Traditional Sequence Research Articles

Assessing serial recall as a measure of artificial grammar learning

IntroductionImplicit statistical learning is, by definition, learning that occurs without conscious awareness. However, measures that putatively assess implicit statistical learning often require explicit reflection, for example, deciding if a sequence is ‘grammatical’ or ‘ungrammatical’. By contrast, ‘processing-based’ tasks can measure learning without requiring conscious reflection, by measuring processes that are facilitated by implicit statistical learning. For example, when multiple stimuli consistently co-occur, it is efficient to ‘chunk’ them into a single cognitive unit, thus reducing working memory demands. Previous research has shown that when sequences of phonemes can be chunked into ‘words’, participants are better able to recall these sequences than random ones. Here, in two experiments, we investigated whether serial visual recall could be used to effectively measure the learning of a more complex artificial grammar that is designed to emulate the between-word relationships found in language.MethodsWe adapted the design of a previous Artificial Grammar Learning (AGL) study to use a visual serial recall task, as well as more traditional reflection-based grammaticality judgement and sequence completion tasks. After exposure to “grammatical” sequences of visual symbols generated by the artificial grammar, the participants were presented with novel testing sequences. After a brief pause, participants were asked to recall the sequence by clicking on the visual symbols on the screen in order.ResultsIn both experiments, we found no evidence of artificial grammar learning in the Visual Serial Recall task. However, we did replicate previously reported learning effects in the reflection-based measures.DiscussionIn light of the success of serial recall tasks in previous experiments, we discuss several methodological factors that influence the extent to which implicit statistical learning can be measured using these tasks.

CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences

BackgroundTraditional supervised learning methods applied to DNA sequence taxonomic classification rely on the labor-intensive and time-consuming step of labelling the primary DNA sequences. Additionally, standard DNA classification/clustering methods involve time-intensive multiple sequence alignments, which impacts their applicability to large genomic datasets or distantly related organisms. These limitations indicate a need for robust, efficient, and scalable unsupervised DNA sequence clustering methods that do not depend on sequence labels or alignment.ResultsThis study proposes CGRclust, a novel combination of unsupervised twin contrastive clustering of Chaos Game Representations (CGR) of DNA sequences, with convolutional neural networks (CNNs). To the best of our knowledge, CGRclust is the first method to use unsupervised learning for image classification (herein applied to two-dimensional CGR images) for clustering datasets of DNA sequences. CGRclust overcomes the limitations of traditional sequence classification methods by leveraging unsupervised twin contrastive learning to detect distinctive sequence patterns, without requiring DNA sequence alignment or biological/taxonomic labels. CGRclust accurately clustered twenty-five diverse datasets, with sequence lengths ranging from 664 bp to 100 kbp, including mitochondrial genomes of fish, fungi, and protists, as well as viral whole genome assemblies and synthetic DNA sequences. Compared with three recent clustering methods for DNA sequences (DeLUCS, iDeLUCS, and MeShClust v3.0.), CGRclust is the only method that surpasses 81.70% accuracy across all four taxonomic levels tested for mitochondrial DNA genomes of fish. Moreover, CGRclust also consistently demonstrates superior performance across all the viral genomic datasets. The high clustering accuracy of CGRclust on these twenty-five datasets, which vary significantly in terms of sequence length, number of genomes, number of clusters, and level of taxonomy, demonstrates its robustness, scalability, and versatility.ConclusionCGRclust is a novel, scalable, alignment-free DNA sequence clustering method that uses CGR images of DNA sequences and CNNs for twin contrastive clustering of unlabelled primary DNA sequences, achieving superior or comparable accuracy and performance over current approaches. CGRclust demonstrated enhanced reliability, by consistently achieving over 80% accuracy in more than 90% of the datasets analyzed. In particular, CGRclust performed especially well in clustering viral DNA datasets, where it consistently outperformed all competing methods.

