Derived Features Research Articles

Seeking the first phylogenetic method–Edvard A. Vainio (1853–1929) and his troubled endeavour towards a natural lichen classification in the late nineteenth century Finland

Edvard August Vainio was a world-renowned Finnish lichenologist. In Finland, however, he was a controversial person due to his strong pro-Finnish political views. Equally disputed was his opinion that systematics should be based on evolutionary theory and phylogenetic thinking. Vainio was familiar with the ideas of the early German phylogeneticists—especially those of Carl Wilhelm von Nägeli – and, applying them, aimed to create an exact method for building a natural classification of lichens already at the end of the nineteenth century. In this respect, Vainio was a true pioneer, as no actual phylogenetic method had yet been developed. In the general spirit of the time, Vainio focused on finding the ancestors of species and other taxa by comparing primitive and derived features of homologous characters. However, Vainio already understood the concept of sister groups in 1880, the identification of which is the basis of all modern phylogenetic research. Nevertheless, the distinctive method developed by Vainio was not so much focused on the construction of a phylogenetic tree, but on revealing the origin of species through the differentiation and fixation of their polymorphic variation. Indeed, Vainio’s species concept is surprisingly similar to the phylogenetic species concepts presented a hundred years later. Although in many ways progressive, Vainio’s views did not influence the development of phylogenetics more widely, but his discussions are nevertheless a valuable source to understanding the early development of phylogenetic theory.

Ventricular Arrhythmia Classification Using Similarity Maps and Hierarchical Multi-Stream Deep Learning.

Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventricular tachycardia (VT), ventricular fibrillation (VF) and non-ventricular rhythms (NVR). To address the challenging problem of the discrimination between VT and VF, we develop similarity maps - a novel set of features designed to capture regularity within an ECG trace. These similarity maps are combined with features extracted through learnable Parzen band-pass filters and derivative features to discriminate between VT, VF, and NVR. To combine the benefits of these different features, we propose a hierarchical multi-stream ResNet34 architecture. Our empirical results demonstrate that the similarity maps significantly improve the accuracy of distinguishing between VT and VF. Overall, the proposed approach achieves an average class sensitivity of 89.68%, and individual class sensitivities of 81.46% for VT, 89.29% for VF, and 98.28% for NVR. The proposed method achieves a high accuracy of ventricular arrhythmia detection and classification. Correct detection and classification of ventricular fibrillation and ventricular tachycardia are essential for effective intervention and for the development of new therapies and translational medicine.

Computational Study on Flavin-Catalyzed Aerobic Dioxygenation of Alkenyl Thioesters: Decomposition of Anionic Peroxides.

Flavin-dependent catalysts are widely applied to aerobic monooxygenation/oxidation reactions. In contrast, flavin-catalyzed aerobic dioxygenation reactions exhibit higher atomic economy but are less reported, not to mention the relevant mechanistic studies. Herein, a density functional theory study on flavin-catalyzed aerobic epoxidation-oxygenolysis of alkenyl thioesters was performed for the first time. Different from the previous mechanistic proposal, a pathway featuring two catalytic stages, monoanionic flavin-C(4a)-peroxide/oxide intermediates, and a reverse reaction sequence (epoxidation goes prior to oxygenolysis) was revealed. In comparison, the pathways involving dianionic flavin catalysts, monoanionic flavin-N(5)-(hydro)peroxide/C(10a)-peroxide, or neutral flavin-C(4a)-hydroperoxide/hydroxide/N(5)-oxide, and the pathways where oxygenolysis goes prior to epoxidation are less favored. Epoxidation goes through intramolecular substitution of the O-O bond of anionic flavin-C(4a)-peroxide by β-carbon, while the resulting flavin-C(4a)-oxide accomplishes the oxygenolysis. Furthermore, two other reaction modes, i.e., concerted O-O cleavage/1,2-shift of α-substituents and dyotropic rearrangement were discovered for the decomposition of other anionic peroxides, and preliminary rules were summarized for understanding the chemoselectivity for this process. This study sheds light on the different reaction features of numerous flavin-dioxygen derivatives, providing deeper insights into flavin-catalyzed dioxygenation reactions, and is expected to inspire experimental design based on unconventional anionic peroxides.

