Number Of Complexes Research Articles

Effect of Framework and Complementarity Determining Region H1 Charge on the human VH-B1a Domain Expression, Folding, Stability, and Solubility

Many difficulties related to using antibodies in diagnostic and therapeutic applications can sometimes be circumvented by using smaller, less complex single domain antibodies based on variable heavy chain (VH) domain constructs such as camelid VHH domains. However, VH domains have their own limitations, including an increased tendency to aggregate. VH domains often contain hydrophobic residues within their complementarity-determining regions (CDRs) that facilitate binding to target antigens but can also mediate VH domain aggregation, which is a concern for therapeutic applications since this can trigger immune responses. In this study, we engineered the human VH-1Ba domain to improve its stability and solubility by introducing charged amino acids in the VH domain framework region and CDRH1. We followed two strategies to improve the stability and solubility of VH domains. First, we introduced positive and negatively charged amino acids in the framework region of an autonomous human VH domain (VH-B1a) and observed the effect of framework net charge on VH domain refolding, stability, and solubility. Introducing positive charge into the VH-B1a framework increased its thermostability but slightly lowered its refolding ability and solubility. We were not able to obtain correctly folded negatively charged (-VH) VH domains. Second, we introduced a series of positive and negatively charged amino acids in the CDRH1 loop of near-neutral (VH-B1a) and positively charged (+VH) VH domains, and observed their effect on expression, refolding, stability, and solubility. For both the VH-B1a and +VH domains, we observed a decrease in melting temperature (Tm) and room temperature solubility as more negative or positive charged amino acids were added to the CDRH1. The VH-B1a domain had higher room temperature solubility for negative and slightly positive CDRH1 net charges. The +VH domain had higher Tms for all CDRH1 net charges and was better able to tolerate the adverse effects of adding positive charge to CDRH1. We observed a similar response in refolding and solubility of VH-B1a and +VH in response to changes in CDRH1 net charge after temperature-induced denaturation for negative and neutral CDRH1s. We observed a positional effect with a single Lys (31K) and double Lys (31, 32KK) substitutions in CDRH1, which promoted VH-B1a aggregation and was partially overcome by the +VH domain. In summary, this study provides information for designing VH domains with improved biophysical properties and a +VH domain that will be useful for applications where positive surface charge and CDRH1 are desirable.

Exploring the sequence diversity and surface expression of Factor H-Binding Protein among invasive serogroup B meningococcal strains from selected European countries

ABSTRACT Factor H-Binding Protein (fHbp) is a key component of meningococcal vaccines such as MenB-fHbp, licensed in the EU, UK, and other countries. Sufficient expression of fHbp on the bacterial surface is necessary for vaccine-induced antibodies to bind and exert bactericidal activity. The flow cytometric MEASURE assay quantifies fHbp expression in vitro, and previous studies have shown that strains with a Mean Fluorescence Intensity (MFI) >1000 are likely to be killed by MenB-fHbp-induced antibodies. This study assessed fHbp peptide distribution and expression among 451 invasive group B strains collected in 2016 across England, Wales, and Northern Ireland (EW&NI), Germany, Italy, and Spain. We found that 92% of the strains expressed fHbp above the MFI 1000 threshold. The strain distribution across EW&NI, Germany, and Italy was similar, with coverage ranging from 92.1% to 94.6%, dominated by a small number of clonal complexes and fHbp peptides. Although, the Spanish subset had a higher proportion of lower-expressing strains, particularly clonal complex 213, resulting in a lower predicted coverage for Spain (84%). These results, along with other published MEASURE data, can provide a basis for genotypic MenB-fHbp coverage predictions, however, inclusion of the upstream intergenic sequence of the fHbp gene in the prediction improved its accuracy by distinguishing between low- and high-expressing strains. Future MEASURE analyses of strains with less common fHbp variants would serve to further refine vaccine coverage predictions.

