Sequence Of Residues Research Articles

Advancing Accuracy in Sea Level Estimation with GNSS-R: A Fusion of LSTM-DNN-Based Deep Learning and SNR Residual Sequences

The global navigation satellite system reflectometry (GNSS-R) technique has shown promise in retrieving sea levels using signal-to-noise ratio (SNR) data. However, its accuracy and performance are often limited compared to conventional tide gauges, particularly due to constraints in satellite elevation angles. To address these limitations, we propose a methodology integrating Long Short-Term Memory Deep Neural Networks (LSTM-DNN) models, utilising SNR residual sequences as key feature inputs. Our study focuses on the SC02 station, examining elevation angles ranging from 5° to 10°, 5° to 15°, and 5° to 20°. Results reveal notable reductions in root mean square errors (RMSE) of 2.855%, 17.519%, and 15.756%, respectively, showcasing improvements in accuracy across varying elevation angles. Of particular significance is the enhancement in precision observed at higher elevation angles. This underscores the valuable contribution of our approach to nearshore sea level wave height retrieval, promising advancements in the GNSS-R technique.

PROBING FIBRINOGEN STRUCTURE: INSIGHTS FROM LIMITED PROTEOLYSIS, PEPTIDES, AND MONOCLONAL ANTIBODIES

Aim. The aim of this study was to analyze the prospects of using antibodyes, peptides and proteases in investigating fibrinogen structure and function. Methods. Polyacrylamide gel electrophoresis, western blotting, turbidimetry, electron microscopy. Results. Since antibodies are specific to a small sequence of amino acid residues, monoclonal antibodies can be used to investigate the importance of a specific site on fibrinogen. For example, using the 1-5A antibodies, which are specific to the C-terminal region of the Aα-chain of fibrinogen, the importance of these regions in the lateral association of protofibrils was established. The study indicates that the presence of various antibodies, each specific to a relatively small region of the fibrinogen molecule, can cause different effects on the functioning of the molecule. It has been demonstrated that synthetic peptide Аα195-205 inhibits the stage of fibrin protofibril formation, while peptide γ69-77 inhibits the lateral association of protofibrils. Another approach in the study of proteins is limited proteolysis. For example, using proteases from the culture medium of B. thuringiensis and the venom of G. halys we separated the functional role of different parts of fibrin(ogen) αС-region in its functioning. Conclusions. Thus, it is important to acknowledge that the described approaches in protein research each have their own advantages and disadvantages, and scientists may choose them based on specific research objectives. Funding. The work was carried out within the framework of scientific research works projects of young scientists of the National Academy of Sciences of Ukraine: «The influence of agonists and antagonists of integrin receptors on the functional effects of platelets» (2023–2024, State registration number 0123U103023).

Numerical representations of AB-type copolymer complexes: analysis of 1H NMR chemical shift patterns in terms of a Smith–Cantor set

When considering the possibility of storing information in the sequence of monomer residues within an AB-type copolymer chain, it is constructive to model that sequence as a string of ones and zeros. The intramolecular environment around any given digit (say a “1”) can then be represented by another string of integers—a code—obtained by summing pairs of digits at equivalent positions, in both directions, from that digit. The code can include only integers 0, 1 and 2, and can represent a number in any base b higher than 2. In base b = 3 the resulting set of codes includes all numbers (because only digits 0, 1 and 2 occur in ternary expansions), but in any base b > 3 the codes define a limited set of numbers comprising a fractal we term a Smith–Cantor set. The 1H NMR spectrum of a random, AB-type co(polyester-imide) shows, on complexation with pyrene, a pattern of complexation shifts approximating very closely to the Smith–Cantor set for which b = 4. Other co(polyimide) complexes show a 1H NMR pattern corresponding to a specific sub-set of this fractal. The sub-set arises from a “stop-at-zero” limitation, whereby digits in the initial string are set to zero for code-generating purposes if they occur beyond a zero, as viewed from the central “1”. The limitation arises in copolymers where pyrene binds by intercalation between pairs of adjacent diimide residues. This numerical approach provides a complete, unifying theory to account for the emergence of fractal character in the 1H NMR spectra of AB-type copolymer complexes.