Postcranial anatomy of Besanosaurus leptorhynchus (Reptilia: Ichthyosauria) from the Middle Triassic Besano Formation of Monte San Giorgio (Italy/Switzerland), with implications for reconstructing the swimming styles of Triassic ichthyosaurs

Besanosaurus leptorhynchus Dal Sasso & Pinna, 1996 was originally described on the basis of a single complete fossil specimen excavated near Besano (Italy). However, a recent taxonomic revision and re-examination of the cranial osteology allowed for the assignment of five additional specimens to the taxon. Here, we analyse, describe and discuss the postcranial anatomy of Besanosaurus leptorhynchus in detail. The size of the specimens examined herein ranged from slightly more than one meter to eight meters. Overall, several diagnostic character states for this taxon are proposed, demonstrating a mosaic of plesiomorphic and derived features. This is best exemplified by the limbs, which show very rounded elements in the forelimbs, and pedal phalanges with retained rudimentary shafts. We suggest that the widely spaced phalanges in the forefins of Besanosaurus leptorhynchus were embedded in a fibrocartilage-rich connective tissue, like in modern cetaceans. We also review the similarities of Besanosaurus with Pessopteryx and Pessosaurus, allowing us to conclude that Besanosaurus is not a junior synonym of either of the two taxa. Lastly, to test the swimming capabilities of Besanosaurus leptorhynchus, we expanded on a previously published study focussing on reconstructing the swimming styles of ichthyosaurs. Besanosaurus leptorhynchus was found to possess a peculiar locomotory mode, somewhat intermediate between anguilliform swimmers, such as Cymbospondylus and Utatsusaurus, and some shastasaur-grade (e.g., Guizhouichthyosaurus) and early-diverging euichthyosaurian (e.g., Californosaurus) ichthyosaurs. Based on our results, we furthermore suggest that mixosaurids acquired their characteristic body profile (dorsal fin and forefins that are distinctly enlarged compared to the hindfins) independently and convergently to the one that later appeared in Parvipelvia. Moreover, the different swimming styles inferred for Cymbospondylus, Mixosauridae, and Besanosaurus strengthen the earlier hypothesis of niche partitioning among these three distinct ichthyosaur taxa from the Besano Formation.

Organocatalyzed Asymmetric Cascade Michael/Michael Reaction of α‐Alkylidene Succinimides with a 2‐(Trifluoromethylmethylene)indane‐1,3‐dione

AbstractA diaminomethylenemalononitrile organocatalyst efficiently promoted asymmetric cascade Michael/Michael reactions between a 2‐(trifluoromethylmethylene)indane‐1,3‐dione derivative and α‐alkylidene succinimides, resulting in spiroindane‐1,3‐dione derivatives. These derivatives feature four consecutive carbon stereocenters, including one stereocenter with a trifluoromethyl substituent, and were formed in high yield with excellent enantioselectivity (up to 94 % ee). This study marks the first successful example of using methyleneindane‐1,3‐dione derivatives, bearing alkoxycarbonyl and trifluoromethyl groups at the β‐position, as Michael acceptors.

Exploring the dynamic relationship: Changes in photoplethysmography features corresponding to intracranial pressure variations

This study investigates the relationship between photoplethysmography (PPG) signals and intracranial pressure (ICP) through two primary hypotheses. Firstly, it examines whether alterations in PPG-derived features correspond to changes in ICP levels. Secondly, it explores whether these changes are more pronounced in features derived from “cerebral” long-distance near-infrared (NIR) PPG data compared to “extracerebral” short-distance NIR-PPG data. A clinical dataset comprising synchronised measurements from a non-invasive NIR-PPG sensor and an intra-parenchymal, invasive ICP pressure probe across 27 patients was compiled. From this dataset, two distinct datasets were derived, comprising short and long-distance NIR-PPG data. Within each dataset, 141 features were extracted for every one-minute window of non-invasive NIR-PPG data, including original, first derivative, and second derivative features. Correlation analysis using Spearman’s correlation and a non-parametric Kruskal–Wallis test across the range of ICP values were conducted to evaluate the relationship between features and ICP levels. The results support both hypotheses, showing significant correlations between the features and ICP levels. Specifically, 77.30% and 79.43% of features significantly correlated (p<0.05) with the label in distal and proximal datasets, respectively. Kruskal–Wallis analysis revealed that 81.56% and 75.89% of features significantly changed (p<0.05) across ICP groups 0–10, 10–20, and 20–39 mmHg. The distal dataset yielded a meaningfully higher absolute average correlation coefficient of all features and significantly correlated features in-comparison to the proximal dataset of 25.76% and 24.24% respectively. These findings indicate NIR-PPG features are reflective of variations in ICP.

