Search Results (44)

Harmonic emissions cause power quality issues in electrical systems, making source identification necessary to determine the best way of remedying them. Existing methods rely on measuring variations in harmonic amplitude, angle, and phase which require multiple measurement points or extensive system knowledge. We identified a methodological research gap because Discrete Harmonic Cycle Time (DHCT) has not been evaluated as a measuring principle for harmonic source identification. We propose a method using DHCT to enable single point measurements to profile harmonic sources. To determine the cycle length, we used a combination of sifting, a filter bank of cascaded high-pass and notch filters, and zero-crossing detection. For comparing the devices, we extracted motifs from the time series of discrete cycle lengths and applied principal component analysis. While it has previously been known in other contexts that harmonics can have varying cycle lengths, our laboratory results show that this is also true for the emissions of power electronic devices and that the differentiation can be used to identify the devices, thus bridging this knowledge gap. This method is independent of system impedance and topology. While further validation in more complex environments is needed, our results suggest that devices can be identified using this measurement principle. Since the measuring principle is orthogonal to other methods, it has potential as a complementary tool in harmonic source identification. Full article

Plant protease inhibitors are a ubiquitous feature of plant species and exert a substantial influence on plant stress responses. However, the KTI (Kunitz trypsin inhibitor) family responding to abiotic stress has not been fully characterized in Populus yunnanensis. In this study, we conducted a genome-wide study of the KTI family and analyzed their gene structure, gene duplication, conserved motifs, cis-acting elements, and response to stress treatment. A total of 29 KTIs were identified in the P. yunnanensis genome. Based on phylogenetic analysis, the PyKTIs were divided into four groups (1,2, 3, and 4). Promoter sequence analysis showed that the PyKTIs contain many cis-acting elements related to light, plant growth, hormone, and stress responses, indicating that PyKTIs are widely involved in various biological regulatory processes. RNA sequencing and real-time quantitative polymerase chain reaction analysis showed that KTI genes were differentially expressed under the inverted cutting stress of P. yunnanensis. Transcriptome analysis of P. yunnanensis leaves revealed that PyKTI16, PyKTI18, and PyKTI19 were highly upregulated after inverted cutting. Through the GEO query of Populus transcriptome data, KTI genes played a positive defense role in MeJa, drought, time series, and pathogen stress. This study provided comprehensive information for the KTI family in P. yunnanensis, which should be helpful for the functional characterization of P. yunnanensis KTI genes in the future. Full article

The detection and deterrence of collusive agreements among firms, such as price-fixing cartels, remain pivotal in maintaining market competition. This study investigates the application of machine learning methodologies in the behavioral screening process for detecting collusion, with a specific focus on parallel pricing behaviors in the wholesale fuel market. By employing unsupervised learning techniques, this research aims to identify patterns indicative of collusion—referred to as collusion markers—within time series data. This paper outlines a comprehensive screening research plan based on the CRISP-DM model, detailing phases from business understanding to monitoring. It emphasizes the significance of machine learning methods, including distance measures, motifs, discords, and semantic segmentation, in uncovering these patterns. A case study of the Polish wholesale fuel market illustrates the practical application of these techniques, demonstrating how anomalies and regime changes in price behavior can signal potential collusion. The findings suggest that unsupervised machine learning methods offer a robust alternative to traditional statistical and econometric tools, particularly due to their ability to process large and complex datasets without predefined models. This research concludes that these methods can significantly enhance the detection of collusive behaviors, providing valuable insights for antitrust authorities. Full article