Switchable-magnetization planar probe MFM sensor for imaging magnetic textures of complex metal oxide perovskite

We introduce an alternative method for switching-magnetization magnetic force microscopy that utilizes planar tip-on-chip probes. Unlike conventional needle-like tips, the planar probe technique incorporates a microdevice near the tip apex on a 1×1mm2 chip, which allows for thin-film engineering and micro/nano-customization aimed at application-specific tip functionalization. In this study, we establish a microscale current pathway near the tip end to manage the tip magnetization state. This planar probe was used to investigate the intricate disordered magnetic domain structure of an epitaxial thin film of the transition metal oxide perovskite LaMnO3, a material previously demonstrated to exhibit complex domains related to superparamagnetism, antiferromagnetism, and ferromagnetism. We successfully visualized an inhomogeneous distribution of magnetic islands near the Curie temperature, with a resolution exceeding 10nm.

Comparing random walks in graph embedding and link prediction.

Random walks find extensive applications across various complex network domains, including embedding generation and link prediction. Despite the widespread utilization of random walks, the precise impact of distinct biases on embedding generation from sequence data and their subsequent effects on link prediction remain elusive. We conduct a comparative analysis of several random walk strategies, including the true self-avoiding random walk and the traditional random walk. We also analyze walks biased towards node degree and those with inverse node degree bias. Diverse adaptations of the node2vec algorithm to induce distinct exploratory behaviors were also investigated. Our empirical findings demonstrate that despite the varied behaviors inherent in these embeddings, only slight performance differences manifest in the context of link prediction. This implies the resilient recovery of network structure, regardless of the specific walk heuristic employed to traverse the network. Consequently, the results suggest that data generated from sequences governed by unknown mechanisms can be successfully reconstructed.

Molybdenum Complex-Catalyzed N-Alkylation of Bulky Primary and Secondary Amines.

Aliphatic allylic amines are present in a large number of complex and pharmaceutically relevant molecules. The direct amination of allylic electrophiles serves as the most common method toward the preparation of these motifs. However, the use of feedstock reaction components (allyl alcohol and aliphatic amine) in these transformations remains a great challenge. Such a challenge primarily stems from the high Lewis basicity and large steric hindrance of aliphatic amines, in addition to the low reactivity of allylic alcohols. Herein, we report a general solution to these challenges. The developed protocol allows an efficient allylic amination of allyl alcohols with sterically bulky aliphatic amines in the presence of an inexpensive earth-abundant molybdenum complex. This simple and economic protocol also enables regioselective branched amination; the practicality of the reaction was shown in an efficient, scaled-up synthesis of several drugs.

Structural insights into brassinosteroid export mediated by the Arabidopsis ABC transporter ABCB1

Brassinosteroids (BRs) are steroidal phytohormones indispensable for plant growth, development, and responses to environmental stresses. The export of bioactive BRs to the apoplast is essential for BR signalling initiation, which requires binding of BR molecule to the extracellular domains of the plasma membrane-localized receptor complex. We have previously shown that the Arabidopsis thaliana ATP-binding cassette (ABC) transporter, ABCB19, functions as a BR exporter, and together with its close homologue, ABCB1, positively regulate BR signalling. Here, we demonstrate that ABCB1 is another BR transporter. The ATP hydrolysis activity of ABCB1 was stimulated by bioactive BRs, and its transport activity was confirmed in proteoliposomes and protoplasts. Structures of ABCB1 in substrate-unbound (apo), brassinolide (BL)-bound, and ATP plus BL-bound states were determined. In the BL-bound structure, BL was bound to the hydrophobic cavity formed by the transmembrane domain, and triggered local conformational changes. Together, our data provide additional insights into the ABC transporter-mediated BR export.

Systematic identification and prediction of sex-specific MADS-box genes in Zanthoxylum armatum during flower development

Zanthoxylum armatum, as a vital economic tree species serving both as a food source and medicinal plant, it stands as one of the key industries supporting rural revitalization in southwestern China, offered significant economic, ecological, and social benefits. In recent years, there has been a significant increase in the occurrence of male flowers being sterile, which has greatly impeded the industrial development of Z. armatum. In this study, we investigated the MADS-box gene as a pivotal transcription factor (TFs) influencing flower development. To gain a comprehensive understanding of the evolutionary dynamics of MADS-box genes in Z. armatum and establish a solid foundation for future research on this significant gene family, we conducted a genome-wide investigation and analyzed the expression patterns of MADS-box genes. The present study identified a total of 72 MADS-box genes (ZaMADS1–72) from Z. armatum and proposed that the ZaMADS41 gene is pivotal candidate gene influencing the female-to-male transformation. Phylogenetic analysis revealed that these genes can be categorized into two types: Type I (29 genes) and Type II (43 genes), with the latter exhibiting more complex protein domains and motifs compared to the former. Protein-protein interactions were observed among members of the ZaMADS-box gene family, while their promoter regions contained cis-acting elements associated with light response, hormone response, and plant growth and development. Expression profiling during different stages of male and female flower development demonstrated distinct high expression patterns for certain genes specifically in mature male or female flowers. Furthermore, the determination of endogenous hormone content indicated a potential correlation between iP9G and MEJA with sex transformation, while iP9G exhibited a significantly negative association with ZaMADS33 expression. This comprehensive study on MADS-box genes in Z. armatum combined with changes in endogenous hormone levels during male and female flower development, provides a solid theoretical foundation for investigating bud differentiation mechanisms and regulatory techniques in this species.