Impact of leguminous crop residues on rice performance in diverse cropping systems in the alluvial soils of eastern Uttar Pradesh

In eastern Uttar Pradesh, the traditional rice-wheat cropping sequence has been identified as a factor contributing to unsustainable agricultural practices, resulting in low productivity and returns for small and marginal farmers. Crop diversification presents a viable solution to enhance productivity and ensure food and nutritional security for these farmers. A field experiment conducted under the All India Coordinated Research Project (AICRP) on Integrated Farming Systems (IFS) at Acharya Narendra Deva University of Agriculture and Technology, Ayodhya, U.P., India, from 2019-20 to 2021-22, evaluated the growth, yield attributes, and production potential of rice under ten rice-based cropping systems under irrigated medium-land conditions. The systems assessed included rice-wheat-fallow, rice-wheat-greengram, rice-frenchbean-greengram, rice-gram-cowpea, rice-mustard-greengram, rice-linseed-blackgram, rice-berseem-sudanchari, rice-oat-maize + cowpea, rice-cauliflower-okra, and rice-potato-cowpea (vegetable). The legume-based systems significantly enhanced plant height, which was attributed to the higher nitrogen availability from the decomposition of legume residues. Legume-based systems showed an increase in the number of leaves per hill and LAI, reflecting improved vegetative growth. Effective tiller production was higher in these systems, which contributed to superior panicle length, grain count, and grain weight. Among these systems, rice-frenchbean-greengram and rice-gram-greengram consistently outperformed the conventional system in terms of yield attributes and the yield of grain and straw. The integration of legume crops into rice-based sequences can improve soil fertility, promote better vegetative growth, and enhance yield, thereby contributing to more sustainable and productive rice cultivation.

Improved differential protection for two-terminal weak feed AC system considering negative sequence control coordination strategy

The flexible DC transmission project of renewable energy has become an inevitable development trend for large-scale renewable energy grid connection. Its two-terminal weak feed AC system is often composed of 100 % power electronic power equipment. The fault control strategy is used at both ends of the line to change the fault current characteristics, leading to a decrease in the performance of traditional differential protection. Based on analyzing the adaptability of current differential protection in the two-terminal weak feed AC system under traditional control strategy (TCS), this paper proposed an improved differential protection for two-terminal weak feed AC system considering negative sequence control coordination strategy. Firstly, the fault characteristics of the two terminal weak feed AC systems are analyzed. On this basis, the adaptability of differential protection under TCS is analyzed, and the quantization function that can quantify the operation performance of differential protection is given. Secondly, based on the problem of the traditional negative sequence control strategy, an improved control strategy is introduced and the operation performance of differential protection under the improved control strategy is studied. Finally, an improved differential protection scheme based on the change of current amplitude is proposed. Simulation results show that the proposed protection scheme can resist higher fault impedance(300 Ω) and noise interference(25 dB). In addition, the proposed differential protection scheme is suitable for the protection requirements of the two terminal weak feed AC systems in different scenarios and has higher sensitivity.

The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction

Multiple sequence alignment (MSA) has evolved into a fundamental tool in the biological sciences, playing a pivotal role in predicting molecular structures and functions. With broad applications in protein and nucleic acid modeling, MSAs continue to underpin advancements across a range of disciplines. MSAs are not only foundational for traditional sequence comparison techniques but also increasingly important in the context of artificial intelligence (AI)-driven advancements. Recent breakthroughs in AI, particularly in protein and nucleic acid structure prediction, rely heavily on the accuracy and efficiency of MSAs to enhance remote homology detection and guide spatial restraints. This review traces the historical evolution of MSA, highlighting its significance in molecular structure and function prediction. We cover the methodologies used for protein monomers, protein complexes, and RNA, while also exploring emerging AI-based alternatives, such as protein language models, as complementary or replacement approaches to traditional MSAs in application tasks. By discussing the strengths, limitations, and applications of these methods, this review aims to provide researchers with valuable insights into MSA’s evolving role, equipping them to make informed decisions in structural prediction research.

Abstract 4122650: 1-year comparison of quadruple therapy sequencing strategies for heart failure with reduced ejection fraction using an individual-based state-transition microsimulation model