In this paper, we describe the supervised dynamic correlated topic model (sDCTM) for classifying categorical time series. This model extends the correlated topic model used for analyzing textual documents to a supervised framework that features dynamic modeling of latent topics. sDCTM treats each time series as a document and each categorical value in the time series as a word in the document. We assume that the observed time series is generated by an underlying latent stochastic process. We develop a state-space framework to model the dynamic evolution of the latent process, i.e., the hidden thematic structure of the time series. Our model provides a Bayesian supervised learning (classification) framework using a variational Kalman filter EM algorithm. The E-step and M-step, respectively, approximate the posterior distribution of the latent variables and estimate the model parameters. The fitted model is then used for the classification of new time series and for information retrieval that is useful for practitioners. We assess our method using simulated data. As an illustration to real data, we apply our method to promoter sequence identification data to classify E. coli DNA sub-sequences by uncovering hidden patterns or motifs that can serve as markers for promoter presence. Full article

To develop peptide drugs targeting integrin receptors, synthetic peptide ligands endowed with well-defined selective binding motifs are necessary. The snake venom KTS-containing disintegrins, which selectively block collagen α1β1 integrin, were used as lead compounds for the synthesis and structure–activity relationship of a series of linear peptides containing the KTS-pharmacophore and alternating natural amino acids and 3-aminobenzoic acid (MABA). To ensure a better stiffness and metabolic stability, one, two and three MABA residues, were introduced around the KTS pharmacophore motif. Molecular dynamics simulations determined that the solution conformation of MABA peptide 4 is more compact, underwent larger conformational changes until convergence, and spent most of the time in a single cluster. The peptides’ binding affinity has been characterized by an enzyme linked immunosorbent assay in which the most potent peptide 4 inhibited with IC₅₀ of 324 ± 8 µM and 550 ± 45 µM the binding of GST-α1-A domain to collagen IV fragment CB3, and the cell adhesion to collagen IV using α1-overexpressor cells, respectively. Docking studies and MM-GBSA calculations confirmed that peptide 4 binds a smaller region of the integrin near the collagen-binding site and penetrated deeper into the binding site near Trp1. Peptide 4 inhibited tube formation by endothelial cell migration in the Matrigel angiogenesis in vitro assay. Peptide 4 was acutely tolerated by mice, showed stability in human serum, decreased tumor volume and angiogenesis, and significantly increased the survival of mice injected with B16 melanoma cells. These findings propose that MABA-peptide 4 can further serve as an α1β1-integrin antagonist lead compound for further drug optimization in angiogenesis and cancer therapy. Full article

The transdisciplinary nature of science as a whole became evident as the necessity for the complex nature of phenomena to explain social and life science, along with the physical sciences, blossomed into complexity theory and most recently into complexitysynchronization. This science motif is based on the scaling arising from the 1/f-variability in complex dynamic networks and the need for a network of networks to exchange information internally during intra-network dynamics and externally during inter-network dynamics. The measure of complexity adopted herein is the multifractal dimension of the crucial event time series generated by an organ network, and the difference in the multifractal dimensions of two organ networks quantifies the relative complexity between interacting complex networks. Information flows from dynamic networks at a higher level of complexity to those at lower levels of complexity, as summarized in the ‘complexity matching effect’, and the flow is maximally efficient when the complexities are equal. Herein, we use the scaling of empirical datasets from the brain, cardiovascular and respiratory networks to support the hypothesis that complexity synchronization occurs between scaling indices or equivalently with the matching of the time dependencies of the networks’ multifractal dimensions. Full article