Research on the design of gamified classroom teaching mode based on fuzzy conjoint analysis method

Operations research is an applied mathematics discipline aimed at providing optimal solutions for complex systems and decision-making processes. However, the theoretical aspects of operations research can often be abstract, making it challenging and potentially tedious for students with a weaker foundational background. To address this issue, game-based learning has been introduced into operations research education in recent years, demonstrating promising results. In this context, gamified learning is seen as a potential solution to enhance student motivation and classroom engagement. Although gamification has been explored in education, its application in operations research, especially within a complex mathematical domain, remains under-researched. These games transform abstract operations research theories into concrete scenarios and problems, allowing students to learn and apply operations research knowledge through interactive play. This study aims to investigate students’ perceptions and attitudes toward game-based operations research classrooms using fuzzy set joint analysis. By employing fuzzy conjoint analysis, we aim to provide a nuanced understanding of student preferences and optimize gamified teaching strategies. In the investigation process, 83 students from School D were selected as the research subjects, and a questionnaire encompassing 16 items was administered. These items covered various aspects of integrating operations research board games into the classroom, including students’ engagement, cooperation, understanding, and application of operations research knowledge during the game process. By utilizing a Likert seven-point scale to collect students’ responses to each item, the data were subsequently converted into fuzzy sets to assess students’ views on game-based operations research classrooms. The results were used to provide targeted optimization for gamified teaching. Group experimental results involving the 83 students showed that gamified teaching in operations research has a positive impact on students’ learning of the subject. This finding holds significant implications for educators and teaching practices, offering valuable insights for the design and implementation of future operations research teaching methods.

A Novel Approach to Transient Fourier Analysis for Electrical Engineering Applications

This paper presents a detailed investigation into the application of transient Fourier analysis in select electrical engineering contexts. Two novel approaches for addressing transient analysis are introduced. The first approach combines the Fourier series with the Laplace–Carson (L-C) transform in the complex domain, utilizing complex time vectors to streamline the computation of the original function. The inverse transformation back into the time domain is achieved using the Cauchy-Heaviside (C-H) method. The second approach applies the Fourier transform (F-Τ) for the transient analysis of a power converter circuit with both passive and active loads. The method of complex conjugate amplitudes is employed for steady-state analysis. Both contributions represent innovative approaches within this study. The process begins with Fourier series expansions and the computation of Fourier coefficients, followed by solving the system’s steady-state and transient responses. The transient states are then confirmed using the Fourier transform. To validate these findings, the analytical results are verified through simulations conducted in the Matlab/Simulink R2023b environment.

Towards effective, robust and utility-preserving watermarking of GPS trajectories

Personal GPS trajectory is essential for businesses and emerging data markets due to its relevance in various data-driven methods, including traffic forecasting, accident prediction, and profiling driving behavior. Watermarking is a method that facilitates verification of data ownership and authenticity by embedding provenance information into the data. Whereas watermarking is commonly adopted in the image and audio domains, only a few initial watermarking methods exist for GPS trajectory data. GPS trajectory watermarking is particularly challenging due to the spatio-temporal data properties and easiness of data modification. As a result, existing watermarking methods often embed only minimal provenance information, lack robustness, and can fail to preserve data utility for downstream applications. In this work, we propose W-Trace - a novel, effective, robust, and utility-preserving GPS trajectory watermarking method. W-Trace transforms a GPS trajectory into a complex domain and applies the Fourier transformation to decompose the trajectory into the frequency representation. W-Trace embeds watermarks in the frequency representation and verifies them in a spatiotemporally-aware procedure. We demonstrate the effectiveness, robustness, and utility of the proposed W-Trace approach in realistic settings using real-world GPS trajectory datasets. In contrast to the baselines, the proposed W-Trace approach is robust to a wide range of trajectory modifications while preserving the GPS trajectory characteristics required for the downstream applications.