The Magnetic Properties of Fluorenyl and tert-Butyl-nitroxyl Acene-Based Derivatives: A Quantum Chemical Insight

Acenes, as a class of polycyclic aromatic hydrocarbons, attract considerable attention due to their remarkable nonlinear optical and magnetic properties. The aim of this work was the elucidation of the capability of radical-substituted acene derivatives to undergo spin-state-switching rearrangements. For this purpose, a series of acene-based (anthracene, pentacene, heptacene) molecules bearing fluorenyl and tert-butyl-nitroxyl radicals were investigated through comprehensive quantum chemical modeling of their electronic structures, isomerization and magnetic properties. A possible mechanism of the transformation of the closed-shell folded isomer into the biradical twisted structure of the bis-fluorenyl anthracene has been ascertained by applying the procedure of searching for the Minimum Energy Crossing Point. The conditions favoring the occurrence of spin-state-switching in such classes of polycyclic aromatic hydrocarbon derivatives have been formulated. By varying the size of an acene core and the type of radical substituent, the compounds capable of changing their magnetic properties have been revealed. Considering the unique features of radical-bearing acene-based derivatives, the proposed molecules can be used as functional materials in photonics and electronics.

Mining sensitive hyperspectral feature to non-destructively monitor biomass and nitrogen accumulation status of tea plant throughout the whole year

Rapid and non-destructive estimation of tea plant growth and nitrogen (N) nutrition status using hyperspectral remote sensing is crucial for precise management of tea gardens. This study aimed to mine and fuse sensitive hyperspectral features to achieve an accurate estimation of tea plant growth parameters (biomass and N accumulation) throughout the whole year. An ASD Handheld 2 sensor was used to collect canopy hyperspectral reflectance of tea plants across four periods (Period 1–4) within a year, with tea plant biomass and N accumulation indicators acquired synchronously. The measured spectral reflectance and its first derivative, and wavelet feature were extracted and used to establish quantitative relationships with tea plant growth parameters. Random forest and LASSO algorithms were employed to combine sensitive hyperspectral features and construct the biomass and N accumulation monitoring models. The results showed that wavelet features (R2 = 0.35–0.58) had a stronger correlation with tea plant biomass and N accumulation parameters compared with the measured reflectance or first derivative spectral features. Similarly, the hyperspectral indices (R2 = 0.51–0.69) derived from sensitive wavelet features performed an accurate estimation of tea plant growth parameters. Furthermore, the combination of sensitive hyperspectral indices derived from measured reflectance, first derivative, and wavelet feature using random forest (R2 = 0.67–0.76) and LASSO (R2 = 0.61–0.72) algorithms achieved the greatest accuracy for monitoring tea plant biomass and N accumulation compared with individual hyperspectral feature. Additionally, the above estimation models obtained higher accuracy in period 4 compared to periods 1–3. This study provides valuable remote sensing technical support for predicting biomass and N accumulation status of tea plant throughout the whole year.

Reassessing the management of uncomplicated urinary tract infection: A retrospective analysis using machine learning causal inference.

Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimicrobial therapy. National guidelines for the management of uncomplicated UTI were published by the Infectious Diseases Society of America in 2011, however it is not fully known the extent to which they align with current practices, patient diversity, and pathogen biology, all of which have evolved significantly in the time since their publication. We aimed to re-evaluate efficacy and adverse events for first-line antibiotics (nitrofurantoin, and trimethoprim-sulfamethoxazole), versus second-line antibiotics (fluoroquinolones) and versus alternative agents (oral β-lactams) for uncomplicated UTI in contemporary clinical practice by applying machine learning algorithms to a large claims database formatted into the Observational Medical Outcomes Partnership (OMOP) common data model. Our primary outcome was a composite endpoint for treatment failure, defined as outpatient or inpatient re-visit within 30 days for UTI, pyelonephritis or sepsis. Secondary outcomes were the risk of 4 common antibiotic-associated adverse events: gastrointestinal symptoms, rash, kidney injury and C. difficile infection. We adjusted for covariate-dependent censoring and treatment indication using a broad set of domain-expert derived features. Sensitivity analyses were conducted using OMOP-learn, an automated feature engineering package for OMOP datasets. Our study included 57,585 episodes of UTI from 49,037 patients. First-line antibiotics were prescribed in 35,018 (61%) episodes, second-line antibiotics were prescribed in 21,140 (37%) episodes and alternative antibiotics were prescribed in 1,427 (2%) episodes. After adjustment, patients receiving first-line therapies had an absolute risk difference of -2.1% [95% CI -2.9% to -1.6%] for having a revisit for UTI within 30 days of diagnosis relative to second-line antibiotics. First-line therapies had an absolute risk difference of -6.6% [95% CI -9.4% to -3.8%] for 30-day revisit compared to alternative β-lactam antibiotics. Differences in adverse events were clinically similar between first and second line agents, but lower for first-line agents relative to alternative antibiotics (-3.5% [95% CI -5.9% to -1.2%]). Results were similar for models built with OMOPlearn. Our study provides support for the continued use of first-line antibiotics for the management of uncomplicated UTI. Our results also provide proof-of-principle that automated feature extraction methods for OMOP formatted data can emulate manually curated models, thereby promoting reproducibility and generalizability.

Improving sepsis prediction in intensive care with SepsisAI: A clinical decision support system with a focus on minimizing false alarms.

Prediction of sepsis using machine-learning approaches has recently gained traction. However, the lack of translation of these algorithms into clinical routine remains a major issue. Existing early sepsis detection methods are either based on the older definition of sepsis or do not accurately detect sepsis leading to the high frequency of false-positive alarms. This results in a well-known issue of clinicians' "alarm fatigue", leading to decreased responsiveness and identification, ultimately resulting in delayed clinical intervention. Hence, there is a fundamental, unmet need for a clinical decision system capable of accurate and timely sepsis diagnosis, running at the point of need. In this work, SepsisAI-a deep-learning algorithm based on long short-term memory (LSTM) networks was developed to predict the early onset of hospital-acquired sepsis in real-time for patients admitted to the ICU. The models are trained and validated with data from the PhysioNet Challenge, consisting of 40,336 patient data files from two healthcare systems: Beth Israel Deaconess Medical Center and Emory University Hospital. In the short term, the algorithm tracks frequently measured vital signs, sparsely available lab parameters, demographic features, and certain derived features for making predictions. A real-time alert system, which monitors the trajectory of the predictions, is developed on top of the deep-learning framework to minimize false alarms. On a balanced test dataset, the model achieves an AUROC, AUPRC, sensitivity, and specificity of 0.95, 0.96, 88.19%, and 96.75%, respectively at the patient level. In terms of lookahead time, the model issues a warning at a median of 6 hours (IQR 6 to 20 hours) and raises an alert at a median of 4 hours (IQR 2 to 5 hours) ahead of sepsis onset. Most importantly, the model achieves a false-alarm ratio of 3.18% for alerts, which is significantly less than other sepsis alarm systems. Additionally, on a disease prevalence-based test set, the algorithm reported similar outcomes with AUROC and AUPRC of 0.94 and 0.87, respectively, with sensitivity, and specificity of 97.05%, and 96.75%, respectively. The proposed algorithm might serve as a clinical decision support system to assist clinicians in the accurate and timely diagnosis of sepsis. With exceptionally high specificity and low false-alarm rate, this algorithm also helps mitigate the well-known issue of clinician alert fatigue arising from currently proposed sepsis alarm systems. Consequently, the algorithm partially addresses the challenges of successfully integrating machine-learning algorithms into routine clinical care.

The Emergence of Verb Patterns in Arabic in Children With Developmental Language Disorder Compared to Children With Typical Development.