Background: Severe coronavirus disease 2019 (COVID-19) disease courses are characterized by immuno-inflammatory, thrombotic, and parenchymal alterations. Prediction of individual COVID-19 disease courses to guide targeted prevention remains challenging. We hypothesized that a distinct serologic signature precedes surges of IL-6/D-dimers in severely affected COVID-19 patients. Methods: We performed longitudinal plasma profiling, including proteome, metabolome, and routine biochemistry, on seven seropositive, well-phenotyped patients with severe COVID-19 referred to the Intensive Care Unit at the German Heart Center. Patient characteristics were: 65 ± 8 years, 29% female, median CRP 285 ± 127 mg/dL, IL-6 367 ± 231 ng/L, D-dimers 7 ± 10 mg/L, and NT-proBNP 2616 ± 3465 ng/L. Results: Based on time-series analyses of patient sera, a prediction model employing feature selection and dimensionality reduction through least absolute shrinkage and selection operator (LASSO) revealed a number of candidate proteins preceding hyperinflammatory immune response (denoted ΔIL-6) and COVID-19 coagulopathy (denoted ΔD-dimers) by 24–48 h. These candidates are involved in biological pathways such as oxidative stress/inflammation (e.g., IL-1alpha, IL-13, MMP9, C-C motif chemokine 23), coagulation/thrombosis/immunoadhesion (e.g., P- and E-selectin), tissue repair (e.g., hepatocyte growth factor), and growth factor response/regulatory pathways (e.g., tyrosine-protein kinase receptor UFO and low-density lipoprotein receptor (LDLR)). The latter are host- or co-receptors that promote SARS-CoV-2 entry into cells in the absence of ACE2. Conclusions: Our novel prediction model identified biological and regulatory candidate networks preceding hyperinflammation and coagulopathy, with the most promising group being the proteins that explain changes in D-dimers. These biomarkers need validation. If causal, our work may help predict disease courses and guide personalized treatment for COVID-19. Full article

Benzimidazoles are classified as a category of heterocyclic compounds. Molecules having benzimidazole motifs show promising utility in organic and scientific studies. A series of mono-substituted benzimidazoles were synthesized by ZnO-NPs via cyclocondensation between substituted aromatic aldehydes and o-phenylene diamine. The synthesized compounds were characterized and compared with the traditional methods. The nano-catalyzed method displayed a higher yield, shorter time and recyclable catalyst. The DFT study and antioxidant activity were investigated for benzo[d]imidazole derivatives. Compound 2a exhibited the highest antioxidant activity among the tested compounds. We focused on the catalytic activity of ZnO in the synthesis of heterocyclic structures with the goal of stimulating further progress in this field. The superiorities of this procedure are high yield of product, low amounts of catalyst and short reaction time. Full article

NF-YB, a subfamily of Nuclear Factor Y (NF-Y) transcription factor, play crucial role in many biological processes of plant growth and development and abiotic stress responses, and they can therefore be good candidate factors for breeding stress-resistant plants. However, the NF-YB proteins have not yet been explored in Larix kaempferi, a tree species with high economic and ecological values in northeast China and other regions, limiting the breeding of anti-stress L. kaempferi. In order to explore the roles of NF-YB transcription factors in L. kaempferi, we identified 20 LkNF-YB family genes from L. kaempferi full-length transcriptome data and carried out preliminary characterization of them through series of analyses on their phylogenetic relationships, conserved motif structure, subcellular localization prediction, GO annotation, promoter cis-acting elements as well as expression profiles under treatment of phytohormones (ABA, SA, MeJA) and abiotic stresses (salt and drought). The LkNF-YB genes were classified into three clades through phylogenetic analysis and belong to non-LEC1 type NF-YB transcription factors. They have 10 conserved motifs; all genes contain a common motif, and their promoters have various phytohormones and abiotic stress related cis-acting elements. Quantitative real time reverse transcription PCR (RT-qPCR) analysis showed that the sensitivity of the LkNF-YB genes to drought and salt stresses was higher in leaves than roots. The sensitivity of LKNF-YB genes to ABA, MeJA, SA stresses was much lower than that to abiotic stress. Among the LkNF-YBs, LkNF-YB3 showed the strongest responses to drought and ABA treatments. Further protein interaction prediction analysis for LkNF-YB3 revealed that LkNF-YB3 interacts with various factors associated with stress responses and epigenetic regulation as well as NF-YA/NF-YC factors. Taken together, these results unveiled novel L. kaempferi NF-YB family genes and their characteristics, providing the basic knowledge for further in-depth studies on their roles in abiotic stress responses of L. kaempferi. Full article