Introduction: Guidelines recommend quadruple therapy (angiotensin receptor blocker-neprilysin inhibitor [ARNI]), beta-blocker, mineralocorticoid receptor antagonist (MRA), and sodium-glucose co-transporter 2 inhibitor [SGLT2I]) as the cornerstone of heart failure (HF) with reduced ejection fraction (HFrEF) management. Yet, guidelines do not propose a specific order of initiation and titration (or “sequencing strategy”) of these agents, due to the absence of trial evidence. Aims: To model the 1-year efficacy and harms of proposed HFrEF quadruple therapy sequencing strategies using a microsimulation model. Methods: We conducted an individual-based state-transition microsimulation modeling study to compare the 1-year cumulative incidence of death, total HF hospitalization, and adverse events with 6 different HFrEF medication sequencing strategies (emulating 2 different traditional strategies, 2 two-drug combinations, a "cluster" strategy, and four-drug "simultaneous" strategy), each with two versions (weekly or biweekly medication adjustments), applied to treatment-naïve outpatients with HFrEF. We modeled death as an incidence at 1 year, and total HF hospitalization (first and recurrent events) and adverse events (bradycardia, hyperkalemia, hypotension, and renal impairment) as incidence rates per 100 patient-years. Results: At 1 year, an estimated 15.5% died without treatment compared to 6.9% with the traditional sequence adjusted biweekly, and 5.2-6.3% with other sequencing strategies. Similarly, the HF hospitalization rate decreased from 32.8 per 100 patient-years with no treatment to 11.1 per 100 patient-years traditional sequencing adjusted biweekly, and 6.9-9 per 100 patient-years with other strategies. The incidence rates of medication-related adverse events per 100 patient-years were: hypotension (5.8-6.9), renal impairment (4.6-6.0), bradycardia (2.9-3.2), and hyperkalemia (approximately 0.5). Conclusions: For treatment-naïve outpatients with HFrEF, pharmacotherapy sequencing strategies that started 2 to 4 medications on the first visit reduced the risk of death and hospitalization at 1 year compared to a traditional sequencing strategy. Weekly medication adjustments did not outperform biweekly adjustments when >=2 medications were started on the initial visit. These findings can inform clinicians and policymakers in developing HFrEF medication optimization programs with sequencing strategy protocols that fit the local practical context.

Role of Contrast Perfusion Weighted Magnetic Resonance Imaging in Grading of Brain Tumors with Histopathological Correlation

Background: Traditional MRI sequences provide precise anatomical information about brain tumors. But these sequences can’t quantitatively evaluate vascular physiology, or capture tumor biology at the molecular level; which is important for tumor grading, therapeutic assessment, and prognostication. Furthermore, non-enhancing parts of the tumor affected brain, which generally indicates peri-tumoral brain edema with infiltrative tumor cells, is not visible on conventional MRI sequences. Perfusion weighted Magnetic Resonance Imaging (PW-MRI) techniques such as Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and Dynamic Susceptibility Contrast Magnetic Resonance Imaging (DSC-MRI) have shown promise as imaging biomarkers for glioma therapy, since these new sequences can also provide information about vascular hemodynamics. Our study was first prospective study from Himalayan belt of India, wherein we tried to assess the role of PW-MRI in the grading of brain tumors, with histopathological correlation. Aim and Objective: To compare tumor perfusion and histopathological tumor grading, in evaluation of brain tumor especially gliomas. Material and Method: 40 patients who were referred to Department of Radiodiagnosis for evaluation of brain tumor, and who gave informed consent, were included in this prospective study, done over a period of 18 months. All patients underwent MRI, followed by surgical resection. Observation and Result: In our study, diagnostic accuracy was 90% for mean rCBV (lesion) (cut off: 7 by ROC), mean rCBF (lesion) (cut off: 13.9 by ROC), and rCBF ratio (cut off: 2.9 by ROC); for differentiating benign versus malignant brain tumor. rCBF ratio was found to be best diagnostic parameter with sensitivity of 96%, specificity of 80%, positive predictive value of 89%, negative predictive value of 92%, diagnostic accuracy of 90%, area under ROC curve=0.909, and p value<0.001. Conclusion: We come to conclusion that high grade brain tumor display higher CBF, than do low grade brain tumor. Three parameters showed good diagnostic accuracy, which includes rCBF ratio, mean rCBF (lesion), and mean rCBV (lesion). These parameters can be used to predict the grade of the brain tumor, with good diagnostic accuracy.

Development of a Flow Through-Based Limited Digestion Approach for High-Throughput and High-Sequence Coverage Mapping of Therapeutic mRNAs.