Topically applied GHK as an anti-wrinkle peptide: Advantages, problems and prospective

Introduction: Peptides are promising and attractive anti-wrinkle active ingredients, amongst which glycyl-histidyl-lysine peptide (GHK) is one of the most broadly promoted peptide for topical application. This simple sequence of amino acid residues not only has the capability of tissue regeneration and the enhancement of collagen and glycosaminoglycans synthesis but also is able to increase nerve outgrowth and angiogenesis. Consequently, GHK has several properties, from wound healing to prevention/reduction wrinkles. GHK-Cu and Pal-GHK are metal complex and palmitoylated derivatives of GHK, respectively. Although GHK-Cu and Pal-GHK are widely used in anti-wrinkle products available on the cosmetic market, the published information on their skin permeability, effectiveness, physicochemical properties and so on is insufficient. Methods: This review aims to highlight whether GHK is sufficiently effective on wrinkle prevention/reduction. Apart from the effectiveness, another question that is tried to be answered is whether skin permeability of GHK allows it to act as an anti-wrinkle peptide at its site of action? Skin permeation enhancement methods employed so far are also reviewed. Results: Based on cellular studies, undoubtedly, GHK can be considered as an anti-wrinkle ingredient. Although GHK-Cu and Pal-GHK have been of interest as effective peptides to be incorporated in the anti-wrinkle products, there is a surprising absence of clinical studies using them. Metal complexation and chemical modification with a hydrophobic moiety increase permeability of this peptide. Besides, cell penetrating peptides seem promising to increase skin permeation of GHK and its derivatives. Skin pretreatment with microneedles also has the potential to be further studied for permeation enhancement of such peptides. As peptide ingredients, their formulation may encounter some challenges, mainly due to their hydrophilic (high aqueous solubility and low partition coefficient) and unstable nature. Conclusion: Although GHK-Cu and Pal-GHK are effective and relatively skin permeable, their permeability could be successfully increased using permeation enhancement methodologies.

Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction

Accurate medium- and long-term runoff prediction models play crucial guiding roles in regional water resources planning and management. However, due to the significant variation in and limited amount of annual runoff sequence samples, it is difficult for the conventional machine learning models to capture its features, resulting in inadequate prediction accuracy. In response to the difficulties in leveraging the advantages of machine learning models and limited prediction accuracy in annual runoff forecasting, firstly, the variational mode decomposition (VMD) method is adopted to decompose the annual runoff series into multiple intrinsic mode function (IMF) components and residual sequences, and the spectral clustering (SC) algorithm is applied to classify and reconstruct each IMF. Secondly, an annual runoff prediction model based on the adaptive particle swarm optimization–long short-term memory network (APSO-LSTM) model is constructed. Finally, with the basis of the APSO-LSTM model, the decomposed and clustered IMFs are predicted separately, and the predicted results are integrated to obtain the ultimate annual runoff forecast results. By decomposing and clustering the annual runoff series, the non-stationarity and complexity of the series have been reduced effectively, and the endpoint effect of modal decomposition has been effectively suppressed. Ultimately, the expected improvement in the prediction accuracy of the annual runoff series based on machine learning models is achieved. Four hydrological stations along the upper reaches of the Fen River in Shanxi Province, China, are studied utilizing the method proposed in this paper, and the results are compared with those obtained from other methods. The results show that the method proposed in this article is significantly superior to other methods. Compared with the APSO-LSTM model and the APSO-LSTM model based on processed annual runoff sequences by single VMD or Wavelet Packet Decomposition (WPD), the method proposed in this paper reduces the RMSE by 40.95–80.28%, 25.26–57.04%, and 15.49–40.14%, and the MAE by 24.46–80.53%, 16.50–59.30%, and 16.58–41.80%, in annual runoff prediction, respectively. The research has important reference significance for annual runoff prediction and hydrological prediction in areas with data scarcity.