Genetically engineered bacteria can do complex math

Genetically engineered bacteria can do complex math

Statistical Distribution Theory and Fractional Calculus

This is an overview paper. This paper is an attempt to show that fractional calculus can be reached through statistical distribution theory. This paper brings together results on fractional integrals and fractional derivatives of the first and second kinds in the real and complex domains in the scalar, vector, and matrix-variate cases, and shows that all these results can be reached through statistical distribution theory. It is shown that the whole area of fractional integrals can be reached through distributions of products and ratios in the scalar variable case and distributions of symmetric products and symmetric ratios in the matrix-variate cases. While summarizing the materials, the real domain results are also listed side by side with the complex domain results so that a comparative study is possible. Fractional integrals and derivatives in the real domain mean that the parameters involved could be real or complex with appropriate conditions, the arbitrary function is real-valued, and the variables involved are all real. These in the complex domain mean that the parameters could be real or complex and the arbitrary function is still real-valued but the variables involved are in the complex domain. Fully complex domain means the variables as well as the arbitrary function are in the complex domain. Most of the materials on fractional integrals and fractional derivatives involving a single matrix or a number of matrices in the real or complex domain are of this author. Slight modifications of the results, compared with the published works in various papers, are there in various sections. In the paragraph on notations, the lemmas that are taken from this author’s own book on Jacobians are common with published works and hence the similarity index with this author’s works will be high. Section Matrix-Variate Joint Distributions and Fractional Integrals in Many Matrix-Variate Cases material on a statistical approach to Kiryakova’s multi-index fractional integral and its extension to the real scalar case of second kind integrals as well as extensions of first and second kind integrals to real and complex matrix-variate cases are believed to be new. Matrix differential operators are introduced in Section Fractional Derivatives and, with the help of these operators, fractional derivatives are constructed from the corresponding fractional integrals. These operators are applicable in a large variety of functions. Applicability is shown through identities created from scale transformed gamma random variables. Some concluding remarks are given and some open problems are pointed out in Section Concluding Remarks.

Structural and functional insights from the sequences and complex domain architecture of adhesin-like proteins from Methanobrevibacter smithii and Methanosphaera stadtmanae.

Methanogenic archaea, or methanogens, are crucial in guts and rumens, consuming hydrogen, carbon dioxide, and other fermentation products. While their molecular interactions with other microorganisms are not fully understood, genomic sequences provide information. The first genome sequences of human gut methanogens, Methanosphaera stadtmanae and Methanobrevibacter smithii, revealed genes encoding adhesin-like proteins (ALPs). These proteins were also found in other gut and rumen methanogens, but their characteristics and functions remain largely unknown. This study analyzes the ALP repertoire of M. stadtmanae and M. smithii using AI-guided protein structure predictions of unique ALP domains. Both genomes encode more than 40 ALPs each, comprising over 10% of their genomes. ALPs contain repetitive sequences, many of which are unmatched in protein domain databases. We present unique sequence signatures of conserved ABD repeats in ALPs and propose a classification based on domain architecture. Our study offers insights into ALP features and how methanogens may interact with other microorganisms.

A dynamic mid-crustal magma domain revealed by the 2023 to 2024 Sundhnúksgígar eruptions in Iceland.

Mid-crustal magma domains are the source of many basaltic eruptions. Lavas from individual eruptions are often chemically homogeneous, suggesting that they derive from single, well-mixed magma reservoirs. The 2023 to 2024 eruptions at Sundhnúksgígar in the Svartsengi volcanic system in Iceland provide an opportunity to observe the behavior of a mid-crustal magma domain at high spatial and temporal resolution by detailed sampling and geochemical characterization. We observed substantial mantle-derived geochemical variability in the products erupted in the first hours of the December 2023 and January, February, and March to May 2024 eruptions, indicating that the eruptions derived from multiple magma reservoirs, which mineral-melt equilibration pressures place in the mid crust. The unusual presence of geochemical heterogeneity in the mid-crustal magma domain provides insights into how dynamic and complex mid-crustal magma domains can be.

