Analysis Of Features Research Articles

Inside the Host: Understanding the Evolutionary Trajectories of Intracellular Parasitism.

This review explores the origins of intracellular parasitism, an intriguing facet of symbiosis, where one organism harms its host, potentially becoming deadly. We focus on three distantly related groups of single-celled eukaryotes, namely Kinetoplastea, Holomycota, and Apicomplexa, which contain multiple species-rich lineages of intracellular parasites. Using comparative analysis of morphological, physiological, and molecular features of kinetoplastids, microsporidians, and sporozoans, as well as their closest free-living relatives, we reveal the evolutionary trajectories and adaptations that enabled the transition to intracellular parasitism. Intracellular parasites have evolved various efficient mechanisms for host acquisition and exploitation, allowing them to thrive in a variety of hosts. Each group has developed unique features related to the parasitic lifestyle, involving dedicated protein families associated with host cell invasion, survival, and exit. Indeed, parallel evolution has led to distinct lineages of intracellular parasites employing diverse traits and approaches to achieve similar outcomes.

Silent Letters in the Balinese Script | | Adeg Adeg: A Graphetic and Graphematic Feature Analysis

The concept of “silent letters” relates to graphic symbols that are not pronounced or that do not have mapping with a specific linguistic unit. In the context of processing the mapping of sequences of linguistic units to writing, this concept often results in ambiguity. The Balinese script, which refers to a silent letter, appears in a basic shape || adeg adeg. Scholars of Balinese script define adeg adeg as a process to eliminate characters (Akshara’s), resulting in consonants without inherent vowels. This explanation is very metaphorical, ambiguous, and not based on adequate terms to describe graphetic and graphematic features that are related to mappings between graphic unit sequences and linguistic unit sequences. Balinese script || adeg adeg as primary data comes from the 16.162 lemma in the Balinese dictionary - Indonesian with Latin and Balinese script. Adeg adeg is analyzed both graphically and graphematically to identify its characteristics, with the stages of analysis adapted from the Multimodular Model of Writing System proposed by Meletis (2018). Graphetic analysis shows that the adeg adeg is identified as basic shapes || with three salient graphetic features: -recodability, -paired variants, and +additive. Graphematically, adeg-adeg is defined as a grapheme <> with the following features: +lexical distinctiveness, +linguistic value, and +minimality. The clarity of graphetic and graphematic characteristics significantly contributes to a more comprehensive and transparent cognitive-linguistic mechanism depiction, especially in deciphering writing systems based on Akshara’s highly complex characteristics.

Adaptive Parallel Scheduling Scheme for Smart Contract

With the increasing demand for decentralized systems and the widespread usage of blockchain, low throughput and high latency have become the biggest stumbling blocks in the development of blockchain systems. This problem seriously hinders the expansion of blockchain and its application in production. Most existing smart contract scheduling solutions use static feature analysis to prevent contract conflicts during parallel execution. However, the conflicts between transactions are complex; static feature analysis is not accurate enough. In this paper, we first build the dependency between smart contracts by analyzing the features. After numerous experiments, we propose a conflict model to adjust the relationship between threads and conflict to achieve high throughput and low latency. Based on these works, we propose adaptive parallel scheduling for smart contracts on the blockchain. Our adaptive parallel scheduling can distinguish conflicts between smart contracts and dynamically adjust the execution strategy of smart contracts based on the conflict factors we define. We implement our scheme on ChainMaker, one of the most popular open-source permissioned blockchains, and build experiments to verify our solution. Regarding latency, our solution demonstrates remarkable efficiency compared with the fully parallel scheme, particularly in high-conflict transaction scenarios, where our solution achieves latency levels just one-twentieth of the fully parallel scheme. Regarding throughput, our solution significantly outperforms the fully parallel scheme, achieving 30 times higher throughput in high-conflict transaction scenarios. These results highlight the superior performance and effectiveness of our solution in addressing latency and throughput challenges, particularly in environments with high transaction conflicts.