The Arabic verb system features a nonlinear root and pattern derivational morphology. Previous studies suggest that young Arabic and Hebrew speakers' early verb use is based on semantic complexity rather than derivational morphological structure. The present study examines the role of morphological and semantic complexity in the emergence of the verb derivational morphology in Arabic speaking children with developmental language disorder (DLD) compared to children with typical language development (TLD). Natural language data were collected from native Arabic-speaking children (40 with DLD; aged 4-6 years and 133 with TLD aged 2;6-6;0 [years;months]) using picture-based elicitation tasks, and verbs were coded morphologically for derivational features and for features of semantic complexity. The results showed that children with DLD demonstrated a more limited production of verb patterns, both in types and in tokens, than age-matched children with TLD. Also, children with DLD age 5;0-6;0 were similar in types and tokens of verb patterns to younger children with TLD at the age of 3;6-4 years. Moreover, while children with TLD at the age of 2;6-3 years used a smaller number of verb patterns than older 4;0-5;0 aged children with DLD, the two groups were not different in verb semantics. Finally, the morphological and semantic diversity demonstrated by the children with DLD was similar to the morphological and semantic diversity shown by children with TLD. Our findings support the conclusion that children with DLD and with TLD acquire the derivational verb system in the same pathway and the quantitative lexical differences between the two groups support a delay rather than a deviation from the typical developmental trajectory.

New quantitative analyses of the Nacholapithecus kerioi proximal ulna confirm morphological affinities with Equatorius and large papionins.

The elbow of Nacholapithecus has been extensively described qualitatively, however its ulnar morphology has never been the focus of an in-depth quantitative analysis before. Hence, our main aim is quantifying the proximal ulnar morphology in Nacholapithecus and exploring whether it is similar to those of Equatorius and Griphopithecus as previously reported. We compared Nacholapithecus proximal ulnar morphology with a sample of extant and extinct anthropoids through principal component analysis and agglomerative hierarchical cluster analysis. Moreover, we calculated the Cophenetic Correlation Coefficient and checked for taxonomical group mean differences through MANOVA and pairwise post-hoc comparisons, as well as the phylogenetic signal in the variables used. The Nacholapithecus ulna displays a moderately long and relatively narrow olecranon, a relatively wide trochlear surface-radial notch width, and a relatively thin sigmoid notch depth. These features resemble those of large papionins and chimpanzees, and some extinct taxa, mainly Equatorius. Results presented here reinforce previous inferences on the functional morphology of the Nacholapithecus elbow, showing adaptations for general quadrupedal behaviors. However, other derived features (e.g., a relatively wide trochlear surface) might be associated with the ape-like traits described for its distal humerus (e.g., wide trochlear groove), thus displaying a combination of primitive and derived features in the proximal ulna. Finally, affinities with large papionins could suggest the presence of some terrestrial habits in Nacholapithecus. However, the lack of evidence in the rest of the skeleton prevents us from suggesting terrestrial affinities in this taxon in a conclusive manner.

Evaluation of the protective properties and genotoxic potential of pyrazolo pyridine derivatives against neutron and gamma radiation using the Ames/Salmonella test system

Purpose Nuclear applications are being increasingly used in various fields, necessitating studies to protect from radiation hazards and their effects. In this study, five different chemical structures of pyrazolo [3,4-b] pyridine derivatives were synthesized. The gamma and neutron radiation protective abilities of these samples were determined and demonstrated their potential use as ingredients in radioprotective drugs. Material and methods Gamma radiation absorption parameters were calculated both theoretical and experimental. Important attenuation parameters for fast neutrons (4.5 MeV energy radiation) were figured out using the Monte Carlo simulation Geant4 code. Additionally, experimental dose rates were measured for each sample and compared to those of Paraffin and high-density polyethylene, an organic substance. Besides, Ames/Salmonella test system was aimed to detecting genotoxicity features of pyrazolo pyridine derivatives. Results All results demonstrated that each sample possesses both gamma and neutron radiation attenuation capabilities. It was determined that sample PPC4 (C20H14BrN5) exhibited the highest gamma radiation attenuation capacity among all samples, while sample PPC2 (C22H20N6) displayed an excellent neutron stopping capacity. The genotoxic properties of pyrazolo[3,4-b] pyridine derivatives were examined using the Ames/Salmonella test, and as a result, it was determined that these substances did not exhibit genotoxic effects at test doses up to 5 mM. Conclusion All obtained results indicate that all PPC (pyrazolo[3,4-b] pyridine derivatives) samples do not possess a toxic effect, and they can be utilized as an active substance for the development of a drug or cream with protective properties against both gamma and neutron radiations.

