Essential Component Research Articles

Description-Enhanced Label Embedding Contrastive Learning for Text Classification.

Text classification is one of the fundamental tasks in natural language processing, which requires an agent to determine the most appropriate category for input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially pretrained language models (PLMs). Usually, these methods concentrate on input sentences and corresponding semantic embedding generation. However, for another essential component: labels, most existing works either treat them as meaningless one-hot vectors or use vanilla embedding methods to learn label representations along with model training, underestimating the semantic information and guidance that these labels reveal. To alleviate this problem and better exploit label information, in this article, we employ self-supervised learning (SSL) in model learning process and design a novel self-supervised relation of relation ( [Formula: see text]) classification task for label utilization from a one-hot manner perspective. Then, we propose a novel relation of relation learning network( [Formula: see text]-Net) for text classification, in which text classification and [Formula: see text] classification are treated as optimization targets. Meanwhile, triplet loss is employed to enhance the analysis of differences and connections among labels. Moreover, considering that one-hot usage is still short of exploiting label information, we incorporate external knowledge from WordNet to obtain multiaspect descriptions for label semantic learning and extend [Formula: see text]-Net to a novel description-enhanced label embedding network(DELE) from a label embedding perspective. One step further, since these fine-grained descriptions may introduce unexpected noise, we develop a mutual interaction module to select appropriate parts from input sentences and labels simultaneously based on contrastive learning (CL) for noise mitigation. Extensive experiments on different text classification tasks reveal that [Formula: see text]-Net can effectively improve the classification performance and DELE can make better use of label information and further improve the performance. As a byproduct, we have released the codes to facilitate other research.

SESELI LIBANOTIS (L.) W.D.J. KOCH (APIACEAE): CHEMICAL COMPOSITION OF ESSENTIAL OILS, THEIR ANTIBACTERIAL AND CYTOTOXIC ACTIVITIES

Introduction. Infectious diseases and cancer remain among the leading causes of death worldwide. In this regard, the search for biologically active molecules from among synthetic and natural compounds for the purpose of creating medicines remains an urgent task of modern science. Plants that have impressive natural and biotechnological resource potential, taking into account modern advances in the field of ex situ, in situ and in vitro cultiva-tion, remain a promising source of native substances. One of the promising sources of biologically active substances is a perennial plant, the Seseli libanotis (L.) W.D.J. Koch, Apiaceae, growing in central, eastern Europe and Asia in dry pastures and forest edges. The main components of the genus Seseli L. are furo- and pyranocoumarins, essential oils and polyacetylene compounds, which exhibit antibacterial, antifungal, insecticidal, diuretic, antiviral, anti-inflammatory, and antitumor properties. The aim – study of the chemical composition, antibacterial and cytotoxic activities of S. libanotis roots essential oil. Material and methods. The roots were collected in places of natural growth in September 2022, during the fruiting period (Irkutsk region, Maloye Goloustnoye village). The voucher specimen is stored in the herbarium of the Institute of Ecological Ecology SB RAS (UUH 020438). Obtaining essential oils. Essential oils were obtained by hydrodistillation. The qualitative composition and quantitative content of essential oil compo-nents were studied by GC/MS. The study of the antibacterial activity of essential oils was carried out in vitro on test cultures of gram-positive and gram-negative bacteria (Staphylococcus aureus, Streptococcus pyogenes, Escherichia coli, Pseudomona saeruginosa, Salmonella enterica) using the disc diffusion method. Cell viability assay was performed on HeLa cell lines using trypan blue. Results. The composition and quantitative content of the components of essential oils of the roots of S. libanotis of the Baikal flora were studied for the first time. 28 components of predominantly monoterpene nature have been identified, the main ones being limonene (57,60%), α-pinene (6,88%), cis-β-ocimene (6,75%). Also found were fatty acids, polyacetylene-falcorinol and 7-isopen-tinyloxycoumarin. Essential oils of S libanotis have antibacterial activity against gram-positive bacteria Staphylococcus aureus, Streptococcus pyogenes and gram-negative bacteria Escherichia coli. Cultivation of Hela with S. libanotis essential oils resulted in decreased viability in the concentration range of 0.04–2.56 μL/mL for 72 hours. Conclusions. Essential oils of roots of S. libanotis, with high yield, containing falcarinol and coumarin, have antibacterial and cytotoxic activity.

Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review

High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.

(Ag0.80Bi0.04Sr0.04)(Nb1-xTax)O3 ceramics with enhanced energy storage for high-temperature application via synergic optimization

Dielectric capacitors with high power density are essential components in advanced electronic and micro-electronic products. Nevertheless, the limited energy storage density and efficiency, coupled with poor performance at elevated temperatures, severely restrict their usefulness. In this work, Ta was introduced to the (Ag0.80Bi0.04Sr0.04)NbO3 ceramic prepared via the hydrothermal-synthesized powders to improve the room- and high-temperature energy storage performance. A large breakdown strength of 729kV/cm, a high recoverable energy storage density of 8.5J/cm3 and a benign energy efficiency of 75.6% were simultaneously achieved in (Ag0.80Bi0.04Sr0.04)(Nb0.85Ta0.15)O3 ceramics. Moreover, the larger energy storage efficiency (83.5%) at elevated temperatures than room temperature, the benign frequency and cycle stability foreshow great potential in realizing the application of capacitors at high-temperatures.

The Metaverse for Cognitive Health: A Paradigm Shift

The Metaverse, frequently dubbed as ”the successor of mobile internet”, is a paradigm which aims to build an immersive, spatiotemporal and self-sustained virtual, but realistic world for all age groups. The Metaverse is stepping from science fiction to reality due to the advances in technologies like Extended Reality, Virtual Reality, Augmented Reality, Artificial Intelligence, the Internet of Everything, eXtended meta-uni-omni-Verse and Digital Twins. Consumer electronics such as wearables, television, and smartphones are essential components of the Metaverse that can be used to accurately collect and measure data concerning user interaction and thereby create an immersive environment. This article discusses the state of art literature about how the Metaverse can be used for improving cognitive health, various scenarios where the Metaverse could help doctors to better treat cognitive patients, and finally, the future research directions that can realize the potential of the Metaverse for cognitive health.

Codon Usage Characteristics and Evolutionary Analysis of Mitochondrial Genome of Winter Squash (Cucurbita maxima Duch.)

Mitochondrial genomes are essential components of eukaryotic cells, understanding their structural characteristics and codon usage bias is critical. Winter squash (Cucurbita maxima Duch ex Poiret) is an economically important cucurbit crop. To clarify its structural characteristics and codon usage bias of mitochondrial genome, as well as the evolutionary characteristics in the Cucurbitaceae family, whole genome sequencing analysis of C. maxima mitochondrial genome was carried out. The results revealed that the total length of the C. maxima mitochondrial genome was 640,814 bp, containing 9,162,318,300 bp of bases. There were 68 annotated genes identified. The mitochondrial coding genes of C. maxima exhibited an average GCall content of 42.57%, with the last position enriched in A/T bases. Codon usage analysis indicated weak codon bias, with the effective codon number (ENC) ranging from 44.22 to 61.00. Correlation analysis and neutrality plot analysis demonstrated a nonsignificant correlation between GC3 and GC12. PR2-plot and ENC-plot analysis further supported the influence of natural selection on the codon preference in the C. maxima mitochondrial genome. Relative synonymous codon usage (RSCU) analysis identified 19 optimal codons, with a preference for U or A as the final base. The analysis of scattered repeat sequences in the genome indicated a high frequency of forward and palindromic repeats in the C. maxima mitochondrial genome. Nucleic acid diversity analysis highlighted rpl5 as the gene with the highest variability. Comparative analysis of mitochondrial structure and phylogenetic tree analysis revealed a close evolutionary relationship between C. maxima and Cucurbita pepo, showing synteny in evolution. Homologous fragment analysis demonstrated a similarity of over 70% between the C. maxima mitochondrial genome and chloroplast genome fragments. These findings provide a foundation for C. maxima identification and phylogenetic studies.