Adaptive expansion of ERVK solo-LTRs is associated with Passeriformes speciation events

Endogenous retroviruses (ERVs) are ancient retroviral remnants integrated in host genomes, and commonly deleted through unequal homologous recombination, leaving solitary long terminal repeats (solo-LTRs). This study, analysing the genomes of 362 bird species and their reptilian and mammalian outgroups, reveals an unusually higher level of solo-LTRs formation in birds, indicating evolutionary forces might have purged ERVs during evolution. Strikingly in the order Passeriformes, and especially the parvorder Passerida, endogenous retrovirus K (ERVK) solo-LTRs showed bursts of formation and recurrent accumulations coinciding with speciation events over past 22 million years. Moreover, our results indicate that the ongoing expansion of ERVK solo-LTRs in these bird species, marked by high transcriptional activity of ERVK retroviral genes in reproductive organs, caused variation of solo-LTRs between individual zebra finches. We experimentally demonstrated that cis-regulatory activity of recently evolved ERVK solo-LTRs may significantly increase the expression level of ITGA2 in the brain of zebra finches compared to chickens. These findings suggest that ERVK solo-LTRs expansion may introduce novel genomic sequences acting as cis-regulatory elements and contribute to adaptive evolution. Overall, our results underscore that the residual sequences of ancient retroviruses could influence the adaptive diversification of species by regulating host gene expression.

Electric field induced the changes in structure and function of human transforming growth factor beta receptor type I: from molecular dynamics to docking

The transforming growth factor beta (TGF-β) signaling pathway is believed to play essential roles in several physiological activities, including cancer. TGF-β receptor type I (TBR-I) is a key membrane receptor protein in the TGF-β signaling pathway, which relates to many intracellular biological effects. In recent years, cold atmospheric plasma (CAP) has been found to have promising prospects in selective anticancer therapy and has confirmed its essential role in the TGF-β signaling pathway. However, the ambiguous effect of CAP-induced electric field (EF) on TBR-I still limits the application of CAP in clinical therapy. Molecular dynamics is applied to assess the effect of EF on the structure of the extracellular domain of TBR-I using a series of indicators and methods, and then we discuss the ligand binding ability of TBR-I. Results show that moderate EF intensities’ structural restraints may contribute to the structural stability and ligand-binding ability of TBR-I, but an EF higher than 0.1 V/nm will be harmful. What’s more, EF induces a change in the docking interface of TBR-I, showing the conformation and position of special sequences of residues decide the ligand binding surface. The relevant results suggest that CAP-induced EF plays a crucial role in receptor-receptor interaction and provides significant guidelines for EF-related anticancer therapy. Communicated by Ramaswamy H. Sarma

Phase transformation sequence of pre-oxidized roast-leach ferrovanadium residue

The depletion of the primary metal sources has prompted the exploration of alternative avenues for metal recovery. In the case of titanium and iron, the ferrovanadium residue produced through roast-leach processing of titanomagnetite presents a viable option for accessing these metals. Titanomagnetite resources, which contain valuable elements, such as iron, vanadium, and titanium, possess significant valuable potential. Titanomagnetite deposits are normally treated via smelting for vanadium or vanadium and iron recovery; titanium is not commercially recoverable. Titanomagnetites have recently been processed through the roast-leach method for vanadium primary production, and iron and titanium are typically part of the waste stream in this process. This study proposes a novel approach to determine the characteristic mineralogy and to study the phase transformation sequence of the roasted-leached ferrovanadium residue during the pre-oxidation process. Leaching was also done to evaluate the extraction potential of Fe, V and Ti on the pre-oxidized residue in comparison to the raw residue The roasted-leached ferrovanadium residue was sampled using the cone and quartering method and then, dried in an oven at temperatures of between 30 and 40 °C, for an hour after which, the remaining moisture content was determined. The bond milling method was employed to reduce the sample size, while the particle size distribution (PSD) was verified by using the standard laboratory Tyler series. Thereafter, the roasted-leached ferrovanadium residue was characterized with XRD, SEM, ICP-OES, and XRF. The samples were pre-oxidized at temperatures ranging from 300 °C to 1000 °C with an aim of improving the grades of iron, vanadium, and titanium-bearing minerals prior leaching. The results revealed the moisture content to be ∼5.07%. The bond work index of typical slags was estimated to be 10.2 kwh/t, with a determined d80 value of 200 μm. According to the XRF analysis, the predominant compounds present are hematite, Fe2O3 (75.55%), titanium dioxide, TiO₂ (12.79%), silicon dioxide, SiO2 (3.03%), and alumina, Aℓ2O3 (2.62%), along with minor compounds. XRD patterns exhibited the presence of FeTiO3 and VO2 in the as-received samples, while pre-oxidation induced the evolution of new phases such as hematite, rutile, anatase, and pseudobrookite.

