Search results for: cancer classification

Authors: Peder O. Laugen Heggdal, Oyvind Nordvik, Jonas Brannstrom, Flemming Vassbotn, Anne Kari Aarstad, Hans Jorgen Aarstad

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Background: Hearing loss (HL) affect people of all ages and stages in life. To author's best knowledge, if patients with an HL have reduced Generic Quality of life (QoL), has yet not been answered. Aim: The aim of the present study was to investigate the relationship between HL and generic and disease-specific Health Related Quality of Life (HRQoL) in adult patients (aged 18–78 years) with an HL, seeking Hearing Aid (HA). Material and Methods: 158 adult (aged 18-78 years) patients with HL, referred for HA fitting at Haukeland University Hospital in western Norway, participated in the study. Both first-time users, as well as patients referred for HA renewals, were included. First-time users had been pre-examined by an Ear Nose and Throat specialist. The questionnaires were answered before the actual HA fitting procedure. The pure-tone average (PTA; frequencies 0.5, 1, 2 and 4 kHz) was determined for each ear. The generic European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire general part and a shortened version of the Abbreviated Profile of Hearing Aid Benefit (APHAB) were answered. In addition, EORTC HRQoL answers from a general population and patients with former head and neck cancer served as comparison. Results: In general, no lowered HRQoL scores were determined among HL patients compared to the general population. Patients with unilateral HL to some extent showed lower HRQoL than those with bilateral HL (social function and fatigue). The APHAB scores correlated significantly with the EORTC HRQoL scores. By stepwise linear regression analysis, the APHAB scores were scored secondary to PTA (best ear), cognitive and physical function. Conclusion: HRQoL scores in HL patients, in general, seems to be at the population level, but the unilateral HL patients scored to some extent lower than the bilateral HI patients. APHAB and generic QoL scores levels are associated. Both HRQoL and APHAB scores are generated more complexly than anticipated.

Keywords: quality of life, hearing loss, hearing impairment, distress, depression, anxiety, hearing aid

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Authors: M. Regina Carreira-Lopez

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Lightweight ontologies seek to concrete union relationships between a parent node, and a secondary node, also called "child node". This logic relation (L) can be formally defined as a triple ontological relation (LO) equivalent to LO in ⟨LN, LE, LC⟩, and where LN represents a finite set of nodes (N); LE is a set of entities (E), each of which represents a relationship between nodes to form a rooted tree of ⟨LN, LE⟩; and LC is a finite set of concepts (C), encoded in a formal language (FL). Mondoc enables more refined searches on semantic and classified facets for retrieving specialized knowledge about Atlantic migrations, from the Declaration of Independence of the United States of America (1776) and to the end of the Spanish Civil War (1939). The model looks forward to increasing documentary relevance by applying an inverse frequency of co-ocurrent hypernymy phenomena for a concrete dataset of textual corpora, with RMySQL package. Mondoc profiles archival utilities implementing SQL programming code, and allows data export to XML schemas, for achieving semantic and faceted analysis of speech by analyzing keywords in context (KWIC). The methodology applies random and unrestricted sampling techniques with RMySQL to verify the resonance phenomena of inverse documentary relevance between the number of co-occurrences of the same term (t) in more than two documents of a set of texts (D). Secondly, the research also evidences co-associations between (t) and their corresponding synonyms and antonyms (synsets) are also inverse. The results from grouping facets or polysemic words with synsets in more than two textual corpora within their syntagmatic context (nouns, verbs, adjectives, etc.) state how to proceed with semantic indexing of hypernymy phenomena for subject-heading lists and for authority lists for documentary and archival purposes. Mondoc contributes to the development of web directories and seems to achieve a proper and more selective search of e-documents (classification ontology). It can also foster on-line catalogs production for semantic authorities, or concepts, through XML schemas, because its applications could be used for implementing data models, by a prior adaptation of the based-ontology to structured meta-languages, such as OWL, RDF (descriptive ontology). Mondoc serves to the classification of concepts and applies a semantic indexing approach of facets. It enables information retrieval, as well as quantitative and qualitative data interpretation. The model reproduces a triple tuple ⟨LN, LE, LT, LCF L, BKF⟩ where LN is a set of entities that connect with other nodes to concrete a rooted tree in ⟨LN, LE⟩. LT specifies a set of terms, and LCF acts as a finite set of concepts, encoded in a formal language, L. Mondoc only resolves partial problems of linguistic ambiguity (in case of synonymy and antonymy), but neither the pragmatic dimension of natural language nor the cognitive perspective is addressed. To achieve this goal, forthcoming programming developments should target at oriented meta-languages with structured documents in XML.