Histidine and cysteine residues, with their imidazole and thiol moieties that deprotonate at approximately physiological pH values, are primary binding sites for Zn(II), Ni(II) and Fe(II) ions and are thus ubiquitous both in peptidic metallophores and in antimicrobial peptides that may use nutritional immunity as a way to limit pathogenicity during infection. We focus on metal complex solution equilibria of model sequences encompassing Cys–His and His–Cys motifs, showing that the position of histidine and cysteine residues in the sequence has a crucial impact on its coordination properties. CH and HC motifs occur as many as 411 times in the antimicrobial peptide database, while similar CC and HH regions are found 348 and 94 times, respectively. Complex stabilities increase in the series Fe(II) < Ni(II) < Zn(II), with Zn(II) complexes dominating at physiological pH, and Ni(II) ones—above pH 9. The stabilities of Zn(II) complexes with Ac-ACHA-NH₂ and Ac-AHCA-NH₂ are comparable, and a similar tendency is observed for Fe(II), while in the case of Ni(II), the order of Cys and His does matter—complexes in which the metal is anchored on the third Cys (Ac-AHCA-NH₂) are thermodynamically stronger than those where Cys is in position two (Ac-ACHA-NH₂) at basic pH, at which point amides start to take part in the binding. Cysteine residues are much better Zn(II)-anchoring sites than histidines; Zn(II) clearly prefers the Cys–Cys type of ligands to Cys–His and His–Cys ones. In the case of His- and Cys-containing peptides, non-binding residues may have an impact on the stability of Ni(II) complexes, most likely protecting the central Ni(II) atom from interacting with solvent molecules. Full article

A series of four Werner-type complexes of Pd(II) and Pt(II) with planar, isomeric conjugated aromatic naphtoquinone oximes were synthesized for the first time. These ligands were 1-oxime-2-naphtoquinone (HL¹) and 2-oxime-1-napthoquinone (HL²). Compounds were characterized using thermal analysis, spectroscopic methods, and X-ray analysis. TG/DSC data were collected for pure starting organic ligands, their complexes, and indicated vigorous exothermic decomposition with at ~155 °C for starting HL and ~350 °C for transition metal complexes. Crystal structures for two Pt compounds with 2-oxime-1-quinone were determined and revealed the formation of the cis-geometry complexes and incorporation of molecules of stoichiometric solvents in the lattice: acetonitrile and nitrobenzene. Both solvents of crystallization displayed attractive interactions between their C-H groups and the oxygen atoms of the nitroso groups in complexes, leading to short distances in those fragments. Despite the presence of solvents of inclusion, the overall structure motifs in both compounds represent 1D columnar coordination polymer, in which the PtL₂ units are held together via metallophilic interactions, thereby forming ‘Pt-wires’. The Hirshfield surface analysis was performed for both crystallographically characterized complexes. The results showed intermolecular π–π stacking and Pt–Pt interactions among the planar units of both complexes. In addition, the analysis also verified the presence of hydrogen bonding interactions between the platinum unit and solvent molecules. Solid bulk powdery samples of both PtL¹₂ and PtL²₂ demonstrated pronounced photoluminescence in the near infrared region of spectrum at ~980 nm, being excited in the range of 750–800 nm. The NIR emission was observed only for Pt-complexes and not for pure starting organic ligands or Pd-complexes. Additionally, synthesized Pt-naphtoquinone oximes do not show luminescence in solutions, which suggests the importance of a 1D ‘metal wire’ structure for this process. Full article