All-atom RNA structure determination from cryo-EM maps.

Many methods exist for determining protein structures from cryogenic electron microscopy maps, but this remains challenging for RNA structures. Here we developed EMRNA, a method for accurate, automated determination of full-length all-atom RNA structures from cryogenic electron microscopy maps. EMRNA integrates deep learning-based detection of nucleotides, three-dimensional backbone tracing and scoring with consideration of sequence and secondary structure information, and full-atom construction of the RNA structure. We validated EMRNA on 140 diverse RNA maps ranging from 37 to 423 nt at 2.0-6.0 Å resolutions, and compared EMRNA with auto-DRRAFTER, phenix.map_to_model and CryoREAD on a set of 71 cases. EMRNA achieves a median accuracy of 2.36 Å root mean square deviation and 0.86 TM-score for full-length RNA structures, compared with 6.66 Å and 0.58 for auto-DRRAFTER. EMRNA also obtains a high residue coverage and sequence match of 93.30% and 95.30% in the built models, compared with 58.20% and 42.20% for phenix.map_to_model and 56.45% and 52.3% for CryoREAD. EMRNA is fast and can build an RNA structure of 100 nt within 3 min.

Biosynthesis of UDP-α-N-Acetyl-d-mannosaminuronic Acid and CMP-β-N-Acetyl-d-neuraminic Acid for the Capsular Polysaccharides of Campylobacter jejuni.

Campylobacter jejuni is a human pathogen and a leading cause of food poisoning in North America and Europe. The exterior surface of the bacterial cell wall is attached to a polymeric coat of sugar molecules known as the capsular polysaccharide (CPS) that helps protect the organism from the host immune response. The CPS is composed of a repeating sequence of common and unusual sugar residues. In the HS:11 serotype of C. jejuni, we identified two enzymes in the gene cluster for CPS formation that are utilized for the biosynthesis of UDP-α-N-acetyl-d-mannosaminuronic acid (UDP-ManNAcA). In the first step, UDP-α-N-acetyl-d-glucosamine (UDP-GlcNAc) is epimerized at C2 to form UDP-α-N-acetyl-d-mannosamine (UDP-ManNAc). This product is then oxidized by a NAD+-dependent C6-dehydrogenase to form UDP-ManNAcA. In the HS:6 serotype (C. jejuni strain 81116), we identified three enzymes that are required for the biosynthesis of CMP-β-N-acetyl-d-neuraminic acid (CMP-Neu5Ac). In the first step, UDP-GlcNAc is epimerized at C2 and subsequently hydrolyzed to form N-acetyl-d-mannosamine (ManNAc) with the release of UDP. This product is then condensed with PEP by N-acetyl-d-neuraminate synthase to form N-acetyl-d-neuraminic acid (Neu5Ac). In the final step, CMP-N-acetyl-d-neuraminic acid synthase utilizes CTP to convert this product into CMP-Neu5Ac. A bioinformatic analysis of these five enzymes from C. jejuni serotypes HS:11 and HS:6 identified other bacterial species that can produce UDP-ManNAcA or CMP-Neu5Ac for CPS formation.

Improved resolution of 3-mercaptopropionate dioxygenase active site provided by ENDOR spectroscopy offers insight into catalytic mechanism