The Influence Of Corn Flour Substitution On The Quality Of Dry Noodles

The use of corn flour contains various essential components such as a source of plant-based vitamin A, also known as beta-carotene content, essential fatty acids, protein, carbohydrates, and high dietary fiber. However, the utilization of local corn flour is not maximized in the food industry, as it is mainly focused on supplying animal feed, even though corn is one of Indonesia's staple commodities in supporting the national food security program. The use of corn flour can replace synthetic coloring agents with a natural yellow color obtained from the use of local corn flour derived from the beta-carotene pigment contained in corn. This research aims to analyze the influence of substituting corn flour by 0% (X0), 20% (X1), and 40% (X2) on the quality of dry noodles in terms of color, aroma, texture, and taste. The research design used was a pure experiment with a Completely Randomized Design (CRD) method. The data used were obtained directly from three limited panelists using the organoleptic evaluation format for the quality of dry noodles with corn flour substitution. The collected data were then tabulated in table format, and an Analysis of Variance (ANOVA) was conducted. If the analysis of variance shows Fcount > Ftable, then Duncan's test was carried out. The results of the research indicate a significant influence of corn flour substitution at 0% (X0), 20% (X1), and 40% (X2) on the quality of color (pale yellow), and texture (chewy). The best result was observed at the 20% substitution level (X1) with a 20% substitution of corn flour from the total amount of wheat flour used.

A novel method for indian vehicle registration number plate detection and recognition using CNN

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard.

Syntactic analysis of complex sentences containing Arabic psychological verbs

Complex Arabic sentences, especially those containing Arabic psychological verbs, follow a common underlying structure characterized by two essential components: the predicate and the subject. In addition, there are two optional elements: the head and the complement. These sentences, rooted in basic noun phrases (NPs), can be expanded within the predicate, subject, or complement, resulting in compound structures. This study aims to develop a syntactic analyzer for parsing complex sentences containing Arabic psychological verbs. To achieve this, we will use the dictionary generated from the lexicon-grammar table of Arabic psychological verbs, which contains all lexical, syntactic, semantic, and transformational information related to these verbs. Then, we will extend an existing analyzer to recognize and label all grammatical structures within complex sentences containing Arabic psychological verbs. Finally, we will evaluate the efficiency of this analyzer through tests on different texts and corpora.

A Novel Tensor Learning Model for Joint Relational Triplet Extraction.

The relational triplet is a format to represent relational facts in the real world, which consists of two entities and a semantic relation between these two entities. Since the relational triplet is the essential component in a knowledge graph (KG), extracting relational triplets from unstructured texts is vital for KG construction and has attached increasing research interest in recent years. In this work, we find that relation correlation is common in real life and could be beneficial for the relational triplet extraction task. However, existing relational triplet extraction works neglect to explore the relation correlation that bottlenecks the model performance. Therefore, to better explore and take advantage of the correlation among semantic relations, we innovatively utilize a three-dimension word relation tensor to describe relations between words in a sentence. Then, we treat the relation extraction task as a tensor learning problem and propose an end-to-end tensor learning model based on Tucker decomposition. Compared with directly capturing correlation among relations in a sentence, learning the correlation of elements in a three-dimension word relation tensor is more feasible and could be addressed through tensor learning methods. To verify the effectiveness of the proposed model, extensive experiments are also conducted on two widely used benchmark datasets, that is, NYT and WebNLG. Results show that our model outperforms the state-of-the-art by a large margin of F1 scores, such as the developed model has an improvement of 3.2% on the NYT dataset compared to the state-of-the-art. Source codes and data can be found at https://github.com/Sirius11311/TLRel.git.

