Identification Of Class Research Articles

Sub-clustering based recommendation system for stroke patient: Identification of a specific drug class for a given patient

Stroke is one of the leading causes of death worldwide. Previous studies have explored machine learning techniques for early detection of stroke patients using content-based recommendation systems. However, these models often struggle with timely detection of medications, which can be critical for patient management and decision-making regarding the prescription of new drugs. In this study, we developed a content-based recommendation model using three machine learning algorithms: Gaussian Mixture Model (GMM), Affinity Propagation (AP), and K-Nearest Neighbors (KNN), to aid Healthcare Professionals (HCP) in quickly detecting medications based on the symptoms of a patient with stroke. Our model focused on three classes of drugs: antihypertensive, anticoagulant, and fibrate. Each machine learning algorithm was used to accomplish specific tasks, thereby reducing the partial search space, computational cost, and accurately detecting a primary drug class without loss of precision and accuracy. Our proposed model, called CRGANNC (Clustering Recommendation Gaussian Affinity Nearest Neighbors Classifier), effectively addresses the sparsity and scalability issues faced by content-based recommendation models. The CRGANNC model dynamically partition clusters into sub-clusters with variable numbers based on the group, and can diagnose healthy, sick, and at-risk patients, and recommend drugs to the HCP. In addition to our analysis, we developed a semi-artificial dataset with new features such as weakness, dizziness, headache, nausea, and vomiting, using a pipeline. This dataset serves as a valuable resource for researchers in the sensitive domain of stroke, providing a starting point for building and testing models when real data is often restricted. Our work not only contributes to the development of predictive models for stroke but also establishes a framework for creating similar datasets in other sensitive domains, accelerating research efforts and improving patient care. Our experiments were conducted on our dataset consisting of 9691 patient records, with 1206 records for stroke attacks and 8485 healthy patients. The CRGANNC model achieved an average precision of 0.98, recall of 0.95 and F1-score of 0.96 across all three drugs classes. Furthermore, our model demonstrated significant improvement in computational efficiency compared to existing content-based recommendation models, reducing the processing time by 25.80% . This results indicate the effectiveness of our model in accurately detecting medications for stroke patients based on their symptoms.

Circularly Polarized Luminescence of Thiophene-Bridged Macrocyclic pseudo-meta [2.2]Paracyclophanes.

Chiral organic molecules possessing high quantum yields, circular dichroism, and circularly polarized luminescence values have great potential as optically active materials for future applications. Recently, the identification of a promising class of inherently chiral compounds was reported, namely macrocyclic 1,3-butadiyne-linked pseudo-meta[2.2]paracyclophanes, displaying high circular dichroism and related gabs values albeit modest quantum yields. Increasing the quantum yields in an attempt to get bright circularly polarized light emitters, the high-yielding heterocyclization of those 1,3-butadiyne bridges resulting in macrocyclic 2,5-thienyls-linked pseudo-meta [2.2]paracyclophanes is herein described. The chiroptical properties of both, the previously reported 1,3-butadiyne, and the novel 2,5-thienyl bridged macrocycles of various sizes, are experimentally recorded, and theoretically described using density-functional theory.

Doing style for Saraswati Puja: Girlhood, Class, and Community Identity among Muslim girls in Assam

Saraswati Puja, a celebration of the Hindu goddess of learning, is organised by youth clubs and educational institutions in eastern India. We draw on debates on girlhood, codes of respectable femininity in a neoliberal world, and how these play out for Muslim girls in the school context. These ideas frame our analysis of the dressing up practice among Muslim girls in a government school. We argue that the middle-class and ethnicised ideals of girlhood are amplified and reconfigured by the popular discourses on Saraswati Puja and add to the tensions over the appropriate code of dressing within the context of Puja at school. While the reality of girls’ lives is being shaped in novel ways, the Muslim girls engagement with the ideals of ‘respectable femininity’ through varied modes of ‘doing style’ put them under contrary pressures in public places like school. In such a scenario, their accounts of dressing up and participating in this event serve as a vantage point to understand how girlhood is being construed and experienced from varied positions of class, caste, age, and community. These accounts highlight Muslim girls’ engagement with the codes of femininity in the majoritarian cultural universe of a school.