Spectral Galerkin Approximation and Error Analysis Based on a Mixed Scheme for Fourth‐Order Problems in Complex Regions

ABSTRACTIn this article, an efficient spectral Galerkin method, which is based on a mixed scheme, is proposed and studied for solving fourth‐order problems in complex regions. The fundamental idea behind this approach is to transform the initial problem into an equivalent form in cylindrical coordinates and to reshape the computational domain into a product‐type rectangular one, which facilitates the utilization of spectral methods. However, when considering the equivalent fourth‐order form directly in cylindrical coordinates, it introduces intricate pole conditions and variable coefficients, posing challenges to both theoretical analysis and algorithm implementation. To address this, we employ the orthogonality of Fourier series to further decompose it into a sequence of decoupled two‐dimensional fourth‐order eigenvalue problems. For each such problem, we introduce an auxiliary function to transform it into an equivalent second‐order coupled system. Building on this, we formulate a mixed variational formulation and discrete scheme, and prove the error estimates for eigenvalue and eigenfunction approximations. Furthermore, we extend this algorithm to the two‐dimensional complex domains. Finally, a series of numerical examples are presented, and the numerical results validate the effectiveness of the algorithm and the correctness of the theoretical results.

Functions of a Complex Variable with a Large Parameter and Construction of Regions

The study of the asymptotic behavior of solutions of singularly perturbed equations in complex domains comes down to the study of integrals of exponential functions containing a large parameter. Such integrals differ significantly from the integrals to which the saddle point method is applicable. The saddle point method is not applicable to study such integrals. Thus, the problem arises of constructing domains and choosing integration paths for studying such integrals. In this work, specific examples of integrals show the construction of regions in the complex plane and the choice of integration paths. The chosen integration paths ensure that the integrals are bounded over a large parameter as this parameter tends to infinity. When constructing the domain and choosing integration paths, level lines of some harmonic functions that have zeros and singular points were used. The principle of symmetry is also used. In early works, cases were considered when the eigenvalues ​​of the first approximation matrix of a singularly perturbed equation had only zeros or only poles. Cases where the eigenvalues ​​have zeros and poles were not considered.

Discovery of Novel Thiazole-Based SIRT2 Inhibitors as Anticancer Agents: Molecular Modeling, Chemical Synthesis and Biological Assays.

The search and development of effective sirtuin small molecule inhibitors (SIRTIs) continues to draw great attention due to their wide range of pharmacological applications. Based on SIRTs' involvement in different biological pathways, their ligands were investigated for many diseases, such as cancer, neurodegenerative disorders, diabetes, cardiovascular diseases and autoimmune diseases. The elucidation of a substantial number of SIRT2-ligand complexes is steering the identification of novel and more selective modulators. Among them, SIRT2 in the presence of the SirReal2 analog series was the most studied. On this basis, we recently reported structure-based analyses leading to the discovery of thiazole-based compounds acting as SIRT2 inhibitors (T1, SIRT2 IC50 = 17.3 µM). Herein, ligand-based approaches followed by molecular docking simulations allowed us to evaluate in silico a novel small series of thiazoles (3a-3d and 5a, 5d) as putative SIRT2 inhibitors. Results from the computational studies revealed comparable molecular interaction fields (MIFs) and docking positionings of most of these compounds with respect to reference SIRT2Is. Biochemical and biological assays validated this study and pointed to compound 5a (SIRT2 IC50 = 9.0 µM) as the most interesting SIRT2I that was worthy of further development as an anticancer agent.

Domain knowledge-powered attention for air traffic management hazardous events classification