IoT driven joint compressed sensing and shallow learning approach for ECG signal-reconstruction

Because biological signal transmission in real time might be very demanding, cloud and internet of things (IoT) infrastructure are required. To do this, the main component of the signal serves as the focal point of a reconstruction strategy that has been developed. The input is transferred to the intended destination once it has been encoded. Security is an important consideration that must not be disregarded. For long-term healthcare monitoring via lightweight wireless networks, electrocardiogram (ECG) compression is a major difficulty. Reducing energy consumption in wireless data transmission and precisely calculating error rates for data reconstruction are two essential components of compressed sensing. The application of effective encoding methods is crucial for these considerations. We present multi-task compressed sensing (MT-CS), a unique method for compressing ECG data. When used to wireless network systems with several embedded sensors, this technique is quite effective. From the ECG data, the model learns the fundamental adaptive properties needed for correlation. We use the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset to investigate the performance of the suggested MT-CS reconstruction technique in order to assess its strength and application. In comparison to current compressed sensing methods, the simulation results demonstrate that the suggested reconstruction methodology utilizing MT-CS generates high-quality reconstruction signals with fewer observations.

Chemical management in electronics manufacturing: Protecting worker health and the environment

In the realm of electronics manufacturing, the management of chemicals is paramount to safeguarding both worker health and environmental sustainability. This review delves into the strategies and challenges associated with chemical management within this industry. The utilization of various chemicals in electronics manufacturing processes presents potential hazards to both workers and the environment. From cleaning agents to solvents and fluxes, these substances pose risks ranging from acute toxicity to long-term health effects and environmental contamination. Effective chemical management strategies are therefore indispensable. This review discusses the proactive measures implemented by electronics manufacturers to mitigate these risks. It explores the adoption of alternative, less hazardous chemicals and the implementation of engineering controls to minimize exposure. Additionally, stringent protocols for handling, storage, and disposal are essential components of comprehensive chemical management programs. Furthermore, regulatory frameworks play a pivotal role in shaping chemical management practices within the electronics manufacturing sector. Compliance with local and international regulations such as REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals) and RoHS (Restriction of Hazardous Substances) is imperative to ensure the safety of workers and the environment. However, despite these efforts, challenges persist. Balancing the need for innovation and productivity with chemical safety requirements remains a significant challenge for manufacturers. Additionally, global supply chain complexities add another layer of complexity to chemical management efforts. Effective chemical management in electronics manufacturing demands a multi-faceted approach encompassing technological innovation, regulatory compliance, and a commitment to worker health and environmental stewardship. By addressing these challenges collaboratively, the industry can strive towards safer and more sustainable practices.

Functional Analyses of Four Cryptochromes From Aquatic Organisms After Heterologous Expression in Drosophila melanogaster Circadian Clock Cells.

Cryptochromes (Crys) represent a multi-facetted class of proteins closely associated with circadian clocks. They have been shown to function as photoreceptors but also to fulfill light-independent roles as transcriptional repressors within the negative feedback loop of the circadian clock. In addition, there is evidence for Crys being involved in light-dependent magneto-sensing, and regulation of neuronal activity in insects, adding to the functional diversity of this cryptic protein class. In mammals, Crys are essential components of the circadian clock, but their role in other vertebrates is less clear. In invertebrates, Crys can function as circadian photoreceptors, or as components of the circadian clock, while in some species, both light-receptive and clock factor roles coexist. In the current study, we investigate the function of Cry proteins in zebrafish (Danio rerio), a freshwater teleost expressing 6 cry genes. Zebrafish peripheral circadian clocks are intrinsically light-sensitive, suggesting the involvement of Cry in light-resetting. Echinoderms (Strongylocentrotus purpuratus) represent the only class of deuterostomes that possess an orthologue (SpuCry) of the light-sensitive Drosophila melanogaster Cry, which is an important component of the light-resetting pathway, but also works as transcriptional repressor in peripheral clocks of fruit flies. We therefore investigated the potential of different zebrafish cry genes and SpuCry to replace the light-resetting and repressor functions of Drosophila Cry by expressing them in fruit flies lacking endogenous cry function. Using various behavioral and molecular approaches, we show that most Cry proteins analyzed are able to fulfill circadian repressor functions in flies, except for one of the zebrafish Crys, encoded by cry4a. Cry4a also shows a tendency to support light-dependent Cry functions, indicating that it might act in the light-input pathway of zebrafish.