Pengaruh Kombinasi Ekstrak Etanol Daun Beluntas (Pluchea indica Less.) dan Daun Sukun (Artocarpus altilis (Park.) Fosberg) terhadap Bakteri Staphylococcus aureus ATCC 25923

Indonesians are frequently afflicted with infectious diseases caused by the Staphylococcus aureus bacteria. Beluntas and breadfruit leaves contain flavonoids, saponins, alkaloids, and tannins which have potential as antibacterials. This research seeks to the impact of mixing ethanol extracts of breadfruit and beluntas leaves on S. aureus ATCC 25923. Powder were macerated using 96% ethanol followed by identification of the compound class. Single extracts were examined for dilution to acquire effective concentrations before being mixed with concentration ratios of 1:1, 1:2, and 2:1. The extracts combination was tested using the disc diffusion method, and the combination effect was tested using the paper tape method. The results demonstrated that the beluntas and breadfruit leaf extracts had MBC value of 12.5% and 6.25%. The most effective extract combination was at a concentration ratio of 2:1, with an average diameter of the inhibition zone of 20.72 mm, while the ratios of 1:1 and 1:2 were only 17.78 and 18.15 mm, respectively. The combination effect of extracts is synergistic

Comprehensive genomic exploration of class III peroxidase genes in guava unravels physiology, evolution, and postharvest storage responses

Peroxidases (PRXs) play multifaceted roles in plant growth, development, and stress responses. Here, we present a comprehensive analysis of the PRX gene family in guava, a globally significant fruit. In the guava genome, we identified 37 PRX genes, a number lower than that of Arabidopsis, suggesting a distinctive gene family expansion pattern. Phylogenetic analysis unveiled close relationships with Arabidopsis PRXs, with 12 PgPRX genes forming ortholog pairs, indicating a specific expansion pattern. Predictions placed most PRX proteins in the chloroplast and extracellular regions. Structural analysis of PgPRX proteins revealed commonalities in domain structures and motif organization. Synteny analysis underscored the dynamic role of segmental duplication in the evolution of guava's PRX genes. We explored the dynamic expression of PgPRX genes across guava tissues, exposing functional diversity. Furthermore, we examined changes in peroxidase levels and gene expressions during postharvest fruit storage, providing insights for preserving fruit quality. This study offers an initial genome-wide identification and characterization of Class III peroxidases in guava, laying the foundation for future functional analyses.

Gli scavi di Ocriculum nella cultura neoclassica e antiquaria

The remains of the ancient Roman town, crossed by the Flaminia road and lapped by a bend of the Tiber, are located in a natural landscape of significant beauty, perfect synthesis of archaeology and nature that remained unchanged throughout centuries. Excavations were conducted here from a very early period, especially from 1776 to 1784, when a great quantity of material was removed. The archaeological excavations carried out in Otricoli in the second half of the Seventeenth century together with discoveries in Herculaneum, Pompeii, Stabiae and other cities of ancient Italy, contributed during the Neoclassical period to the rediscovery of classical ideals in art and architecture, partly already rediscovered during Humanism and the Renaissance in Italy.

“We won't go back home!” Women's Experiences with Deindustrialization and Unemployment at Fiat and LIP, a Comparative Perspective

AbstractThis article stems from a project aiming to investigate women's unemployment in the phase of deindustrialization that affected Western European countries from the aftermath of the 1973 oil crisis. Countries such as Italy and France, with both a strong working-class movement and a vibrant feminist movement, have had to face economic crises since the mid-seventies and from the eighties have witnessed how neoliberal capitalism started to heavily reshape the global labor market. The old stereotype of female salary as ‘pin money’ within the household budget was again publicly put forth. How did women experience unemployment? What did it mean in terms of their social status, economic independence, sense of self, relationship to the home? To answer these questions and to understand the reconfiguration of class and gender identities, I focus on two milestone cases of labour struggles that are recognized as turning points in the history of the affirmation of neoliberal dynamics: the crisis of FIAT in Italy and of LIP in France. Despite their being at the center of many academic investigations as fundamental sites of resistance, their outcomes in terms of unemployment and particularly the gender dimension of this phenomenon have been largely overlooked so far. I will delineate a comparison between the two cases by drawing on my past research about trade union feminism in the two countries, on archival sources, published accounts and oral histories of two key activists in these struggles. Key factors that will be analysed are: women's participation in the collective mobilisations in the face of unemployment, their relation to the domestic sphere and to care work, their ability to build female networks within their wounded communities.