DRIVER DROWSINESS DETECTION AND CREATING SAFETY MEASURES USING OPENCV,PYTHON

Accidents due to driver drowsy driving is significantly increasing in high ratio now days. So we proposed an AI based solution to reduce the driver drossy driving accident by alerting the driver when they fall asleep. This is an brief abstract that explains system working and principles. This paper present a novel, Real-Time Driver Drowsiness Detection System(DDDS) that utilizes a multi-model approach for fatigue assessment. The system incorporates computer vision and machine learning techniques to analyze facial features, eye movements, and physical signal(if applicable) captured through a webcam processing techniques enhance data processing techniques enhanced data quality for better analysis.Facial landmarks are identified to locate the eyes, and features like Eye Aspect Ratio(EAR) are used to detect signs of fatigue. The system employs a multimodal approach, utilizing computer vision techniques to analyze facial features and eye movements captured through a webcam. Specifically, the DDDS leverages the Haar cascade algorithm for efficient facial landmark detection and the Histogram of Oriented Gradients (HOG) algorithm for feature extraction from facial regions of interest. This combination allows for accurate localization and analysis of key features like eyes. Additionally, the system explores the potential of physiological data (e.g., heart rate variability) for a more comprehensive analysis (if applicable). Machine learning techniques (if applicable) can be employed on the combined features, including those extracted using Haar and HOG, for enhanced fatigue classification. Upon detecting drowsiness, the DDDS triggers customizable alerts (visual, audio, or haptic) to warn the driver. The system additionally explores the potential of physiological data (e.g., heart rate variability) for fatigue detection, offering a more comprehensive approach (if applicable).A machine learning model can be employed on the combined features for robust fatigue classification. Upon detection of drowsiness,the DDDS triggers customizable alerts ( visual, audio, or haptic) to warn the driver. This system addresses limitations of existing solution by: 1.Real-Time processing:Ensuring immediate detection and response to fatigue signs. 2.Multi feature analysis: Combining facial features, eye movements,and physiological data for robust detection. 3.Customizable alerts: Providing drivers control over alert type for a personalized experience. The evaluation methodology employs a dataset of [data type e.g.., image, videos] to access the system’s performance.Metrics like accuracy,precision,recall,and F1-score will be used to evaluate the effectiveness of the DDDS in detecting drowsy driving. This research aims to contribute to safer roads by developing a comprehensive and adaptable Driver Drowsiness Detection System. II.KEYWORDS: Drowsy Driving, Driver Monitoring,Computer Vision, Machine Learning,Eye Tracking, Fatigue Detection,Physiological Signs(if applicable)

Emergency Vehicle Detection and Traffic Prevention Using Open CV

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization

Tick-Borne Bacterial Diseases in Europe: Threats to public health.

Tick-borne diseases, caused by bacterial pathogens, pose a growing threat to public health in Europe. This paperprovides an overview of the historical context of the discovery of the most impactful pathogens transmitted by ticks,including Borrelia burgdorferi sensu lato, Rickettsia spp., Anaplasma spp., Francisella spp., Ehrlichia spp., andNeoehrlichia mikurensis. Understanding the historical context of their discovery provides insight into the evolutionof our understanding of these pathogens. Systematic investigation of the prevalence and transmission dynamics of these bacterial pathogens is provided,highlighting the intricate relationships among ticks, host organisms, and the environment. Epidemiology is explored, providing an in-depth analysis of clinical features associated with infections. Diagnostic methodologies undergocritical examination, with a spotlight on technological advancements that enhance detection capabilities.Additionally, the paper discusses available treatment options, addressing existing therapeutic strategies andconsidering future aspects. By integrating various pieces of information on these bacterial species, the paper aims to provide a comprehensiveresource for researchers and healthcare professionals addressing the impact of bacterial tick-borne diseases inEurope. This review underscores the importance of understanding the complex details influencing bacterialprevalence and transmission dynamics to better combat these emerging public health threats.

Degradation and polarization curve prediction of proton exchange membrane fuel cells: An interpretable model perspective

