oat, maize Activator (Ac) and Dissociation (Ds) transposons were introduced into the oat genome using the biolistic delivery system. A total of 2035 oat calli were bombarded and twenty-four independent, stable transgenic events were obtained. Transformation frequencies were up to 19.0%, and 1.9% for bialaphos and hygromycin selection, respectively. Re-mobilization of the non-autonomous Ds element, by introducing Ac transposase source, led to a transposition frequency up to 16.8%. The properties of ten unique flanking sequences have been characterized to reveal the Ds-tagged sites in the oat genome. Genes at Ds insertion sites showed homology to gibberellin 20-oxidase 3, (1,3;1,4)-beta-D-glucan synthase, and aspartate kinase. This Ac/Ds transposon-based gene tagging system could facilitate and expedite functional genomic studies in oat.

RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis.

MOTIVATION: The accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary coupling or direct coupling analysis. Currently, these coupling analyses relied on manually curated multiple sequence alignments collected in the Rfam database, which contains 3016 families. By comparison, millions of non-coding RNA sequences are known. Here, we established RNAcmap, a fully automatic pipeline that enables evolutionary coupling analysis for any RNA sequences. The homology search was based on the covariance model built by INFERNAL according to two secondary structure predictors: a folding-based algorithm RNAfold and the latest deep-learning method SPOT-RNA. RESULTS: We showed that the performance of RNAcmap is less dependent on the specific evolutionary coupling tool but is more dependent on the accuracy of secondary structure predictor with the best performance given by RNAcmap (SPOT-RNA). The performance of RNAcmap (SPOT-RNA) is comparable to that based on Rfam-supplied alignment and consistent for those sequences that are not in Rfam collections. Further improvement can be made with a simple meta predictor RNAcmap (SPOT-RNA/RNAfold) depending on which secondary structure predictor can find more homologous sequences. Reliable base-pairing information generated from RNAcmap, for RNAs with high effective homologous sequences, in particular, will be useful for aiding RNA structure prediction. AVAILABILITY AND IMPLEMENTATION: RNAcmap is available as a web server at https://sparks-lab.org/server/rnacmap/ and as a standalone application along with the datasets at https://github.com/sparks-lab-org/RNAcmap_standalone. A platform independent and fully configured docker image of RNAcmap is also provided at https://hub.docker.com/r/jaswindersingh2/rnacmap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SPOT-1D-Single: improving the single-sequence-based prediction of protein secondary structure, backbone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning.

MOTIVATION: Knowing protein secondary and other one-dimensional structural properties are essential for accurate protein structure and function prediction. As a result, many methods have been developed for predicting these one-dimensional structural properties. However, most methods relied on evolutionary information that may not exist for many proteins due to a lack of sequence homologs. Moreover, it is computationally intensive for obtaining evolutionary information as the library of protein sequences continues to expand exponentially. Here, we developed a new single-sequence method called SPOT-1D-Single based on a large training dataset of 39 120 proteins deposited prior to 2016 and an ensemble of hybrid long-short-term-memory bidirectional neural network and convolutional neural network. RESULTS: We showed that SPOT-1D-Single consistently improves over SPIDER3-Single and ProteinUnet for secondary structure, solvent accessibility, contact number and backbone angles prediction for all seven independent test sets (TEST2018, SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ, CASP12 and CASP13 free-modeling targets). For example, the predicted three-state secondary structure's accuracy ranges from 72.12% to 74.28% by SPOT-1D-Single, compared to 69.1-72.6% by SPIDER3-Single and 70.6-73% by ProteinUnet. SPOT-1D-Single also predicts SS3 and SS8 with 6.24% and 6.98% better accuracy than SPOT-1D on SPOT-2018 proteins with no homologs (Neff = 1), respectively. The new method's improvement over existing techniques is due to a larger training set combined with ensembled learning. AVAILABILITY AND IMPLEMENTATION: Standalone-version of SPOT-1D-Single is available at https://github.com/jas-preet/SPOT-1D-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-1d-single. The datasets used in this research can also be downloaded from GitHub. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning.

MOTIVATION: The recent discovery of numerous non-coding RNAs (long non-coding RNAs, in particular) has transformed our perception about the roles of RNAs in living organisms. Our ability to understand them, however, is hampered by our inability to solve their secondary and tertiary structures in high resolution efficiently by existing experimental techniques. Computational prediction of RNA secondary structure, on the other hand, has received much-needed improvement, recently, through deep learning of a large approximate data, followed by transfer learning with gold-standard base-pairing structures from high-resolution 3-D structures. Here, we expand this single-sequence-based learning to the use of evolutionary profiles and mutational coupling. RESULTS: The new method allows large improvement not only in canonical base-pairs (RNA secondary structures) but more so in base-pairing associated with tertiary interactions such as pseudoknots, non-canonical and lone base-pairs. In particular, it is highly accurate for those RNAs of more than 1000 homologous sequences by achieving >0.8 F1-score (harmonic mean of sensitivity and precision) for 14/16 RNAs tested. The method can also significantly improve base-pairing prediction by incorporating artificial but functional homologous sequences generated from deep mutational scanning without any modification. The fully automatic method (publicly available as server and standalone software) should provide the scientific community a new powerful tool to capture not only the secondary structure but also tertiary base-pairing information for building three-dimensional models. It also highlights the future of accurately solving the base-pairing structure by using a large number of natural and/or artificial homologous sequences. AVAILABILITY AND IMPLEMENTATION: Standalone-version of SPOT-RNA2 is available at https://github.com/jaswindersingh2/SPOT-RNA2. Direct prediction can also be made at https://sparks-lab.org/server/spot-rna2/. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network.