Keywords: hypernymy, information retrieval, lightweight ontology, resonance

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Authors: Guoheng Liu, Zhilong Huang

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For more than the past ten years, oil and gas production from marine shale such as the Barnett shale. In addition, in recent years, major breakthroughs have also been made in lacustrine shale gas exploration, such as the Yanchang Formation of the Ordos Basin in China. Lucaogou Formation shale, which is also lacustrine shale, has also yielded a high production in recent years, for wells such as M1, M6, and ML2, yielding a daily oil production of 5.6 tons, 37.4 tons and 13.56 tons, respectively. Lithologic identification and classification of reservoirs are the base and keys to oil and gas exploration. Lithology and lithofacies obviously control the distribution of oil and gas in lithological reservoirs, so it is of great significance to describe characteristics of lithology and lithofacies of reservoirs finely. Lithofacies is an intrinsic property of rock formed under certain conditions of sedimentation. Fine-grained sedimentary rocks such as shale formed under different sedimentary conditions display great particularity and distinctiveness. Hence, to our best knowledge, no constant and unified criteria and methods exist for fine-grained sedimentary rocks regarding lithofacies definition and classification. Consequently, multi-parameters and multi-disciplines are necessary. A series of qualitative descriptions and quantitative analysis were used to figure out the lithofacies characteristics and its effect on oil accumulation of Lucaogou formation fine-grained sedimentary rocks in Santanghu basin. The qualitative description includes core description, petrographic thin section observation, fluorescent thin-section observation, cathode luminescence observation and scanning electron microscope observation. The quantitative analyses include X-ray diffraction, total organic content analysis, ROCK-EVAL.II Methodology, soxhlet extraction, porosity and permeability analysis and oil saturation analysis. Three types of lithofacies were mainly well-developed in this study area, which is organic-rich massive shale lithofacies, organic-rich laminated and cloddy hybrid sedimentary lithofacies and organic-lean massive carbonate lithofacies. Organic-rich massive shale lithofacies mainly include massive shale and tuffaceous shale, of which quartz and clay minerals are the major components. Organic-rich laminated and cloddy hybrid sedimentary lithofacies contain lamina and cloddy structure. Rocks from this lithofacies chiefly consist of dolomite and quartz. Organic-lean massive carbonate lithofacies mainly contains massive bedding fine-grained carbonate rocks, of which fine-grained dolomite accounts for the main part. Organic-rich massive shale lithofacies contain the highest content of free hydrocarbon and solid organic matter. Moreover, more pores were developed in organic-rich massive shale lithofacies. Organic-lean massive carbonate lithofacies contain the lowest content solid organic matter and develop the least amount of pores. Organic-rich laminated and cloddy hybrid sedimentary lithofacies develop the largest number of cracks and fractures. To sum up, organic-rich massive shale lithofacies is the most favorable type of lithofacies. Organic-lean massive carbonate lithofacies is impossible for large scale oil accumulation.

Keywords: lithofacies classification, tuffaceous shale, oil enrichment, Lucaogou formation

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Authors: N. Tochukwu, M. Mukhopadhyay, A. Mohamed

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It is no new that Nigeria faces a continual power shortage problem due to its vast population power demand and heavy reliance on nonrenewable forms of energy such as thermal power or fossil fuel. Many researchers have recommended using geothermal energy as an alternative; however, Past studies focus on the geophysical & geochemical investigation of this energy in the sedimentary and basement complex; only a few studies incorporated the remote sensing methods. Therefore, in this study, the preliminary examination of geothermal resources in the Middle Benue was carried out using satellite imagery in ArcMap. Landsat 8 scene (TIR, NIR, Red spectral bands) was used to estimate the Land Surface Temperature (LST). The Maximum Likelihood Classification (MLC) technique was used to classify sites with very low, low, moderate, and high LST. The intermediate and high classification happens to be possible geothermal zones, and they occupy 49% of the study area (38077km2). Riverline were superimposed on the LST layer, and the identification tool was used to locate high temperate sites. Streams that overlap on the selected sites were regarded as geothermal springs as. Surprisingly, the LST results show lower temperatures (<36°C) at the famous thermal springs (Awe & Wukari) than some unknown rivers/streams found in Kwande (38°C), Ussa, (38°C), Gwer East (37°C), Yola Cross & Ogoja (36°C). Studies have revealed that temperature increases with depth. However, this result shows excellent geothermal resources potential as it is expected to exceed the minimum geothermal gradient of 25.47 with an increase in depth. Therefore, further investigation is required to estimate the depth of the causative body, geothermal gradients, and the sustainability of the reservoirs by geophysical and field exploration. This method has proven to be cost-effective in locating geothermal resources in the study area. Consequently, the same procedure is recommended to be applied in other regions of the Precambrian basement complex and the sedimentary basins in Nigeria to save a preliminary field survey cost.