Balancing the performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) is a significant challenge. Current data-driven technologies mainly focus on improving the accuracy of performance degradation prediction. However, these methods are often considered as black-box models, where the internal workings of the model are in an unknown state, making it challenging to explain the relationship of each feature with performance/durability. To address this challenge, a novel interpretable model for PEMFC performance degradation and polarization curve is proposed, incorporating feature selection interpretation and black-box model interpretation. The key innovation of this paper lies in transforming the black-box model into a dual interpretable transparent model both before and during the learning process. Additionally, the prediction and interpretation of PEMFC polarization curves are performed. The Boruta algorithm is employed to interpret the significance of each feature's impact on the model, filtering out key feature variables. Based on the Boruta-selected feature variables, the SHapley Additive exPlanations (SHAP) algorithm is used to calculate the marginal contribution of each numerical value to the predicted target. This provides an in-depth explanation of the internal workings of the model, analyzing how each predictive factor influences the model results. Through Boruta feature analysis, it is discovered that the cathode characteristics of PEMFC have a greater impact on performance degradation than anode characteristics. The SHAP algorithm interpretative analysis identifies the optimal flow rates corresponding to the best performance for the cathode hydrogen flow rate and anode oxygen flow rate as 0.090 to 0.13 NLPM and 0.39 to 0.50 NLPM, respectively. This indicates that the algorithmic framework has a certain physical meaning in explaining PEMFC performance degradation.

Evaluation of multiple machine learning models for ASR expansion of concrete

Machine learning algorithms (MLA) show promise in predicting alkali-silica reaction (ASR) expansion. Nevertheless, the predictive performance of different machine learning algorithms (MLAs) varies across various engineering scenarios and datasets. Given that, this study was devoted to constructing an appropriate predictive model for ASR expansion of concrete. Multiple MLAs including evolutionary-based, support vector-based, assembly-based, and neural network-based algorithms, were employed to establish predictive models. An optimization technique, the particle swarm optimization algorithm, was also utilized and integrated with the aforementioned MLA models to effectively search for optimal hyperparameters and topological structures. The predictive performance was evaluated using statistical methods, which showed that the extreme gradient boosting (XGBoost) model had the best fitting goodness among the standard algorithms, with an R2 value of 0.9695. Moreover, the Particle Swarm Optimization (PSO) model significantly improved the predictive performance of all Machine Learning Algorithm (MLA) models. The error distribution analysis was then performed. There are fewer large errors in the predicted values of the PSO-XGBoost model. Monte Carlo simulation and probability distribution functions were used to thoroughly analyze the stability of each model after multiple independent runs. The results show that all models have excellent stability. The PSO-XGBoost model performed the best and was subsequently employed to conduct feature analysis to determine the significance of each input parameter and capture the underlying evidence of ASR expansion.

Analysis of the clinicopathological features of tailgut cyst with emphasis on the development of neoplastic lesions.

Tailgut cyst is a rare congenital cyst occurring in the retrorectal space and development of neoplastic lesions in tailgut cyst has been reported. Due to the rarity of the tumor, the histogenesis of neoplastic lesions in tailgut cyst has remained elusive. In the present study, the clinicopathological features of tailgut cyst were analyzed with a particular focus on the development of neoplastic lesions. The clinicopathological features of four patients with tailgut cyst (one female and three males) were retrospectively reviewed. No symptoms were present in two patients. Perineal discomfort, and constipation and urinary retention, were described in the other two patients, respectively. Magnetic resonance imaging showed that the cystic lesions were hypointense on T1- and hyperintense on T2-weigted images in all patients. Histopathological analysis revealed that all lesions were multilocular, and cystic walls were covered by squamous and ciliated epithelia without nuclear atypia. The development of neoplastic lesions was noted in two patients. Dysplastic change composed of piling-up proliferation of glandular cells with mild to moderate nuclear atypia was present in one patient, and invasive adenocarcinoma with a dysplasia component was observed in another patient. Dysplasia of the glandular cells, as seen in two patients in the present series, may be a precursor lesion of invasive adenocarcinoma; therefore, adenocarcinoma arsing in tailgut cyst may show a dysplasia-carcinoma sequence. While the reported incidence of neoplastic lesions in tailgut cysts is ~9% or less, their frequency remains to be accurately determined. Therefore, complete surgical resection is important for the management of patients with tailgut cyst. Additional clinicopathological and molecular studies with large cohorts may be required to clarify the histogenesis of neoplastic lesion in tailgut cyst.