Remarkable enhancement of cinnamaldehyde antimicrobial activity encapsulated in capped mesoporous nanoparticles: A new “nanokiller” approach in the era of antimicrobial resistance

Combating antimicrobial resistance is one of the biggest health challenges because of the ineffectiveness of standard biocide treatments. This challenge could be approached using natural products, which have demonstrated powerful therapeutics against multidrug-resistant microbes. In the present work, a nanodevice consisting of mesoporous silica nanoparticles loaded with an essential oil component (cinnamaldehyde) and functionalized with the polypeptide ε-poly-l-lysine is developed and used as an antimicrobial agent. In the presence of the corresponding stimuli (i.e., exogenous proteolytic enzymes from bacteria or fungi), the polypeptide is hydrolyzed, and the cinnamaldehyde delivery is enhanced. The nanodevice's release mechanism and efficacy are evaluated in vitro against the pathogenic microorganisms Escherichia coli, Staphylococcus aureus, and Candida albicans. The results demonstrate that the new device increases the delivery of the cinnamaldehyde via a biocontrolled uncapping mechanism triggered by proteolytic enzymes. Moreover, the nanodevice notably improves the antimicrobial efficacy of cinnamaldehyde when compared to the free compound, ca. 52-fold for E. coli, ca. 60-fold for S. aureus, and ca. 7-fold for C. albicans. The enhancement of the antimicrobial activity of the essential oil component is attributed to the decrease of its volatility due to its encapsulation in the porous silica matrix and the increase of its local concentration when released due to the presence of microorganisms.

Differential carbonic anhydrase activities control EBV-induced B-cell transformation and lytic cycle reactivation.

Epstein-Barr virus (EBV) contributes to ~1% of all human cancers including several B-cell neoplasms. A characteristic feature of EBV life cycle is its ability to transform metabolically quiescent B-lymphocytes into hyperproliferating B-cell blasts with the establishment of viral latency, while intermittent lytic cycle induction is necessary for the production of progeny virus. Our RNA-Seq analyses of both latently infected naïve B-lymphocytes and transformed B-lymphocytes upon lytic cycle replication indicate a contrasting expression pattern of a membrane-associated carbonic anhydrase isoform CA9, an essential component for maintaining cell acid-base homeostasis. We show that while CA9 expression is transcriptionally activated during latent infection model, lytic cycle replication restrains its expression. Pharmacological inhibition of CA-activity using specific inhibitors retards EBV induced B-cell transformation, inhibits B-cells outgrowth and colony formation ability of transformed B-lymphocytes through lowering the intracellular pH, induction of cell apoptosis and facilitating degradation of CA9 transcripts. Reanalyses of ChIP-Seq data along with utilization of EBNA2 knockout virus, ectopic expression of EBNA2 and sh-RNA mediated knockdown of CA9 expression we further demonstrate that EBNA2 mediated CA9 transcriptional activation is essential for EBV latently infected B-cell survival. In contrast, during lytic cycle reactivation CA9 expression is transcriptionally suppressed by the key EBV lytic cycle transactivator, BZLF1 through its transactivation domain. Overall, our study highlights the dynamic alterations of CA9 expression and its activity in regulating pH homeostasis act as one of the major drivers for EBV induced B-cell transformation and subsequent B-cell lymphomagenesis.