3-mercaptopropionate (3MPA) dioxygenase (MDO) is a mononuclear nonheme iron enzyme that catalyzes the O2-dependent oxidation of thiol-bearing substrates to yield the corresponding sulfinic acid. MDO is a member of the cysteine dioxygenase family of small molecule thiol dioxygenases and thus shares a conserved sequence of active site residues (Serine-155, Histidine-157, and Tyrosine-159), collectively referred to as the SHY-motif. It has been demonstrated that these amino acids directly interact with the mononuclear Fe-site, influencing steady-state catalysis, catalytic efficiency, O2-binding, and substrate coordination. However, the underlying mechanism by which this is accomplished is poorly understood. Here, pulsed electron paramagnetic resonance spectroscopy [1H Mims electron nuclear double resonance spectroscopy] is applied to validate density functional theory computational models for the MDO Fe-site simultaneously coordinated by substrate and nitric oxide (NO), (3MPA/NO)-MDO. The enhanced resolution provided by electron nuclear double resonance spectroscopy allows for direct observation of Fe-bound substrate conformations and H-bond donation from Tyr159 to the Fe-bound NO ligand. Further inclusion of SHY-motif residues within the validated model reveals a distinct channel restricting movement of the Fe-bound NO-ligand. It has been argued that the iron-nitrosyl emulates the structure of potential Fe(III)-superoxide intermediates within the MDO catalytic cycle. While the merit of this assumption remains unconfirmed, the model reported here offers a framework to evaluate oxygen binding at the substrate-bound Fe-site and possible reaction mechanisms. It also underscores the significance of hydrogen bonding interactions within the enzymatic active site.

Short Peptide Amyloids Are a Potential Sequence Pool for the Emergence of Proteins

Under prebiotic conditions, peptides are capable of self-replication through a structure-based template-assisted mechanism when they form amyloids. Furthermore, peptide amyloids can spontaneously form inside fatty acid vesicles creating membrane enclosed complex structures of variable morphologies. This is possible because fatty acid vesicle membranes act as filters allowing passage of activated amino acids while some amino acids derived from the activated species become non-permeable and trapped in the vesicles. Similarly, nascent peptides derived from the condensation of the activated amino acids are also trapped in the vesicles. It is hypothesized that such preselected peptide amyloids become a sequence pool for the emergence of proteins in life and that after billions of years of cellular evolution, the sequences in the current proteome have diverged significantly from these original seed peptides. If this hypothesis is correct, it could be possible to detect the traces of these seed sequences in current proteomes.Here, we show for all possible 3, 6, 7, 8 or 9 residue sequence motifs that those motifs that are most amyloidogenic/aggregation prone are over-represented in extant proteomes compared to a sequence-randomized proteome. Furthermore, we find that there is a greater proportion of amyloidogenic sequence motifs in archaea proteomes than in the larger primate proteomes. This suggests that the evolution towards larger proteomes leads to smaller proportion of amyloidogenic sequences.

A hybrid crude oil price forecasting framework: Modified ensemble empirical mode decomposition and hidden Markov regression

AbstractThe considerable influence of crude oil prices on the international economy has motivated numerous scholars to develop various prediction models. Two difficulties are encountered in forecasting. One is that the time series of crude oil prices show massive jumps in high frequency. The other is that the time series of crude oil prices are characterised by nonlinearity, structural breaks and highly volatile states. Therefore, we propose a hybrid model that incorporates the principle of decomposition–integration. Its overall process can be divided into the steps of decomposition and integration. In decomposition step, modified ensemble empirical mode decomposition is used to decompose the data on crude oil prices. Next, K‐means and principal component analysis are utilised to extract the optimal intrinsic mode function (IMF) from diverse frequencies amongst decomposed IMFs. Following decomposition, different forecasting methods are used to match IMFs with various frequencies for prediction. In accordance with structural breaks and the highly volatile states of high‐frequency series, hidden Markov regression is applied to describe predicted values in a probabilistic manner, providing objective interpretation and forecasting. A neural network is taken to predict intermediate‐frequency IMFs by using a fully connected network. Owing to the low frequency and tendency of the residual sequence to be more stable than other frequency sequences, the autoregressive integrated moving average model is applied as the forecasting method for prediction. Finally, all individual predictions for the eventual results are ensembled as final predictions. Results confirm that the proposed model is better than several normal hybrid and single models in terms of prediction accuracy and Diebold–Mariano test results. All results confirm that the newly proposed hybrid model is a promising tool for the analysis and forecasting of crude oil prices.

Improved Reproducibility of Deflection Control Process for Cantilever‐Type MEMS Tactile Sensors

This paper describes an improved process for controlling the initial deflection structure of cantilever‐type MEMS tactile sensors. By using residual stress, deposition sequence, and metal layer patterns, the initial deflection of the cantilever was successfully aligned to the same initial deflection with micron accuracy and 97% yield in a simpler and easier process than previous processes. This enables a reduction in the number of processes and flexible control of deflection, which is expected to improve yield and shorten fabrication time. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

ENCAP: Computational prediction of tumor T cell antigens with ensemble classifiers and diverse sequence features.