Feature engineering-based analysis of DaTSCAN-SPECT imaging-derived features in the detection of SWEDD and Parkinson's disease

Scans without evidence of dopamine deficit (SWEDD) refer to patients with a normal dopamine transporter scan who have been clinically considered to have Parkinson's disease (PD). To prevent misdiagnosis between SWEDD and PD, it is imperative to identify the manifestations of SWEDD in the early stages. In this experiment, imaging-based striatum binding ratio (SBR) features of four striatum regions are utilized and obtained from the Parkinson's Progression Marker Initiative (PPMI). After that, by implementing feature engineering, six combinational pairs are created by taking two SBR features at a time using a mathematical-based combination (nCr)technique. Then, by evaluating the maximum and minimum values of features of each pair, the six new features are obtained from each combinational pair based on arithmetic operations. Finally, a total of thirty-six new features have been derived by using these existing four SBR features. These derived 36 features are further applied to various supervised and ensemble classifiers of machine learning for three binary classifications (PD vs SWEDD, PD vs healthy, and healthy vs SWEDD). From the consequences, it has been observed that the Naive Bayes classifier performed well across all classification probabilities and among other classifiers. To differentiate SWEDD from PD, the classifier achieved 98.03 % accuracy, 98.85 % recall, 96.29 % precision, 92.96 % Jaccard_score, 96.29 % F1-score, & 99.34 % AUC. Moreover, the comparison has been made between with and without implementing the feature engineering technique for all three classification probabilities for better interpretation. Thus, it is reasonable to hypothesize that the suggested method, which is based on feature engineering, may not only improve performance but also help medical professionals diagnose diseases at early stages.

Re-Thinking the Principles of (Vocabulary) Learning and Their Applications

Making vocabulary stick in your memory involves dedicating attention to what needs to be learned. There are three main factors involved (focus, quantity, and quality) which can be expressed as six principles (focus, accuracy, repetition, time-on-task, elaboration, and analysis). When we include motivation in this description, then there are two more principles (motivation and self-efficacy). These principles apply to both incidental and deliberate learning, and apply to a wide range of learning focuses beyond vocabulary. These principles are well supported by research evidence. We can use the principles for re-examining teaching and learning, Technique Feature Analysis, understanding research, developing autonomy in learning, guiding curriculum design, and determining future research needs. The factors and principles provide a simple and clear view of what is needed for learning to occur from the viewpoint of attention.

Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions.

The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use. The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs. MDDs were derived based on the previous research involving 15 realizations of nodule models at two different sizes. Our study involved simulations of two consecutive acquisitions using 297 different imaging conditions, representing variations in scanners' reconstruction kernels, dose levels, and slice thicknesses. Parametric polynomial models were developed to establish correlations between imaging system characteristics, lesion size, and MDDs. Additionally, polynomial models were used to model the correlation of the imaging system parameters. Optimization problems were formulated for each MRF to minimize the approximated function. Feature importance was determined for each MRF through permutation feature analysis. The proposed method was compared to the recommended guidelines by the quantitative imaging biomarkers alliance (QIBA). The feature importance analysis showed that lesion size is the most influential parameter to estimate the MDDs in most of the MRFs. Our study revealed that thinner slices and higher doses had a measurable impact on reducing the MDDs. Higher spatial resolution and lower noise magnitude were identified as the most suitable or noninferior acquisition settings. Compared to QIBA, the proposed protocol selection guideline demonstrated a reduced coefficient of variation, with values decreasing from 1.49 to 1.11 for large lesions and from 1.68 to 1.12 for small lesions. The protocol optimization framework provides means to assess and optimize protocols to minimize the MDD to increase the sensitivity of the measurements in lung cancer screening.

The quality of life impacting factors in malnourished patients with gastric cancer.