Method development for the quantification of nine nitazene analogs and brorphine in Dried Blood Spots utilizing liquid chromatography – tandem mass spectrometry

The detection of nitazenes in biological fluids is increasingly needed as they are repeatedly reported in intoxication and overdose cases. A simple method for the quantification of low levels of nine nitazene analogs and brorphine in Dried Blood Spots (DBS) was developed and validated. 10 μL of spiked whole blood is deposited on a Capitainer®B card and allowed to dry. The spot is punched out, and extracted with 500 μL methanol:acetonitrile (3:1 v/v) added with 1.5 μL of fentanyl-D5 as the internal standard. After stirring, sonication, and centrifugation of the vial, the solvent is dried under nitrogen, the extract is reconstituted in 30 μL methanol, and 1 μL is injected into a UHPLC-MS/MS instrument. The method validation showed linear calibration in the 1–50 ng/mL range, LOD values ranging between 0.3 ng/mL (isotonitazene) and 0.5 ng/mL (brorphine), average CV% and bias% within 15 % and 10 % for all compounds, respectively. The matrix effect due to blood and filter paper components was within 85–115 % while recovery was between 15–20 %. Stability tests against time and temperature showed no significant variations for storage periods up to 28 days. Room temperature proved to represent the best samples storage conditions. UHPLC-MS/MS proved capable to reliably identify all target analytes at low concentration even in small specimen volumes, as those obtained from DBS cards, which in turn confirmed to be effective and sustainable micro-sampling devices. This procedure improves the efficiency of toxicological testing and provides an innovative approach for the identification of the nitazene class of illicit compounds.

Genome-wide identification of class III peroxidases in colored calla lily and enhanced resistance to soft rot bacteria

Soft rot disease is one of the primary issues in the production of colored calla lilies, significantly limiting large-scale production. This research conducts an in-depth investigation of the class III peroxidase gene family in colored calla lily, identifying 80 ZePER genes, with their motifs and structural domains remaining conserved. Through phylogenetic tree analysis, these genes were classified into 9 distinct clades. Collinearity analysis and the distribution of ZePER genes suggest that the two main events of ZePER gene family evolution was whole-genome duplication (WGD) as well as tandem duplication. Cis-acting element analysis showed that the ZePER genes could be directly modulated by essential hormones like jasmonic acid (JA) and salicylic acid (SA), highlighting their role in biotic stress responses. Transcriptome profiling revealed tissue-specific gene expressions and their essential function in responding to soft rot bacteria. Upon inoculation with Pectobacterium carotovorum, the leaves manifested a significant upregulation of ZePER genes expression, and there was a concurrent surge in JA and SA content. Treatments with methyl jasmonate (MeJA) and benzothiadiazole (BTH) induced ZePER gene expression and augmented the plant's disease resistance. This indicates the crucial role of ZePER genes in regulating reactive oxygen species in leaves. In summary, these findings emphasize the importance of ZePER genes in the defense mechanisms of colored calla lily.

Heterogeneity, Differentiation Mechanisms and Social Effects of Urban Residential Space in China’s Large Cities: A Case Study of Wuhan

Different choices of living space for urban residents are concrete reflections of a social class divide. Emerging residential space differentiation is a critical issue in Chinese cities. This paper aims to explore the current situations and mechanisms of residential spatial differentiation, and reveal its social responses in Chinese cities. Taking Wuhan, the largest city in central China, as an example, this research divides the residential space based on social resource structures. It analyzes and compares spatial differentiation and influencing factors of different residential areas and houses with different prices in Wuhan by spatial differentiation indicators and geographic detector. The results show that residential areas are divergent due to differences in urban resource spatial structure, with few and concentrated high-quality residential areas. The spatial differentiation level of different residential areas also varies, with central location, landscape, educational resources, and other dominant scarce resources being the key to residential spatial differentiation in most of China’s big cities. In addition, residential spatial differentiation can lead to issues including housing wealth inequality, resource deprivation, and class identity solidification. In the end, this paper puts forward policy implications on alleviating residential spatial differentiation and optimizing allocation of residential resources.