Classification of Blood Cells with Convolutional Neural Network Model

Among the blood cells, white blood cells (WBC), which play a crucial role in forming our body's defense system, are essential components. Originating in the bone marrow, these cells serve as the fundamental components of the immune system, shouldering the responsibility of safeguarding the body against foreign microbes and diseases. Insufficient WBC counts may compromise the body's skill to resist infections, a status known as leukopenia. White blood cell counting is a specialty procedure that is usually carried out by qualified physicians and radiologists. Thanks to recent advances, image processing techniques are frequently used in biological systems to identify a wide spectrum of illnesses. In this work, image processing techniques were applied to enhance the white blood cell deep learning models' classification accuracy. To expedite the classification process, Convolutional Neural Network (CNN) models were combined with Ridge feature selection and Maximal Information Coefficient techniques. These tactics successfully determined the most important characteristics. The selected feature set was then applied to the classification procedure. ResNet-50, VGG19, and our suggested model were used as feature extractors in this study. The categorizing of white blood cells was completed with an amazing 98.27% success rate. Results from the experiments demonstrated a considerable improvement in classification accuracy using the proposed CNN model.

Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption

Natural gas is an indispensable non-renewable energy source for many countries. It is used in many different areas such as heating and kitchen appliances in homes, and heat treatment and electricity generation in industry. Natural gas is an essential component of the transportation sector, providing a cleaner alternative to traditional fuels in vehicles and fleets. Moreover, natural gas plays a vital role in boosting energy efficiency through the development of combined heat and power systems. These systems produce electricity and useful heat concurrently. As nations move towards more sustainable energy solutions, natural gas has gained prominence as a transitional fuel. This is due to its lower carbon emissions when compared to coal and oil, thus making it an essential component of the global energy framework. In this study, monthly natural gas consumption data of 28 different European countries between 2014 and 2022 are used. Symbolic Aggregate Approximation method is used to analyse the data. Analyses are made with different numbers of segments and numbers of alphabet sizes, and alphabet vectors of each country are created. These letter vectors are used in hierarchical clustering and dendrogram graphs are created. Furthermore, the elbow method is used to determine the appropriate number of clusters. Clusters of countries are created according to the determined number of clusters. In addition, it is interpreted according to the consumption trends of the countries in the determined clusters.

Proposing a novel mathematical model for hospital pneumatic system

Hospital Pneumatic Systems, specializing in pneumatic systems, are among the most essential components for hospitals. It offers efficient and cost-effective solutions to problems related to the transportation of various materials in hospitals. However, in existing systems, the need for compressed air is met without worrying about cost control and without depending on the sample transported, and this not only makes the system inefficient but also may cause sample degradation. The main purpose of this study is to provide speed/pressure control according to the type of material transported to eliminate the disadvantages of existing systems such as energy use and sample degradation. In this study, a new mathematical model is presented that can be used to make more energy-efficient hospital pneumatic systems. Although there are many studies on various pneumatic systems in the literature, there is not enough for the control of hospital pneumatic systems. According to the results obtained in this study, the system parameters were determined and the mathematical model of the system was obtained by using the Multivariate nonlinear regression method. A genetic algorithm was used to test the validity of the obtained mathematical model and to optimize the coefficient of the input parameters of the model. It is expected that this proposed model will contribute to the use of hospital pneumatic systems and provide a scientific and practical solution to the proposed mathematical model. The proposed mathematical model provides up to %43 more efficient transportation over the currently used system that has been tested.

Abstract 2027: Serological assessment of cancer associated myo-fibroblast (myCAF) activity by collagen pro-peptide biomarkers provides high prognostic power