Malnutrition is prevalent among individuals with gastric cancer and notably decreases their quality of life (QOL). However, the factors impacting QOL are yet to be clearly defined. This study aimed to identify essential factors impacting QOL in malnourished patients suffering from gastric cancer. By using the Patient-Generated Subjective Global Assessment (PG-SGA) to assess the nutritional status (≥4 defined malnutrition) of hospitalized cancer patients, 4,586 gastric cancer patients were ultimately defined as malnourished. Spearman method was used to calculate the relationship between clinical features and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Then, univariate and multivariate logistic regression were used to observe which factors affected QOL, and subgroup analysis was performed in young and old population respectively. In addition, we used univariate and multivariate logistic regression to explore whether and how self-reported frequent symptoms in the last 2 weeks of the PG-SGA score affected QOL. In multivariate logistic regression analysis of clinical features of patients with malnourished gastric cancer, women, stage II, stage IV, WL had an independent correlation with a low global QOL scores. However, BMI, secondary education, higher education, surgery, chemotherapy, HGS had an independent correlation with a high global QOL scores. In multivariate logistic regression analysis of symptoms in self-reported PG-SGA scores in patients with malnourished gastric cancer, having no problem eating had an independent correlation with a high global QOL scores. However, they have no appetite, nausea, vomiting, constipation and pain had an independent correlation with a lower global QOL scores. The p values of the above statistical results are both < 0.05. This study demonstrates that QOL in malnourished patients with gastric cancer is determined by female sex, stage II, stage IV, BMI, secondary and higher education or above, surgery, chemotherapy, WL, and HGS. Patients' self-reported symptoms of nearly 2 weeks, obtained by using PG-SGA, are also further predictive of malnourished gastric cancer patients. Detecting preliminary indicators of low QOL could aid in identifying patients who might benefit from an early referral to palliative care and assisted nursing.

Music as a countermeasure to fatigue: A driving simulator study

Listening to music while driving is a commonly adopted fatigue-coping strategy, yet its effectiveness has been explored in only a limited number of studies. Here, we aim to evaluate its impact on passive task-related driving fatigue, as measured by a set of both objective and subjective indicators.A driving simulator experiment was conducted involving 60 participants, who completed a 50-minute driving task on a monotonous highway scenario. The participants were divided into a control group, which did not listen to any music, and an experimental group, which drove for 20 min without music and then listened to a self-selected music playlist played at the preferred intensity for the remaining 30 min.Results show that music had a positive acute effect on objective measurements of driving fatigue. However, this effect was transient (15–25 min duration), and more prominently observed in subjective measures of fatigue. An exploratory analysis of song features suggests that higher intensity, higher tempo, more danceable, and less instrumental music tend to be more effective. These findings indicate that listening to music can serve as a viable short-term countermeasure to driving fatigue.

Shearlet Transform Applied to a Prostate Cancer Radiomics Analysis on MR Images

For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. A recent application of wavelet theory is in radiomics, an emerging field aiming to improve diagnostic, prognostic and predictive analysis of various cancer types through the analysis of features extracted from medical images. In this paper, for a radiomics study of prostate cancer with magnetic resonance (MR) images, we apply a similar but more sophisticated tool, namely the shearlet transform which, in contrast to the wavelet transform, allows us to examine variations along more orientations. In particular, we conduct a parallel radiomics analysis based on the two different transformations and highlight a better performance (evaluated in terms of statistical measures) in the use of the shearlet transform (in absolute value). The results achieved suggest taking the shearlet transform into consideration for radiomics studies in other contexts.

Semantic features analysis for biomedical lexical answer type prediction using ensemble learning approach

AbstractLexical answer type prediction is integral to biomedical question–answering systems. LAT prediction aims to predict the expected answer’s semantic type of a factoid or list-type biomedical question. It also aids in the answer processing stage of a QA system to assign a high score to the most relevant answers. Although considerable research efforts exist for LAT prediction in diverse domains, it remains a challenging biomedical problem. LAT prediction for the biomedical field is a multi-label classification problem, as one biomedical question might have more than one expected answer type. Achieving high performance on this task is challenging as biomedical questions have limited lexical features. One biomedical question must be assigned multiple labels given these limited lexical features. In this paper, we develop a novel feature set (lexical, noun concepts, verb concepts, protein–protein interactions, and biomedical entities) from these lexical features. Using ensemble learning with bagging, we use the label power set transformation technique to classify multi-label. We evaluate the integrity of our proposed methodology on the publicly available multi-label biomedical questions dataset (MLBioMedLAT) and compare it with twelve state-of-the-art multi-label classification algorithms. Our proposed method attains a micro-F1 score of 77%, outperforming the baseline model by 25.5%.

GateView: A Multi-Omics Platform for Gene Feature Analysis of Virus Receptors within Human Normal Tissues and Tumors.