Detection of violence incitation expressions in Urdu tweets using convolutional neural network

The popularity and widespread use of social media are constantly generating unmonitored data, spreading unwanted content such as hate speech and expressions that incite violence. Automatic detection of violence incitation is a challenging task and to the best of our knowledge, Urdu language has been completely neglected. Therefore, a robust framework is proposed for identifying expressions exhibiting violence incitation in Urdu tweets. The potentials of the semantic, word embeddings, and language models are explored to learn contextualized representations of the violence incitation in Urdu tweets. In addition, the strength of the 1-Dimensional Convolutional Neural Network (1D-CNN) is exploited by tunning its parameters on the newly proposed annotated Urdu corpus. The annotated dataset consists of 4808 tweets manually collected from Pakistani Twitter accounts. The performance of 1D-CNN with word uni-gram, Urdu Bidirectional Encoder Representations from Transformer (Urdu-BERT), and Urdu- Robustly Optimized BERT Approach (Urdu-RoBERTa) models is compared to fine-tuned Urdu-RoBERTa, Bidirectional Long short-term memory (BiLSTM), Convolutional BiLSTM (CBi-LSTM), and six state-of-the-art Machine Learning (ML) models. The results reveal that the 1D-CNN with word uni-gram model shows benchmark performance by demonstrating 89.84% accuracy and 89.80% macro f1-score. Furthermore, it outperforms all comparable models and achieves 89.76% f1-score for the violence class, and 89.84% f1-score for not-violence class identification. The uniqueness of the proposed model is evaluated using MARS shine-through and MARS occlusion metrics and the CNN model outperformed the others. The MARS metrics facilitate evaluation and visualization of the classifier performance in terms of capturing unique true positive samples that are not predicted by other models. The findings of the proposed framework are very supportive for further investigation in this domain.

Black Feminist Theory as an Approach to Therapeutic Growth and Healing

Black American women are uniquely positioned to experience systemic racism and oppression due to their intersecting identities of gender, race, sexuality, and class. Black feminist therapy (BFT) provides empirically proven strategies to promote the psychological health and well-being of Black women and foster their empowerment, liberation, and radical healing. Through an examination of the principles and concepts of feminist theory, Black feminist theory, and BFT, a model of growth and healing is presented. A case example is provided as evidence of the process and effectiveness of BFT in working with Black women.

Navigating stigmatized motherhood: self-stigma of single mothers with mental illness in Thailand

ABSTRACT Drawing on participant observations and in-depth interviews with twenty-two single mothers with mental illness and four medical workers from a psychiatric hospital in Thailand, this article analyses the self-stigma faced by single mothers with mental illness in Thai society. Using an intersectional approach and looking at motherhood as a social construct, the article shows the connections of intersectional stigma with single motherhood and mental illness, and demonstrates its relation to gender, class, ethnicity, religion and other identity markers within the Thai socio-cultural context. Findings from the research suggest that the current gender ideology, which shapes the stigmatized mothering experiences of single mothers with mental illness in Thailand, needs to be revisited and reformed by individuals, medical workers and the state at every level.

Boyhood masculinities at play: Basketball, learning, and the pedagogies of the court

While there is an abundance of interdisciplinary research on boyhood, education, and sport respectively, especially as relating to males of color in the United States, the extant education scholarship has a dearth of auto-ethnographic work that examines these areas. Hence, this article examines how sport, and specifically, basketball, links to the development of masculinities, identity, and critical pedagogies within our own “education.” Further, considering the various tensions around the schooling of U.S. youth from minoritized backgrounds, we leverage our backgrounds as Chicanx and African American scholars to recollect and analyze our journeys and links to our boyhoods and sense-making in formal and informal spaces of play. Our findings entail a push for a re-examination of how sports and critical identities of race and class may work in dialectical and transformative ways, even amidst various contradictions of boyhood. Rather than perpetuating a view of basketball cultures as solely reproducers of harmful masculinities, this paper imagines the pedagogical and liberatory aspects of the game. We conclude with implications for research around gender identities within formal and informal play.

Identification of Different Classes of VOCs Based on Optical Emission Spectra Using a Dielectric Barrier Helium Plasma Coupled with a Mini Spectrometer.

In this study, a micro helium dielectric barrier discharge (μHDBD) plasma device fabricated using 3D printing and molding techniques was coupled with a mini spectrometer to detect and identify different classes of volatile organic compounds (VOCs) using optical emission spectrometry (OES). We tested 11 VOCs belonging to three different classes (straight-chain alkanes, aromatics, and polar organic compounds). Our results clearly demonstrate that the optical emission spectra of different classes of VOCs show clear differences, and therefore, can be used for identification. Additionally, the emission spectra of VOCs with a similar structure (such as n-pentane, n-hexane, n-heptane, n-octane, and n-nonane) have similar optical emission spectrum shape. Acetone and ethanol also have similar emission wavelengths, but they show different line intensities for the same concentrations. We also found that the side-chain group of aromatics will also affect the emission spectra even though they have a similar structure (all have a benzene ring). Moreover, our μHDBD-OES system can also identify multiple compounds in VOC mixtures. Our work also demonstrates the possibility of identifying different classes of VOCs by the OES shape.