Keywords: ArcMap, geothermal resources, Landsat 8, LST, thermal springs, MLC

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Authors: A. Gilewska, J. Masternak, K. Kazimierczuk, L. Turlej, J. Wietrzyk, B. Barszcz

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Platinum-based drugs are now widely used as chemotherapeutic agents. However the platinum complexes show the toxic side-effects: i) the development of platinum resistance; ii) the occurrence of severe side effects, such as nephro-, neuro- and ototoxicity; iii) the high toxicity towards human fibroblast. Therefore the development of new anticancer drugs containing different transition-metal ions, for example, ruthenium, rhodium, iridium is a valid strategy in cancer treatment. In this paper, we reported the synthesis, spectroscopic, structural and biological properties of complexes of ruthenium, rhodium, and iridium containing N,N-chelating ligand (2,2’-bisimidazole). These complexes were characterized by elemental analysis, UV-Vis and IR spectroscopy, X-ray diffraction analysis. These complexes exhibit a typical pseudotetrahedral three-legged piano-stool geometry, in which the aromatic arene ring forms the seat of the piano-stool, while the bidentate 2,2’-bisimidazole (ligand) and the one chlorido ligand form the three legs of the stool. The spectroscopy data (IR, UV-Vis) and elemental analysis correlate very well with molecular structures. Moreover, the cytotoxic activity of the complexes was carried out on human cancer cell lines: LoVo (colorectal adenoma), MV-4-11 (myelomonocytic leukaemia), MCF-7 (breast adenocarcinoma) and normal healthy mouse fibroblast BALB/3T3 cell lines. To predict a binding mode, a potential interaction of metal complexes with calf thymus DNA (CT-DNA) and protein (BSA) has been explored using UV absorption and circular dichroism (CD). It is interesting to note that the investigated complexes show no cytotoxic effect towards the normal BALB/3T3 cell line, compared to cisplatin, which IC₅₀ values was determined as 2.20 µM. Importantly, Ru(II) displayed the highest activity against HL-60 (IC₅₀ 4.35 µM). The biological studies (UV-Vis and circular dichroism) suggest that arene-complexes could interact with calf thymus DNA probably via an outside binding mode and interact with protein (BSA).

Keywords: ruthenium(II) complex, rhodium(III) complex, iridium(III) complex, biological activity

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

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Authors: Misty Attwood, Helgi Schioth

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Transmembrane proteins are important components in many essential cell processes such as signal transduction, cell-cell signalling, transport of solutes, structural adhesion activities, and protein trafficking. Due to their involvement in diverse critical activities, transmembrane proteins are implicated in different disease pathways and hence are the focus of intense interest in understanding functional activities, their pathogenesis in disease, and their potential as pharmaceutical targets. Further, as the structure and function of proteins are correlated, investigating a group of proteins with the same tertiary structure, i.e., the same number of transmembrane regions, may give understanding about their functional roles and potential as therapeutic targets. In this in silico bioinformatics analysis, we identify and comprehensively characterize the previously unstudied group of proteins with five transmembrane-spanning regions (5TM). We classify nearly 60 5TM proteins in which 31 are members of ten families that contain two or more family members and all members are predicted to contain the 5TM architecture. Furthermore, nine singlet proteins that contain the 5TM architecture without paralogues detected in humans were also identifying, indicating the evolution of single unique proteins with the 5TM structure. Interestingly, more than half of these proteins function in localization activities through movement or tethering of cell components and more than one-third are involved in transport activities, particularly in the mitochondria. Surprisingly, no receptor activity was identified within this family in sharp contrast with other TM families. Three major 5TM families were identified and include the Tweety family, which are pore-forming subunits of the swelling-dependent volume regulated anion channel in astrocytes; the sidoreflexin family that acts as mitochondrial amino acid transporters; and the Yip1 domain family engaged in vesicle budding and intra-Golgi transport. About 30% of the proteins have enhanced expression in the brain, liver, or testis. Importantly, 60% of these proteins are identified as cancer prognostic markers, where they are associated with clinical outcomes of various tumour types, indicating further investigation into the function and expression of these proteins is important. This study provides the first comprehensive analysis of proteins with 5TM regions and provides details of the unique characteristics and application in pharmaceutical development.