Messenger RNAs (mRNAs) have rapidly emerged as a pivotal class of biotherapeutics with great promise in the prevention and treatment of various diseases. As with other biotherapeutics, the sequence accuracy and integrity of mRNAs are critical quality attributes (CQAs), influencing the translation efficiency and protein expression fidelity of mRNAs. Due to the generation of short and repetitive oligonucleotides, traditional sequence mapping methods, which utilize in-solution RNase T1 digestion followed by LC-MS/MS analysis, face challenges in achieving high sequence coverage. In this study, we developed a novel flow through (FT)-based strategy to achieve the limited RNase T1 digestion of therapeutic mRNAs, leading to improved mRNA sequence mapping by LC-MS/MS analysis. Compared with the traditional in-solution digestion methods, the FT-based digestion method could consistently generate an increased number of unique oligonucleotides with miscleavages, which significantly boosted the overall sequence coverage (over 93%) of therapeutic mRNAs of varying sizes. Moreover, the automated digestion workflow using the AssayMAP platform offers significant advantages in method reproducibility and throughput. The high throughput and high sequence coverage features of this method could facilitate its wide application in the development of mRNA-based therapeutics.

Sekwencja szybkiej intubacji. Aktualny stan wiedzy

Rapid sequence intubation is a widely practiced method for handling the airway during anaesthesia induction in patients with gastric problems or those susceptible to regurgitation or aspiration. Preparation involves gathering essential equipment and medications for emergency intubation, including oxygen, suction, bag-valve mask, laryngoscope, endotracheal tubes with stylets, resuscitation gear, and rescue tools. Despite its consistent use for over five decades, debates persist regarding the efficacy of specific elements of the method, such as cricoid pressure and the assessment of fasting in urgent surgical cases. The absence of standardised rapid sequence intubation techniques and universally accepted guidelines has resulted in discrepancies in published data. Cricoid pressure, also known as the Sellick manoeuvre, is a controversial element of rapid sequence intubation that has both supporters and critics. While it has been used for decades and is recommended by many countries to prevent pulmonary aspiration during anaesthesia, recent research has raised questions about its effectiveness. In patients undergoing emergency surgery, ultrasound of the gastric antrum may help evaluate the size of gastric contents in order to minimise the risk of aspiration pneumonia. Techniques and variants of anaesthetic induction in the rapid sequence intubation algorithm should be adapted to a wide range of clinical groups – particularly in children, obese patients and pregnant women. In paediatric anaesthesia and for obese patients, the traditional rapid sequence intubation with apnoea is not commonly used due to their limited respiratory reserve. Despite its numerous side effects, succinylcholine remains the most frequently employed drug for rapid sequence intubation.

RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features.

The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental measurement methods are time-consuming and labor-intensive, it is urgently needed to develop reliable theoretical methods to predict RNA flexibility. In this work, we develop an effective machine learning method, RNAfcg, to predict RNA flexibility, where the Random Forest (RF) is trained by features including the topological centralities, flexibility-rigidity index, and global characteristics first introduced by us, as well as some traditional sequence and structural features. The analyses show that the three types of features introduced first have significant contributions to RNA flexibility prediction, among which the topological type contributes the most, which indicates the importance of structural topology in determining RNA flexibility. The performance comparison indicates that RNAfcg outperforms the state-of-the-art machine learning methods and the commonly used Gaussian Network Model (GNM) models, achieving a much higher Pearson correlation coefficient (PCC) of 0.6619 on the test data set. This work is helpful for understanding RNA dynamics and can be used to predict RNA function information. The source code is available at https://github.com/ChunhuaLab/RNAfcg/.

Imaging of Ulcerative Colitis: The Role of Diffusion-Weighted Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) has emerged as a promising and appealing alternative to endoscopy in the objective assessment of patients with inflammatory bowel disease (IBD). Diffusion-weighted imaging (DWI) is a specialized imaging technique that enables the mapping of water molecule diffusion within biological tissues, eliminating the need for intravenous gadolinium contrast injection. It is expanding the capability of traditional MRI sequences in Ulcerative Colitis (UC). Recently, there has been growing interest in the application of intravoxel incoherent motion (IVIM) imaging in the field of IBD. This technique combines diffusion and perfusion information, making it a valuable tool for assessing IBD treatment response. Previous studies have extensively studied the use of DWI techniques for evaluating the severity of activity in IBD. However, the majority of these studies have primarily focused on Crohn's disease (CD), with only a limited number of reports specifically examining UC. Therefore, this review briefly introduces the basics of DWI and IVIM imaging and conducts a review of relevant studies that have investigated its application in UC to show whether these techniques are useful techniques for evaluating patients with UC in terms of detection, characterization, and quantification of disease activity. Through the extensive literature survey, most of these studies indicate that DWI proves valuable in the differential diagnosis of UC and could be used as an effective modality for staging UC.