Hierarchical-Instance Contrastive Learning for Minority Detection on Imbalanced Medical Datasets.

Deep learning methods are often hampered by issues such as data imbalance and data-hungry. In medical imaging, malignant or rare diseases are frequently of minority classes in the dataset, featured by diversified distribution. Besides that, insufficient labels and unseen cases also present conundrums for training on the minority classes. To confront the stated problems, we propose a novel Hierarchical-instance Contrastive Learning (HCLe) method for minority detection by only involving data from the majority class in the training stage. To tackle inconsistent intra-class distribution in majority classes, our method introduces two branches, where the first branch employs an auto-encoder network augmented with three constraint functions to effectively extract image-level features, and the second branch designs a novel contrastive learning network by taking into account the consistency of features among hierarchical samples from majority classes. The proposed method is further refined with a diverse mini-batch strategy, enabling the identification of minority classes under multiple conditions. Extensive experiments have been conducted to evaluate the proposed method on three datasets of different diseases and modalities. The experimental results show that the proposed method outperforms the state-of-the-art methods.

Identification of PV Fault Classes Using Intelligent Method KNN (K-Nearest Neighbours)

Throughout many developing nations of our humble planet, renewable energy is a hot topic. Every country at this very moment is trying to move away from fossil fuels like petrol to complete renewable energy sources especially Photovoltaic systems. The reliability and efficiency of renewable energy systems is now a frequent topic of discussion. Like all systems of production, renewable energy systems are subject to failures and defects in their normal operating functions with regards to the amount of power output. These systems break down and deteriorate during the period of their operation. This is why a system of diagnostic is required whose many objectives is to provide indicators with the given valuables like temperature, solar irradiation, voltage and current output to detect the faults and thus maintain the energy production at optimum. The work in progress relates to the diagnostic of faults in the PV systems using artificial intelligent methods particularly the K-nearest Neighbour algorithm.

Differentiable multi-ridge regression for system identification

Regularization aims to shrink model parameters, reducing complexity and overfitting risk. Traditional methods like LASSO and Ridge regression, limited by a single regularization hyperparameter, can restrict bias-variance trade-off adaptability. This paper addresses system identification in a multi-ridge regression framework, where an l2-penalty on the model coefficients is introduced, and a different regularization hyperparameter is assigned to each model parameter. To compute the optimal hyperparameters, a cross-validation-based criterion is optimized through gradient descent. Autoregressive and Output Error models are considered. The former requires formulating a regularized least-squares problem. The identification of the latter class is more challenging and is addressed by adopting regularized instrumental variable methods to ensure a consistent parameter estimation.

Imaging Assays to Detect DNA Damage in Trypanosome Parasites Using γH2A.

Diseases caused by trypanosomatid parasites remain a significant unmet medical need for millions of people globally. Trypanosomatid parasites such as Trypanosoma cruzi and subspecies of Trypanosoma brucei cause Chagas disease and human African trypanosomiasis (HAT), respectively. Although efforts to find novel treatments have been successful for HAT, Chagas disease is still treated with decades-old therapies that suffer from long treatment durations and severe safety concerns. We recently described the identification and characterization of the cyanotriazole compound class that kills trypanosomes, in vitro and in vivo, by selective inhibition of the trypanosome nuclear topoisomerase II enzyme. To evaluate whether inhibition of the topoisomerase II enzyme led to parasite death due to lethal double-strand DNA breaks, we developed assays for detecting DNA damage in both intracellular amastigotes of T. cruzi and bloodstream-form T. brucei by using the canonical DNA damage marker γH2A. Herein, this article describes the protocols for detecting DNA damage using an immunofluorescence assessment of γH2A by microscopy in trypanosome parasites. Key features • Immunofluorescence-based assay to detect the γH2A response in T. brucei and T. cruzi parasites. • Robust DNA damage pathway-based cellular assays to evaluate topoisomerase II poisons' ability to cause DNA damage. • A 384-well plate-based T. cruzi protocol allows high-resolution and high-throughput evaluation of compounds that cause DNA damage by measuring γH2A in intracellular parasites. • This assay could be modifiable for evaluation of DNA damage responses in various intracellular and extracellular eukaryotic pathogens.

ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery.

Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.