‘Huangguan’ pear (Pyrus bretschneideri Rehd) fruit is susceptible to cold, characterized by developing peel browning spots (PBS) during cold storage. Additionally, ethylene pretreatment reduces chilling injury (CI) and inhibits PBS occurrence, but the mechanism of CI remains unclear. Here, we deciphered the dynamic transcriptional changes during the PBS occurrence with and without ethylene pretreatment via time-series transcriptome. We found that ethylene suppressed the cold-signaling gene expression, thereby decreasing the cold sensitivity of the ‘Huangguan’ fruit. Moreover, the “Yellow” module closely correlated with PBS occurrence was identified via weighted gene co-expression network analysis (WGCNA), and this module was related to plant defense via Gene Ontology (GO) enrichment analysis. Local motif enrichment analysis suggested that the “Yellow” module genes were regulated by ERF and WRKY transcription factors. Functional studies demonstrated that PbWRKY31 has a conserved WRKY domain, lacks transactivation activity, and localizes in the nucleus. PbWRKY31-overexpressed Arabidopsis were hypersensitive to cold, with higher expression levels of cold signaling and defense genes, suggesting that PbWRKY31 participates in regulating plant cold sensitivity. Collectively, our findings provide a comprehensive transcriptional overview of PBS occurrence and elucidate the molecular mechanism by which ethylene reduces the cold sensitivity of ‘Huangguan’ fruit as well as the potential role of PbWRKY31 in this process. Full article

In this work, the design and synthesis of a series of photochromic gemini diarylethene-based ionic liquids (GDILs) with different cationic motifs is reported. Several synthetic pathways were optimized for the formation of cationic GDILs with chloride as the counterion. The different cationic motifs were achieved through the N-alkylation of the photochromic organic core unit with different tertiary amines, including different aromatic amines such as imidazole derivatives and pyridinium, and other non-aromatic amines. These novel salts present surprising water solubility with unexplored photochromic features that broaden their known applications. The covalent attachment of the different side groups dictates their water solubility and differences upon photocyclization. The physicochemical properties of GDILs in aqueous and in imidazolium-based ionic liquid (IL) solutions were investigated. Upon irradiation with ultraviolet (UV) light, we have observed changes in the physico-chemical properties of distinct solutions containing these GDILs, at very low concentrations. More specifically, in aqueous solution, the overall conductivity increased with the time of UV photoirradiation. In contrast, in IL solution, these photoinducible changes are dependent on the type of ionic liquid used. These compounds can improve non-ionic and ionic liquids’ solutions since we can change their properties, such as conductivity, viscosity or ionicity, only by UV photoirradiation. The electronic and conformational changes associated with these innovative stimuli GDILs may open new opportunities for their use as photoswitchable materials. Full article

Capturing long-term statistics of signals and time series is important for modeling recurrent phenomena, especially when such recurrences are a-periodic and can be characterized by the approximate repetition of variable length motifs, such as patterns in human gestures and trends in financial time series or musical melodies. Regressive and auto-regressive models that are common in such problems, both analytically derived and neural network-based, often suffer from limited memory or tend to accumulate errors, making them sensitive during training. Moreover, such models often assume stationary signal statistics, which makes it difficult to deal with switching regimes or conditional signal dynamics. In this paper, we describe a method for time series modeling that is based on adaptive symbolization that maximizes the predictive information of the resulting sequence. Using approximate string-matching methods, the initial vectorized sequence is quantized into a discrete representation with a variable quantization threshold. Finding an optimal signal embedding is formulated in terms of a predictive bottleneck problem that takes into account the trade-off between representation and prediction accuracy. Several downstream applications based on discrete representation are described in this paper, which includes an analysis of the symbolic dynamics of recurrence statistics, motif extraction, segmentation, query matching, and the estimation of transfer entropy between parallel signals. Full article

We introduce simplicial persistence, a measure of time evolution of motifs in networks obtained from correlation filtering. We observe long memory in the evolution of structures, with a two power law decay regimes in the number of persistent simplicial complexes. Null models of the underlying time series are tested to investigate properties of the generative process and its evolutional constraints. Networks are generated with both a topological embedding network filtering technique called TMFG and by thresholding, showing that the TMFG method identifies high order structures throughout the market sample, where thresholding methods fail. The decay exponents of these long memory processes are used to characterise financial markets based on their efficiency and liquidity. We find that more liquid markets tend to have a slower persistence decay. This appears to be in contrast with the common understanding that efficient markets are more random. We argue that they are indeed less predictable for what concerns the dynamics of each single variable but they are more predictable for what concerns the collective evolution of the variables. This could imply higher fragility to systemic shocks. Full article