Anomaly detection for multivariate time series in IoT using discrete wavelet decomposition and dual graph attention networks

Effective anomaly detection in multivariate time series data is critical to ensuring the security of Internet of Things (IoT) devices and systems. However, building a high precision and low false positive rate anomaly detection model for the complex and volatile IoT environment is a challenging task. This is often due to issues such as a lack of anomaly labeling, high data volatility, and the complexity of device mechanisms. Traditional machine learning algorithms and sequence models frequently fail to account for feature correlation and temporal dependency in anomaly detection. Although deep learning-based anomaly detection methods have progressed, there is still room for improvement in precision, recall, and generalization ability. In this paper, we propose an anomaly detection model called Meta-MWDG to address these issues. The model is based on a multi-scale discrete wavelet decomposition and a dual graph attention network, which can effectively extract feature correlation and temporal dependency in multivariate time series data. Additionally, model-agnostic meta-learning (MAML) is introduced to improve the model’s generalization performance, enabling it to perform well on new tasks even with a few samples. A gated recurrent unit (GRU) is combined with a multi-head self-attention network to output both prediction and reconstruction results in a joint optimization strategy, improving the precision of anomaly detection. Extensive experimental studies demonstrate that Meta-MWDG outperforms the state-of-the-art methods in anomaly detection.

Construction of core-triple shell upconversion nanostructures NaNdF4:Yb,Gd@NaGdF4:Yb@NaGdF4:Yb,Er@NaGdF4 with high FRET efficiency for the biochemical sensor

Upconversion nanoparticles (UCNPs) are excellent energy donors for Föster resonance energy transfer (FRET) systems. However, the lower FRET efficiency to some extend limits its application. To improve the efficiency of FRET, NaNdF4:Yb,Gd@NaGdF4:Yb@NaGdF4:Yb,Er@NaGdF4 core-triple shell (CTS) UCNPs with sensitizers in the core region and activators doped in the outer shell region were successfully prepared by changing the traditional core-shell structure design sequence. By introducing a certain amount of Gd3+ into the core nanoparticles, the crystal growth process of core nanoparticles was regulated, solving the problem of excessive and uneven size caused by high concentration of sensitizers. The insertion of Yb3+ doped transition layer weakens the negative effect between Nd3+ and activator ions, and enhances the luminescence intensity. In addition, comparing CTS UCNPs with UCNPs prepared in the traditional core-shell structure design sequence proves that CTS UCNPs have higher FRET efficiency. A biochemical sensing system was designed using this novel core-shell structure UCNPs, achieving simultaneous detection of GSH and Cd2+ with detection limits of 4.2 nM and 15.2 nM, respectively. The new core-shell structure UCNPs have broader application prospects in the field of biochemical sensing design.

Whole-genome sequencing of Western Canadian Borrelia spp. collected from diverse tick and animal hosts reveals short-lived local genotypes interspersed with longer-lived continental genotypes.

Changing climates are allowing the geographic expansion of ticks and their animal hosts, increasing the risk of Borrelia-caused zoonoses in Canada. However, little is known about the genomic diversity of Borrelia from the west of the Canadian Rockies and from the tick vectors Ixodes pacificus, Ixodes auritulus and Ixodes angustus. Here, we report the whole-genome shotgun sequences of 51 Borrelia isolates from multiple tick species collected on a range of animal hosts between 1993 and 2016, located primarily in coastal British Columbia. The bacterial isolates represented three different species from the Lyme disease-causing Borrelia burgdorferi sensu lato genospecies complex [Borrelia burgdorferi sensu stricto (n=47), Borrelia americana (n=3) and Borrelia bissettiae (n=1)]. The traditional eight-gene multi-locus sequence typing (MLST) strategy was applied to facilitate comparisons across studies. This identified 13 known Borrelia sequence types (STs), established 6 new STs, and assigned 5 novel types to the nearest sequence types. B. burgdorferi s. s. isolates were further differentiated into ten ospC types, plus one novel ospC with less than 92 % nucleotide identity to all previously defined ospC types. The MLST types resampled over extended time periods belonged to previously described STs that are distributed across North America. The most geographically widespread ST, ST.12, was isolated from all three tick species. Conversely, new B. burgdorferi s. s. STs from Vancouver Island and the Vancouver region were only detected for short periods, revealing a surprising transience in space, time and host tick species, possibly due to displacement by longer-lived genotypes that expanded across North America.This article contains data hosted by Microreact.