MOTIVATION: RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition. RESULTS: Using the same training set from the recent predictor RNAsol, RNAsnap2 provides an 11% improvement in median Pearson Correlation Coefficient (PCC) and 9% improvement in mean absolute errors for the same test set of 45 RNA chains. A larger improvement (22% in median PCC) is observed for 31 newly deposited RNA chains that are non-redundant and independent from the training and the test sets. A single-sequence version of RNAsnap2 (i.e. without using sequence profiles generated from homology search by Infernal) has achieved comparable performance to the profile-based RNAsol. In addition, RNAsnap2 has achieved comparable performance for protein-bound and protein-free RNAs. Both RNAsnap2 and RNAsnap2 (SingleSeq) are expected to be useful for searching structural signatures and locating functional regions of non-coding RNAs. AVAILABILITY AND IMPLEMENTATION: Standalone-versions of RNAsnap2 and RNAsnap2 (SingleSeq) are available at https://github.com/jaswindersingh2/RNAsnap2. Direct prediction can also be made at https://sparks-lab.org/server/rnasnap2. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metsulfuron-methyl induced physiological, behavioural and biochemical changes in exotic (Eisenia fetida) and indigenous (Metaphire posthuma) earthworm species: Toxicity and molecular docking studies.

In modern agricultural practices, Metsulfuron-methyl (sulfonylurea herbicide) is widely employed to inhibit the weeds and grasses. The current study revealed that Metaphire posthuma was more sensitive than Eisenia fetida against Metsulfuron-methyl (MSM). The LC50 values for Eisenia fetida were 2884.08 mgkg-1 and 1871.18 mgkg-1after 7 and 14 days, respectively. Similarly, the LC50 values for Metaphire posthuma were 2449.34 mgkg-1 and 1673.10 mgkg-1for 7 and 14 days, respectively. Reproduction parameters were significantly decreased at 400 (T3), 800 (T4) and 1600 (T5) mgkg-1 MSM in E. fetida whereas at 200 (T2), 400 (T3), 800 (T4), 1600 (T5) mgkg-1 MSM in M. posthuma. EC50 of avoidance response for 20% MSM by E. fetida and M. posthuma was recorded 901.76 mgkg-1and 544.21 mgkg-1 respectively. Malondialdehyde (MDA) content along with guaiacol peroxidase (POD), catalase (CAT) and superoxide dismutase (SOD) activities were initially increased up to 21st day by MSM, inducing a slight oxidative stress in earthworms and recovered to control level on 28th day. The GST activities were continuously stimulated throughout the exposure period and enhance the detoxification effect thereby preventing the earthworms from toxins. Molecular docking studies indicated that hydrogen bonding and hydrophobic interactions are key forces in binding between MSM and SOD/CAT/POD/GST. As a result, this is the first study to be reported on physiological, behavioural and biochemical changes in two different earthworm species under the exposure of sulfonyl urea herbicide.

Genome-wide analysis of wall associated kinase (WAK) gene family in barley.

In plants, wall associated kinases (WAKs) form a unique subfamily of receptor like-kinases (RLKs). In Arabidopsis thaliana, WAK-RLKs are known to regulate biotic stress, cell expansion, and metal tolerance, but their detailed characterization in barley is lacking. In this study, we identified a total of 91 WAK genes in the barley genome and classified them into five groups. Evolutionary analysis of HvWAKs with AtWAKs revealed their species-specific expansion. The maximum number (19 to 20) of WAK genes were located on chromosomes 3, 5 and 6. WAK proteins exhibited similar types of motif distribution in their group. Characterization of a Ds transposon insertion mutant of the wak1 revealed differences in the root length. Further, HvSPL23 transcription factor was identified as a positive co-expressing gene with HvWAK1, suggesting its possible upstream regulator. Taken together, our study provides a base for the functional characterization of WAK family members in the future.

Development of a Nuclear Magnetic Resonance Method and a Near Infrared Calibration Model for the Rapid Determination of Lipid Content in the Field Pea (Pisum sativum).