A Novel Financial Anti-fraud Method based on Machine Learning Algorithms

Digital finance is booming, financial technology is maturing, and the development of information technology has had a massively positive impact on society. However, such progress also introduces a new type of risk: the underground network industry is experiencing explosive growth, and telecommunications network fraud has caused enormous financial losses. In the age of digital finance, although commercial banks have ushered in new possibilities and created momentum, they must also confront new obstacles and needs for digital transformation. In this context, online financial services have emerged as the new primary battleground. In this study, based on the RFM high-dimensional derived features and machine learning techniques, a high-dimensional transaction behavior portraits-based anti-fraud machine learning model is created. Using big data, stream computing, and other technologies, as well as systematic deployment, application strategy, and iterative model optimization, we developed a set of machine learning-based in-event risk control solutions. Confirmed to have an AUC of 0.972, this model provides key insight into fraud risk, identifies fraudulent transactions in milliseconds, and has value and relevance for enhancing the in-transaction risk management capabilities of online digital financial organizations.

Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model.

Recent advances in artificial intelligence, particularly with large language models like GPT-4Vision (GPT-4V)-a derivative feature of ChatGPT-have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT-4V in image classification tasks of histopathological images and compares its performance with a traditional convolutional neural network (CNN). We utilised 1520 images, including haematoxylin and eosin staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer's disease (AD), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). We assessed GPT-4V's performance using multi-step prompts to determine how textual context influences image interpretation. We also employed few-shot learning to enhance improvements in GPT-4V's diagnostic performance in classifying three specific tau lesions-astrocytic plaques, neuritic plaques and tufted astrocytes-and compared the outcomes with the CNN model YOLOv8. GPT-4V accurately recognised staining techniques and tissue origin but struggled with specific lesion identification. The interpretation of images was notably influenced by the provided textual context, which sometimes led to diagnostic inaccuracies. For instance, when presented with images of the motor cortex, the diagnosis shifted inappropriately from AD to CBD or PSP. However, few-shot learning markedly improved GPT-4V's diagnostic capabilities, enhancing accuracy from 40% in zero-shot learning to 90% with 20-shot learning, matching the performance of YOLOv8, which required 100-shot learning to achieve the same accuracy. Although GPT-4V faces challenges in independently interpreting histopathological images, few-shot learning significantly improves its performance. This approach is especially promising for neuropathology, where acquiring extensive labelled datasets is often challenging.

OIKONIMICON OF LESYA UKRAINKA’S DRAMATURGY

The article analyzes the semantic features of the oikonyms of Lesya Ukrainka’s drama. The main focus is on connotations acquired by oikonyms in the artistic expression of Lesya Ukrainka. Oikonyms, i.e. proper names of settlements, are historical and cultural heritage, since they reflect historical events, life, culture of the people, as well as their customs and traditions. Semantic, derivational and stylistic features of oikonyms depend on the individual characteristics of the author of the work, genre specificity, time of creation, literary direction and method. The purpose of the article is to analyze the functional load and semantic content of oikonyms that occur in Lesya Ukrainka’s dramatic works. The object of the research is the toponymic vocabulary in Lesya Ukrainka’s drama, the subject is the semantic and functional features of oikonyms as a component of the artistic and figurative system of the work. During our research, a number of methods were used, among which the descriptive method (used to analyze factual material), the quantitative counting method (to indicate the number of oikonyms in the work of art), and elements of the component analysis method, have a leading role. Oikonymicon in the artistic space of Lesya Ukrainka’s dramas is an active artistic technique that illustrates the author’s voice, the author’s concept of the characters and of the work in general, an important factor in character formation and plot development. And even the absence of oikonyms in the context of drama or its muted function becomes the manifestation and shade of the most important thing that the dramatist lays into the artistic background of the work. This helps reveal other problems that the author considers too important and for the sake of which gives life to her poetic masterpieces in pain and exhaustion, which suffered such a difficult stage fate and which are only now being read to full extent by a Ukrainian reader. We define the semantic study of the oikonymy of modern writers as the further direction of our exploration.

Multispecies discrimination of New World Warblers (Family Parulidae) using Hidden Markov models (HMMS)

Bioacoustics allows researchers to study animals through their vocalizations with non-invasive methods. The analysis of the recordings is a difficult task that is best handled by machine learning methods. Hidden Markov Models (HMMs), which are machine learning methods in human speech processing, were developed and implemented for the discrimination of 11 genera and 43 species of the New World Warblers (family Parulidae). Based on the CLO-43SD database, the fundamental goal of the experiments was to determine the classification accuracy on the specific genus and species of birds. Through Mel-Frequency Cepstral Coefficients (MFCCs) along with log energy and time derivative features extracted from the vocalizations, HMMs containing 2 states with single underlying Gaussian Mixture Models (GMMs) generated classification accuracies 91.55% across 11 genera of birds and 63.92% for 43 species of birds. From the results, the framework could be applied to analysis of other birds for both classification and detection of vocalizations.