Cancer immunotherapy enhances the body's natural immune system to combat cancer, offering the advantage of lowered side effects compared to traditional treatments because of its high selectivity and efficacy. Utilizing computational methods to identify tumor T cell antigens (TTCAs) is valuable in unraveling the biological mechanisms and enhancing the effectiveness of immunotherapy. In this study, we present ENCAP, a predictor for TTCA based on ensemble classifiers and diverse sequence features. Sequences were encoded as a feature vector of 4349 entries based on 57 different feature types, followed by feature engineering and hyperparameter optimization for machine learning models, respectively. The selected feature subsets of ENCAP are primarily composed of physicochemical properties, with several features specifically related to hydrophobicity and amphiphilicity. Two publicly available datasets were used for performance evaluation. ENCAP yields an AUC (Area Under the ROC Curve) of 0.768 and an MCC (Matthew's Correlation Coefficient) of 0.522 on the first independent test set. On the second test set, it achieves an AUC of 0.960 and an MCC of 0.789. Performance evaluations show that ENCAP generates 4.8% and 13.5% improvements in MCC over the state-of-the-art methods on two popular TTCA datasets, respectively. For the third test dataset of 71 experimentally validated TTCAs from the literature, ENCAP yields prediction accuracy of 0.873, achieving improvements ranging from 12% to 25.7% compared to three state-of-the-art methods. In general, the prediction accuracy is higher for sequences of fewer hydrophobic residues, and more hydrophilic and charged residues. The source code of ENCAP is freely available at https://github.com/YnnJ456/ENCAP.

РАЗРАБОТКА С ИСПОЛЬЗОВАНИЕМ МОЛЕКУЛЯРНОЙ ПЕПТИДНОЙ ТРАНСПЛАНТАЦИИ ПОТЕНЦИАЛЬНОГО НЕЙРОПРОТЕКТОРНОГО БИОПЕПТИДА

The present research is focused on design of new nutritional neuroprotective biopeptide, using method of molecular peptide transplantation (MPT) based on analysis of literature sources, databases on biophysical properties and biological activity of peptides. The objects of the research are sequences of amino acid residues of peptides with proven neuroprotective properties. Prediction of biological activity of the developed biopeptide was carried out using the Peptide Ranker database (http://distilldeep.ucd.ie/PeptideRanker). Information on cyclic peptides frameworks that are resistant to proteolysis was obtained in the Cybase database (http://www.cybase.org.au), biopeptide identification was carried out using the EROP-Moscow database (http://erop.inbi.ras.ru/index.html), biophysical properties assessment of the developed biopeptide was carried out using the PepCalc peptide properties calculator (http://pepcalc.com) and APD 3 databases (https://aps.unmc.edu/home). A neuro-protective interfering peptide has been developed by preliminary preparation of a cyclotide-based framework followed by insertion of short HFRWPGP peptide into the framework by the MPT method. The developed peptide has the following sequence of amino acid residues: PCRRRCHFRWPGPCRGRCP, has high biological activity equal to 0.89. The hydrophobicity of the peptide according to Wimley-White in the whole residue is 2.91, therefore, the peptide is well soluble in water. The protein binding potential (Boman index) is high and is 4.3 kcal / mol, indicating its ability to bind to target receptors.

Statistical complexity and stochastic resonance of an underdamped bistable periodic potential system excited by Lévy noise

This paper focuses on the underdamped bistable potential system's stochastic resonance (SR) phenomenon under Lévy noise excitation. The statistical complexity of the system is analyzed under the changes of damping factor, amplitude and frequency of the periodic signal, stability index and skew parameter of Lévy noise, and the effects of the changes of above parameters on SR are investigated. The results show that the increase of the damping factor hinders the appearance of SR phenomenon; the larger amplitude and lower frequency of the periodic signal are beneficial to the appearance of SR; the larger stability index of the noise parameter, the more likely SR occurs, and the change of the skew parameter has little effect on the SR. Because the Bandt-Pompe (B-P) algorithm is used in the information-theoretic measure to model the statistical complexity of this system, the robustness of the method is analyzed by changing the embedding dimension and the length of residual time interval sequence in the B-P algorithm. Finally, the mean first passage time of the particle is calculated as a function of the noise intensity and the noise-enhanced stability effect is found.