Abstract Background: Myofibroblast Cancer Associated Fibroblasts (myCAFs) are the main tumor fibrosis drivers and hence different from inflammatory CAFs (iCAFs). MyCAFs are emerging as an important tool for understanding tumor biology. CAFs produce type III, V, VI and XI collagen that are the essential components of tumor fibrosis. Pro-peptides of these collagens can be quantified both in serum with the PRO-C3, PRO-C5, PRO-C6 and PRO-C11 biomarkers where they are prognostic for poor overall survival in patients with various solid tumor types and may be applied in vitro. Here we investigated the association and difference between myCAFs and iCAFs and their collagen expression profile and related that to data available data on serological assessments of PRO-C3, PRO-C5, PRO-C6 and PRO-C11, and cultured CAFs. Methods: The collagen expression in CAF subtypes were established from publicly available single-cell RNA-Seq dataset from pancreas cancer (PMID: 31273297) and non-small cell lung cancer (PMID: 36973297). In addition, we generated a biomarker profile using PRO-C3, PRO-C5, PRO-C6 and PRO-C11 ELISA to measure in serum of 220 patients with various solid tumor types and 33 healthy controls. We cultured CAFs in vitro and measured the same biomarkers in conditioned medium. We compared the result to historical PRO-C3, PRO-C6 and PRO-C11 prognostic data for pancreas cancer patient data. Results: Based on the single-cell RNA-Seq dataset, while type III and type VI collagen were found expressed in fibroblasts both from normal tissues and tumor tissues whereas type V collagen and XI collagen were found almost exclusively expressed in CAFs (tumor tissue). When further examining the CAF subtypes, type III collagen and VI collagen were found expressed in both myCAFs and iCAFs whereas type V collagen and XI collagen were found almost exclusively expressed in myCAFs and not iCAFs. Moreover, in the 220 patients, PRO-C3 and PRO-C6 was elevated in elevated in a few cancer types (p<0.05-0.01) with an inter-patient variation ~2-fold whereas PRO-C5 and PRO-C11 were elevated in all cancer types (p<0.0001) with a ~5-fold inter-patient variation. These four biomarkers identified different pancreas cancer patients endotypes all with poor overall survival, P<0.001. In direct alignment, collagen pro-peptides we highly upregulated in cultures of CAFs. Conclusions: Profiling collagen expression in fibroblast from PDAC and NSCLC reveals that type V collagen and type XI collagen are exclusively found in myCAF not iCAF. Biomarkers of these collagens can be measured in serum from cancer patients and are prognostic for poor overall survival. Thus, these data suggest that cancer associated myo-fibroblast (myCAF) activity can be assessed non-invasively by specific collagen pro-peptide biomarkers. Citation Format: Nicholas Willumsen, Neel I. Nissen, Morten A. Karsdal. Serological assessment of cancer associated myo-fibroblast (myCAF) activity by collagen pro-peptide biomarkers provides high prognostic power [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2027.

Abstract 4259: A novel human model of lung squamous cell carcinoma to deconvolve the cell-intrinsic effects of key dysregulated pathways

Abstract Tractable, patient relevant models are needed to investigate the essential components of normal cell transformation. We developed a unique in vitro model of early lung squamous cell carcinoma (LUSC) genetic alterations and driver pathway dysregulation using genetically modified primary human bronchial basal cells (hBCs) from three independent donors. The co-operation of ubiquitous and early LUSC alterations (TP53 and CDKN2A loss) and commonly deregulated pathways including squamous differentiation (SOX2), PI3K signaling (PTEN) and the oxidative stress response (KEAP1) was investigated using a combinatorial approach, culminating in eight unique hBC mutants harboring cumulative pathway alterations in a TP53-/-/CDKN2A-/- background. Phenotypes indicative of mutant hBC transformation were investigated using proliferation assays, organotypic cultures and anchorage independent growth assays. Organotypic cultures revealed LUSC pathway alterations to drive dramatic changes in airway histology, ranging from a normal epithelium to severe dysplasia. Furthermore, these analyses confirmed the role of SOX2-overexpression in initiating transformation by inducing squamous cell commitment and anchorage independent growth. We also identified a co-operation of SOX2-overexpression with the oxidative stress response and PI3K pathways to drive more advanced lesions in-vivo and in-vitro. Importantly, we were able to map changes in the expression of immunomodulatory factors with the pathway dysregulation. We demonstrated that this strategy constitute an alternative and patient-relevant system to model LUSC and identify genotype-phenotype correlations of clinical relevance. Citation Format: Julia Ogden, Robert Sellers, Anthony Oojageer, Sudhakar Sahoo, Anshuman Chaturvedi, Carlos Lopez-Garcia. A novel human model of lung squamous cell carcinoma to deconvolve the cell-intrinsic effects of key dysregulated pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4259.