Pisum sativum is a leguminous crop suitable for cultivation worldwide. It is used as a forage or dried seed supplement in animal feed and, more recently, as a potential non-traditional oilseed. This study aimed to develop a low-cost, rapid, and non-destructive method for analyzing pea lipids with no chemical modifications that would prove superior to existing destructive solvent extraction methods. Different pea accession seed samples, prepared as either small portions (0.5 mm2) of endosperm or ground pea seed powder for comparison, were subjected to HR-MAS NMR analyses and whole seed samples underwent NIR analyses. The total lipid content ranged between 0.57-3.45% and 1.3-2.6% with NMR and NIR, respectively. Compared to traditional extraction with butanol, hexane-isopropanol, and petroleum ether, correlation coefficients were 0.77 (R2 = 0.60), 0.56 (R2 = 0.47), and 0.78 (R2 = 0.62), respectively. Correlation coefficients for NMR compared to traditional extraction increased to 0.97 (R2 = 0.99) with appropriate correction factors. PLS regression analyses confirmed the application of this technology for rapid lipid content determination, with trends fitting models often close to an R2 of 0.95. A better robust NIR quantification model can be developed by increasing the number of samples with more diversity.

Mapping pre-harvest sprouting resistance loci in AAC Innova × AAC Tenacious spring wheat population.

BACKGROUND: Pre-harvest sprouting (PHS) is a major problem for wheat production due to its direct detrimental effects on wheat yield, end-use quality and seed viability. Annually, PHS is estimated to cause > 1.0 billion USD in losses worldwide. Therefore, identifying PHS resistance quantitative trait loci (QTLs) is crucial to aid molecular breeding efforts to minimize losses. Thus, a doubled haploid mapping population derived from a cross between white-grained PHS susceptible cv AAC Innova and red-grained resistant cv AAC Tenacious was screened for PHS resistance in four environments and utilized for QTL mapping. RESULTS: Twenty-one PHS resistance QTLs, including seven major loci (on chromosomes 1A, 2B, 3A, 3B, 3D, and 7D), each explaining ≥10% phenotypic variation for PHS resistance, were identified. In every environment, at least one major QTL was identified. PHS resistance at most of these loci was contributed by AAC Tenacious except at two loci on chromosomes 3D and 7D where it was contributed by AAC Innova. Thirteen of the total twenty-one identified loci were located to chromosome positions where at least one QTL have been previously identified in other wheat genotype(s). The remaining eight QTLs are new which have been identified for the first time in this study. Pedigree analysis traced several known donors of PHS resistance in AAC Tenacious genealogy. Comparative analyses of the genetic intervals of identified QTLs with that of already identified and cloned PHS resistance gene intervals using IWGSC RefSeq v2.0 identified MFT-A1b (in QTL interval QPhs.lrdc-3A.1) and AGO802A (in QTL interval QPhs.lrdc-3A.2) on chromosome 3A, MFT-3B-1 (in QTL interval QPhs.lrdc-3B.1) on chromosome 3B, and AGO802D, HUB1, TaVp1-D1 (in QTL interval QPhs.lrdc-3D.1) and TaMyb10-D1 (in QTL interval QPhs.lrdc-3D.2) on chromosome 3D. These candidate genes are involved in embryo- and seed coat-imposed dormancy as well as in epigenetic control of dormancy. CONCLUSIONS: Our results revealed the complex PHS resistance genetics of AAC Tenacious and AAC Innova. AAC Tenacious possesses a great reservoir of important PHS resistance QTLs/genes supposed to be derived from different resources. The tracing of pedigrees of AAC Tenacious and other sources complements the validation of QTL analysis results. Finally, comparing our results with previous PHS studies in wheat, we have confirmed the position of several major PHS resistance QTLs and candidate genes.

Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score.

INTRODUCTION: Risk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking. METHODS: Multivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290). RESULTS: 983 patients (median age 70, IQR 53-83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS (SpO2, Obesity, Age, Respiratory rate, Stroke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0-1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%). CONCLUSION: The SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.

RNA Backbone Torsion and Pseudotorsion Angle Prediction Using Dilated Convolutional Neural Networks.

RNA three-dimensional structure prediction has been relied on using a predicted or experimentally determined secondary structure as a restraint to reduce the conformational sampling space. However, the secondary-structure restraints are limited to paired bases, and the conformational space of the ribose-phosphate backbone is still too large to be sampled efficiently. Here, we employed the dilated convolutional neural network to predict backbone torsion and pseudotorsion angles using a single RNA sequence as input. The method called SPOT-RNA-1D was trained on a high-resolution training data set and tested on three independent, nonredundant, and high-resolution test sets. The proposed method yields substantially smaller mean absolute errors than the baseline predictors based on random predictions and based on helix conformations according to actual angle distributions. The mean absolute errors for three test sets range from 14°-44° for different angles, compared to 17°-62° by random prediction and 14°-58° by helix prediction. The method also accurately recovers the overall patterns of single or pairwise angle distributions. In general, torsion angles further away from the bases and associated with unpaired bases and paired bases involved in tertiary interactions are more difficult to predict. Compared to the best models in RNA-puzzles experiments, SPOT-RNA-1D yielded more accurate dihedral angles and, thus, are potentially useful as model quality indicators and restraints for RNA structure prediction as in protein structure prediction.