The automated classification of hazardous events in Air Traffic Management (ATM) involves concise texts with specialized terminology. The problem is currently addressed by machine learning text classification methods; however, some methods overlook domain knowledge, while others require it excessively. This research aims to identify a process for integrating low-cost, text-based domain knowledge into classification to address the issue of domain shift. To achieve this, the Wide and Deep Bidirectional Encoder Representations from Transformers (WD-BERT) model is proposed, featuring a unique knowledge-powered attention mechanism. WD-BERT's Wide Attention module assesses the compatibility between input text and domain knowledge to determine class probabilities, while its Deep Attention module extracts contextual features guided by domain knowledge. Additionally, a text mining method is employed to extract domain knowledge from ATM regulation texts. The model is trained and evaluated using a dataset derived from the China ATM Hazard Source Database. It achieves a multi-label classification accuracy of 80.24% and an F1-micro score of 0.88, outperforming comparative models with state-of-the-art performance. Breakdown analyses of the model indicate that the carefully designed attention mechanism endows WD-BERT with the ability to integrate domain knowledge, allowing it to excel in contexts involving complex sentences, multiple terms, and domain shifts. The innovation of this study lies in the proposed domain knowledge-powered attention mechanism, which allows simply organized domain knowledge to effectively guide ATM hazardous events classification, alleviating the impact of domain shift. This method can also be applied to other vertical domains without knowledge graphs.

Clinical characteristics and genetics functional analysis of two children with Spinal muscular atrophy

To explore the characteristics of SMN1 gene variants and carry out functional verification for two children with Spinal muscular atrophy (SMA). Two male children with complicated SMA diagnosed at the Children's Hospital Affiliated to Capital Institute of Pediatrics respectively in July 2021 and April 2022 due to delayed or retrograde motor development were selected as the study subjects. Clinical data of the children were collected. Primary culture of skin fibroblasts was carried out, and peripheral blood samples were collected from both children and their parents. Multiplex ligation-dependent probe amplification, combined long-range PCR and nested PCR, and Sanger sequencing were carried out to detect the copy number and variants of the SMN1 gene. Absolute quantitative real-time PCR, Western blotting and immunofluorescence were used to determine the transcriptional level of the SMN gene, expression of the SMN protein, and the number of functional SMN protein complexes (gems body), respectively. This study was approved by the Children's Hospital Affiliated to Capital Institute of Pediatrics (Ethics No. SHERLLM2021009). Child 1, a 1-year-old boy, was clinically diagnosed with type 1 SMA. Child 2, a 2-and-a-half-year-old boy, was clinically diagnosed with type 3 SMA. Both children were found to harbor a paternally derived SMN1 deletion and a maternally derived SMN1 gene variant, namely c.824G>T (p.Gly275Val) and c.884A>T (p.*295Leu). Compared with the normal controls and carriers, the levels of full-length SMN1 transcripts in their peripheral blood and skin fibroblast cell lines were significantly decreased (P < 0.05), and the levels of SMN protein normalized to that of β-actin, and the numbers of gems bodies in the primary fibroblast cells were also significantly lower (P < 0.05). Based on the guidelines from the American College of Medical Genetics and Genomics, both variants were classified as likely pathogenic (PS3+PM3+PM5+PP3; PS3+PM3+PM4+PP3). Following the diagnosis, both children had received nusinersen treatment. Although their motor function was improved, child 1 still died at the age of 2 due to severe pulmonary infection. The walking ability of child 2 was significantly improved, and his prognosis appeared to be good. Two cases of clinically complicated SMA have been confirmed by genetic testing and experimental studies, which has provided a reference for their accurate treatment.

A scalable convolutional neural network approach to fluid flow prediction in complex environments

We evaluate the capability of convolutional neural networks (CNNs) to predict a velocity field as it relates to fluid flow around various arrangements of obstacles within a two-dimensional, rectangular channel. We base our network architecture on a gated residual U-Net template and train it on velocity fields generated from computational fluid dynamics (CFD) simulations. We then assess the extent to which our model can accurately and efficiently predict steady flows in terms of velocity fields associated with inlet speeds and obstacle configurations not included in our training set. Real-world applications often require fluid-flow predictions in larger and more complex domains that contain more obstacles than used in model training. To address this problem, we propose a method that decomposes a domain into subdomains for which our model can individually and accurately predict the fluid flow, after which we apply smoothness and continuity constraints to reconstruct velocity fields across the whole of the original domain. This piecewise, semicontinuous approach is computationally more efficient than the alternative, which involves generation of CFD datasets required to retrain the model on larger and more spatially complex domains. We introduce a local orientational vector field entropy (LOVE) metric, which quantifies a decorrelation scale for velocity fields in geometric domains with one or more obstacles, and use it to devise a strategy for decomposing complex domains into weakly interacting subsets suitable for application of our modeling approach. We end with an assessment of error propagation across modeled domains of increasing size.