MotifQuest: An Automated Pipeline for Motif Database Creation to Improve Peptidomics Database Searching Programs.

Endogenous peptides are an abundant and versatile class of biomolecules with vital roles pertinent to the functionality of the nervous, endocrine, and immune systems and others. Mass spectrometry stands as a premier technique for identifying endogenous peptides, yet the field still faces challenges due to the lack of optimized computational resources for reliable raw mass spectra analysis and interpretation. Current database searching programs can exhibit discrepancies due to the unique properties of endogenous peptides, which typically require specialized search considerations. Herein, we present a high throughput, novel scoring algorithm for the extraction and ranking of conserved amino acid sequence motifs within any endogenous peptide database. Motifs are conserved patterns across organisms, representing sequence moieties crucial for biological functions, including maintenance of homeostasis. MotifQuest, our novel motif database generation algorithm, is designed to work in partnership with EndoGenius, a program optimized for database searching of endogenous peptides and that is powered by a motif database to capitalize on biological context to produce identifications. MotifQuest aims to quickly develop motif databases without any prior knowledge, a laborious task not possible with traditional sequence alignment resources. In this work we illustrate the utility of MotifQuest to expand EndoGenius' identification utility to other endogenous peptides by showcasing its ability to identify antimicrobial peptides. Additionally, we discuss the potential utility of MotifQuest to parse out motifs from a FASTA database file that can be further validated as new peptide drug candidates.

Chaotic Orthogonal Composite Sequence for 5G NR Time Service Signal Capture Algorithm

Establishing a national comprehensive PNT (Positioning, Navigation, and Timing) system has become a consensus among major countries worldwide. As a crucial component in completing the entire PNT system, the 5G NR (new radio) time service signal plays a vital role. This paper proposes a 5G NR time service signal that uses a spread spectrum system, shares the 5G signal frequency band, but does not occupy the bandwidth of the 5G communication signal. This timing service signal has relatively low power, making it appear “submerged” within the power of the 5G communication signal. The spread spectrum code for this timing signal employs the chaotic orthogonal composite sequence proposed in this paper. Compared to traditional spread spectrum sequences, this sequence offers better security than m-sequences, improved autocorrelation than Walsh sequences, and an effective suppression of the short-period characteristics exhibited when the Skew Tent-Map chaotic sequence takes special values. This paper simulates the capture of the 5G NR time service signal in an environment with a signal-to-noise ratio of 10 dB using an FFT-based parallel code phase search algorithm, successfully capturing the 5G NR time service signal and verifying the feasibility of the proposed chaotic orthogonal composite sequence as a spread spectrum code.

Diversity and Isolation of Endophytic Fungi in Panax japonicus and Biotransformation Activity on Saponins.

This study reports the diversity and community structure differences of the endophytic fungi of Panax japonicus of different ages to obtain novel endophytic fungi with glycoside hydrolytic activity for rare saponins production. This study used the high-throughput sequencing method to analyze the diversity and community structure of endophytic fungi of P. japonicus. The endophytic fungi were processed by traditional isolation, culture, conservation, and ITS rDNA sequence analyses. Then the total saponins of P. japonicus were used as the substrate to evaluate the glycoside hydrolytic activity. The composition analysis of the community structure showed that the abundance, evenness, and diversity of endophytic fungi of nine-year-old P. japonicus were the best among all samples. A total of 210 endophytic fungi were isolated from P. japonicus samples and further annotated by sequencing the internal transcribed spacer. Then the biotransformation activity of obtained strains was further examined on total saponins of P. japonicus (TSPJ), with a strain identified as Fusarium equiseti (No.30) from 7-year-old P. japonicus showing significant glycoside hydrolytic activity on TSPJ, including ginsenoside Ro→zinglbroside R1, pseudoginsenoside RT1→pseudoginsenoside RP1, chikusetsusaponin IV→tarasaponin VI and chikusetsusaponin IVa →calenduloside E. These results reveal the diversity and community structure differences of the endophytic fungi of P. japonicus with different ages and establish a resource library of endophytic fungi of P. japonicus. More importantly, we identified a valuable endophytic fungus with glycoside hydrolytic activity and provided a promising convenient microbial transformation approach to produce minor deglycosylated ginsenosides.