Viruses are obligate intracellular parasites that rely on cell surface receptor molecules to complete the first step of invading host cells. The experimental method for virus receptor screening is time-consuming, and receptor molecules have been identified for less than half of known viruses. This study collected known human viruses and their receptor molecules. Through bioinformatics analysis, common characteristics of virus receptor molecules (including sequence, expression, mutation, etc.) were obtained to study why these membrane proteins are more likely to become virus receptors. An in-depth analysis of the cataloged virus receptors revealed several noteworthy findings. Compared to other membrane proteins, human virus receptors generally exhibited higher expression levels and lower sequence conservation. These receptors were found in multiple tissues, with certain tissues and cell types displaying significantly higher expression levels. While most receptor molecules showed noticeable age-related variations in expression across different tissues, only a limited number of them exhibited gender-related differences in specific tissues. Interestingly, in contrast to normal tissues, virus receptors showed significant dysregulation in various types of tumors, particularly those associated with dsRNA and retrovirus receptors. Finally, GateView, a multi-omics platform, was established to analyze the gene features of virus receptors in human normal tissues and tumors. Serving as a valuable resource, it enables the exploration of common patterns among virus receptors and the investigation of virus tropism across different tissues, population preferences, virus pathogenicity, and oncolytic virus mechanisms.

EMD-based noise reduction study of steel cored conveyor belt containing slag signal

This paper proposes a noise reduction technique that combines empirical mode decomposition (EMD) and wavelet thresholding to eliminate the noise interference caused by metal slag in the steel cord conveyor belt damage signal. Initially, the original signal undergoes decomposition by EMD into high- and low-frequency intrinsic mode function (IMF) components based on metal slag signal feature analysis. The wavelet threshold de-noising method is then applied to filter out the high-frequency noise while preserving the low-frequency IMF components. The IMF components containing slag noise are discarded, and the filtered signal and the low-frequency IMF components are used for signal reconstruction to obtain an optimal noise reduction effect. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

Comparative analysis of linguacultural features of english and uzbek fairytales (based on the plots of “Tom thumb” and “Nohatpolvon”)

The paper examines linguacultural characteristics of English and Uzbek fairytales. The relevance of the study is determined by great interest of linguistic science to the issues of national and cultural specificity of folk texts, which is caused by peculiarities of historical development, beliefs, traditions and customs of people. The objective of the study is to reveal traditional and cultural characteristics of the English and Uzbek tales by means of their linguacultural analysis which is based on the plots of the similar fairytales in two languages. This paper is divided into two sections. The first section examines linguacultural features of the literary tales in two languages. They are characterised by the variety of lexical and stylistic means and a diversity of characters and plots. The second section gives an overview of differences and similarities of fairytales of two different western and eastern cultures. The results of the investigation can be effectively used for a further profound comparative analysis of sociolinguistic and linguacultural features of English and Uzbek tales.

Podophyllic Aldehyde, a Podophyllotoxin Derivate, Elicits Different Cell Cycle Profiles Depending on the Tumor Cell Line: A Systematic Proteomic Analysis.

When new antitumor therapy drugs are discovered, it is essential to address new target molecules from the point of view of chemical structure and to carry out efficient and systematic evaluation. In the case of natural products and derived compounds, it is of special importance to investigate chemomodulation to further explore antitumoral pharmacological activities. In this work, the compound podophyllic aldehyde, a cyclolignan derived from the chemomodulation of the natural product podophyllotoxin, has been evaluated for its viability, influence on the cell cycle, and effects on intracellular signaling. We used functional proteomics characterization for the evaluation. Compared with the FDA-approved drug etoposide (another podophyllotoxin derivative), we found interesting results regarding the cytotoxicity of podophyllic aldehyde. In addition, we were able to observe the effect of mitotic arrest in the treated cells. The use of podophyllic aldehyde resulted in increased cytotoxicity in solid tumor cell lines, compared to etoposide, and blocked the cycle more successfully than etoposide. High-throughput analysis of the deregulated proteins revealed a selective antimitotic mechanism of action of podophyllic aldehyde in the HT-29 cell line, in contrast with other solid and hematological tumor lines. Also, the apoptotic profile of podophyllic aldehyde was deciphered. The cell death mechanism is activated independently of the cell cycle profile. The results of these targeted analyses have also shown a significant response to the signaling of kinases, key proteins involved in signaling cascades for cell proliferation or metastasis. Thanks to this comprehensive analysis of podophyllic aldehyde, remarkable cytotoxic, antimitotic, and other antitumoral features have been discovered that will repurpose this compound for further chemical transformations and antitumoral analysis.