Bioinformatics-based Analyses and B/Th Cell Epitope Prediction of Mugwort Pollen Allergen Art v 1 Protein.

To analyze the sequence characteristics of mugwort pollen allergen Art v 1 protein and predict its B cell and Th (helper T cell) cell epitopes, the gene sequence and amino acid sequence of Art v 1 protein were obtained by referring to Genebank. ExPASy's Prot Param, TMHMM, DNAstar Protean, Swiss-Model, UCLA-DOE LAB SAVES v6.0, and IEDB were used to analyze and predict physicochemical properties, transmembrane region, secondary structure, tertiary structure, and B cell and Th cell epitopes of the protein. The Art v 1 protein is composed of 132 amino acid residues, the relative molecular weight is 13404.26, the molecular formula is C584H903N157O181S12, pI value is 7.49, the lipid solubility index is 41.59, and the hydrophilic index is -0.454, which is considered as hydrophilic protein. The instability index (ii) is 78.11, which is classified as an unstable protein. The N-terminus of the protein has an α-helical transmembrane region, which is located in the 5-27 amino acid residue sequence, and the 1-24 position is the signal peptide sequence. There are random coil, β-turn, α-helix, and β-sheet, and it also contains hydrophilic region, flexible region, and surface accessibility region structures. The prediction results of tertiary structure are consistent with the analysis results of secondary structure. Five dominant B cell epitopes were predicted, which were Art v 1 71-87, Art v 1 33-49, Art v 1 104-120, Art v 1 95-111, and Art v 1 86-102. There were five Th cell dominant epitopes, which were Art v 1 2-16, Art v 1 3-17, Art v 1 4-18, Art v 1 5-19, and Art v 1 6-20. The Art v 1 protein is predicted to have good antigenicity due to the presence of B cell and Th cell epitopes.

An Improved VMD–EEMD–LSTM Time Series Hybrid Prediction Model for Sea Surface Height Derived from Satellite Altimetry Data

Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of sea level change predictions, this study introduced an improved variational mode decomposition and ensemble empirical mode decomposition–long short-term memory hybrid model (VMD–EEMD–LSTM). This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the intrinsic mode functions (IMFs) with various frequencies, along with a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results, obtained through a comparative analysis of six sets of Dutch coastal sea surface height data, confirm the excellent accuracy of the hybrid model proposed (root mean square error (RMSE) = 47.2 mm, mean absolute error (MAE) = 33.3 mm, coefficient of determination (R2) = 0.9). Compared to the VMD-LSTM model, the average decrease in RMSE was 58.7%, the average reduction in MAE was 60.0%, and the average increase in R2 was 49.9%. In comparison to the EEMD-LSTM model, the average decrease in RMSE was 27.0%, the average decrease in MAE was 28.0%, and the average increase in R2 was 6.5%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance. The model proposed in this study demonstrates a notable enhancement in global mean sea lever (GMSL) forecasting accuracy during testing along the Dutch coast.

A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM

BackgroundHand, foot, and mouth disease (HFMD) is a common infectious disease that poses a serious threat to children all over the world. However, the current prediction models for HFMD still require improvement in accuracy. In this study, we proposed a hybrid model based on autoregressive integrated moving average (ARIMA), ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) to predict the trend of HFMD.MethodsThe data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were adopted to evaluate the performances of the prediction models.ResultsOverall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R2 of 4.37, 2.94 and 0.996, respectively. Performing EEMD on the residual sequence yields 11 intrinsic mode functions. EEMD-LSTM model is the second best, with RMSE, MAPE and R2 of 6.20, 3.98 and 0.996.ConclusionResults showed the advantage of ARIMA-EEMD-LSTM model over the ARIMA model, the LSTM model, the ARIMA-LSTM model and the EEMD-LSTM model. For the prevention and control of epidemics, the proposed hybrid model may provide a more powerful help. Compared with other three models, the two integrated with EEMD method showed significant improvement in predictive capability, offering novel insights for modeling of disease time series.