MRCat: A Novel CatBoost Predictor for the Binary Classification of mRNA Subcellular Localization by Fusing Large Language Model Representation and Sequence Features.

The subcellular localization of messenger RNAs (mRNAs) is a pivotal aspect of biomolecules, tightly linked to gene regulation and protein synthesis, and offers innovative insights into disease diagnosis and drug development in the field of biomedicine. Several computational methods have been proposed to predict the subcellular localization of mRNAs within cells. However, there remains a deficiency in the accuracy of these predictions. In this study, we propose an mRCat predictor based on the gradient boosting tree algorithm specifically to predict whether mRNAs are localized in the nucleus or in the cytoplasm. This predictor firstly uses large language models to thoroughly explore hidden information within sequences and then integrates traditional sequence features to collectively characterize mRNA gene sequences. Finally, it employs CatBoost as the base classifier for predicting the subcellular localization of mRNAs. The experimental validation on an independent test set demonstrates that mRCat obtained accuracy of 0.761, F1 score of 0.710, MCC of 0.511, and AUROC of 0.751. The results indicate that our method has higher accuracy and robustness compared to other state-of-the-art methods. It is anticipated to offer deep insights for biomolecular research.

Steady‐state and dynamic control of hydrocracking tail oil distillation process for high‐valued products

AbstractThe maximum utilization of hydrocracking tail oil becomes increasingly important for petrochemical industry. The aim of this work is to develop optimized distillation processes to achieve various high‐valued qualified oil products from hydrocracking tail oil. Six different oil products is produced and the steady‐state distillation process, which aims to fractionate six qualified narrow distillates is established. The algorithm method incorporating divided‐wall column (DWC) configuration was introduced into the steady‐state design. Compared with traditional separation sequences, the DWC configuration leads to an energy‐saving potential up to 11.17%. Furthermore, effective dynamic control strategies were proposed, demonstrating precise and efficient control performance. In the presence of a 15% feed disturbance, the dynamic control structure is capable of maintaining the product distillation range near the set value. This comprehensive study provides a thorough investigation into the efficient utilization of hydrocracking tail oil, establishing a robust theoretical foundation for its industrial application.

Sodium triple quantum MR signal extraction using a single-pulse sequence with single quantum time efficiency.

Sodium triple quantum (TQ) signal has been shown to be a valuable biomarker for cell viability. Despite its clinical potential, application of Sodium TQ signal is hindered by complex pulse sequences with long scan times. This study proposes a method to approximate the TQ signal using a single excitation pulse without phase cycling. The proposed method is based on a single excitation pulse and a comparison of the free induction decay (FID) with the integral of the FID combined with a shifting reconstruction window. The TQ signal is calculated from this FID only. As a proof of concept, the method was also combined with a multi-echo UTE imaging sequence on a 9.4 T preclinical MRI scanner for the possibility of fast TQ MRI. The extracted Sodium TQ signals of single-pulse and spin echo FIDs were in close agreement with theory and TQ measurement by traditional three-pulse sequence (TQ time proportional phase increment [TQTPPI)]. For 2%, 4%, and 6% agar samples, the absolute deviations of the maximum TQ signals between SE and theoretical (time proportional phase increment TQTPPI) TQ signals were less than 1.2% (2.4%), and relative deviations were less than 4.6% (6.8%). The impact of multi-compartment systems and noise on the accuracy of the TQ signal was small for simulated data. The systematic error was <3.4% for a single quantum (SQ) SNR of 5 and at maximum <2.5% for a multi-compartment system. The method also showed the potential of fast in vivo SQ and TQ imaging. Simultaneous SQ and TQ MRI using only a single-pulse sequence and SQ time efficiency has been demonstrated. This may leverage the full potential of the Sodium TQ signal in clinical applications.