Target repurposing unravels avermectins and derivatives as novel antibiotics inhibiting energy-coupling factor transporters (ECFTs).

Energy-coupling factor transporters (ECFTs) are membrane-bound ATP-binding cassette (ABC) transporters in prokaryotes that are found in pathogens against which novel antibiotics are urgently needed. To date, just 54 inhibitors of three molecular-structural classes with mostly weak inhibitory activity are known. Target repurposing is a strategy that transfers knowledge gained from a well-studied protein family to under-studied targets of phylogenetic relation. Forty-eight human ABC transporters are known that may harbor structural motifs similar to ECFTs to which particularly multitarget compounds may bind. We assessed 31 multitarget compounds which together target the entire druggable human ABC transporter proteome against ECFTs, of which nine showed inhibitory activity (hit rate 29.0%) and four demonstrated moderate to strong inhibition of an ECFT (IC50 values between 4.28 and 50.2 µM) as well as antibacterial activity against ECFT-expressing Streptococcus pneumoniae. Here, ivermectin was the most potent candidate (MIC95: 22.8 µM), and analysis of five ivermectin derivatives revealed moxidectin as one of the most potent ECFT-targeting antibacterial agents (IC50: 2.23 µM; MIC95: 2.91 µM). Distinct molecular-structural features of avermectins and derivatives as well as the differential biological response of the hit compounds in general provided first indications with respect to the structure-activity relationships and mode of action, respectively.

In silico investigation and biological evaluation viz antimicrobial, genotoxic, and anticancer potentials of new dihydropyrimidinones (DHPMs) synthesized by Biginelli reaction

Dihydropyrimidin-2(1H)-ones (DHPMs) are attracting growing interest in synthetic and medical organic chemistry due to their biological and therapeutic potential. In this study, we synthesized DHPM derivatives via the Biginelli reaction from bisdialdehyde to identify lead compounds. Using SWISSADME analysis, we assessed druglikeness features of the DHPM derivatives. Antimicrobial properties, including antibacterial and antifungal activities, were evaluated, and promising compounds were tested for their ability to inhibit A549 lung cancer cell growth. Thin-layer chromatography confirmed product formation, while spectroscopic methods characterized the structures. Lipophilicity values indicated strong oral absorption potential. In vitro drug release and cell viability studies were conducted. DHPMs 7, 8, and B1 exhibited broad-spectrum antimicrobial activity. Molecular docking revealed DHPM 2′s potential in cancer treatment via effective binding with VEGFR-2 kinase. Genotoxic and anticancer activities were assessed, with DHPM 8 showing promising action against dangerous bacteria and fungi. Further research is warranted to explore therapeutic applications of these compounds.

Time series clustering based on polynomial fitting and multi-order trend features

Time series clustering serves as a potent data mining method, facilitating the analysis of an extensive array of time series data without the prerequisite of any prior knowledge. It finds wide-ranging use across various sectors, including but not limited to, financial and medical data analysis, and sensor data processing. Given the high dimensionality, non-linearity, and redundancy characteristics associated with time series, conventional clustering algorithms frequently fall short in yielding satisfactory results when directly applied to this kind of data. As such, there is a critical need to judiciously select suitable feature extraction methods and dimension reduction techniques. This paper introduces a time series clustering algorithm, drawing primarily from polynomial fitting derivative features as a wellspring for feature extraction to achieve effective clustering results. Initially, Hodrick Prescott (HP) filtering comes into play for the processing of raw time series data, thereby eliminating noise and redundancy. Subsequently, polynomial curve fitting (PCF) is applied to the data to derive a globally continuous function fitting this time series. Next, by securing multi-order derivative values via this function, the time series is transformed into a multi-order derivative feature sequence. Lastly, we designed a polynomial function derivative features-based dynamic time warping (PFD_DTW) algorithm for determining the distance between two equal or unequal granular length time series, and subsequently a hierarchical clustering method anchored on the PFD_DTW distances for time series clustering after computing interspecies distances. The effectiveness of this method is corroborated by experimental results obtained from several practical datasets.