Keywords: 5TM, cancer prognostic marker, drug targets, transmembrane protein

Procedia PDF Downloads 108

Authors: Fei Ye, Åsa Barrefelt, Manuchehr Abedi-Valugerdi, Khalid M. Abu-Salah, Salman A. Alrokayan, Mamoun Muhammed, Moustapha Hassan

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With appropriate encapsulation in functional nanoparticles drugs are more stable in physiological environment and the kinetics of the drug can be more carefully controlled and monitored. Furthermore, targeted drug delivery can be developed to improve chemotherapy in cancer treatment, not only by enhancing intracellular uptake by target cells but also by reducing the adverse effects in non-target organs. Inorganic imaging agents, delivered together with anti-cancer drugs, enhance the local imaging contrast and provide precise diagnosis as well as evaluation of therapy efficacy. We have developed biodegradable polymeric vesicles as a nanocarrier system for multimodal bio-imaging and anticancer drug delivery. The poly (lactic-co-glycolic acid) PLGA) vesicles were fabricated by encapsulating inorganic imaging agents of superparamagnetic iron oxide nanoparticles (SPION), manganese-doped zinc sulfide (MN:ZnS) quantum dots (QDs) and the anticancer drug busulfan into PLGA nanoparticles via an emulsion-evaporation method. T2-weighted magnetic resonance imaging (MRI) of PLGA-SPION-Mn:ZnS phantoms exhibited enhanced negative contrast with r2 relaxivity of approximately 523 s-1 mM-1 Fe. Murine macrophage (J774A) cellular uptake of PLGA vesicles started fluorescence imaging at 2 h and reached maximum intensity at 24 h incubation. The drug delivery ability PLGA vesicles was demonstrated in vitro by release of busulfan. PLGA vesicles degradation was studied in vitro, showing that approximately 32% was degraded into lactic and glycolic acid over a period of 5 weeks. The biodistribution of PLGA vesicles was investigated in vivo by MRI in a rat model. Change of contrast in the liver could be visualized by MRI after 7 min and maximal signal loss detected after 4 h post-injection of PLGA vesicles. Histological studies showed that the presence of PLGA vesicles in organs was shifted from the lungs to the liver and spleen over time.

Keywords: biodegradable polymers, multifunctional nanoparticles, quantum dots, anticancer drugs

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Authors: Husham Bayazed

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Purpose of Presentation: Obesity and overweight are among the biggest health challenges of the 21st century, according to the WHO. Obviously, obese individuals suffer different courses of disease – from infections and allergies to cancer- and even respond differently to some treatment options. Of note, obesity often seems to predispose and triggers several secondary diseases such as diabetes, arteriosclerosis, or heart attacks. Since decades it seems that immunological signals gear inflammatory processes among obese individuals with the aforementioned conditions. This review aims to shed light how obesity sparks or rewire the immune system and predisposes to such unpleasant health outcomes. Moreover, lessons from the Covid-19 pandemic ascertain that people living with pre-existing conditions such as obesity can develop severe acute respiratory syndrome (SARS), which needs to be elucidated how obesity and its adjuvant inflammatory process distortion contribute to enhancing severe COVID-19 consequences. Recent Findings: In recent clinical studies, obesity was linked to alter and sparks the immune system in different ways. Adipose tissue (AT) is considered as a secondary immune organ, which is a reservoir of tissue-resident of different immune cells with mediator release, making it a secondary immune organ. Adipocytes per se secrete several pro-inflammatory cytokines (IL-6, IL-4, MCP-1, and TNF-α ) involved in activation of macrophages resulting in chronic low-grade inflammation. The correlation between obesity and T cells dysregulation is pivotal in rewiring the immune system. Of note, autophagy occurrence in adipose tissues further rewire the immune system due to flush and outburst of leptin and adiponectin, which are cytokines and influencing pro-inflammatory immune functions. These immune alterations among obese individuals are collectively incriminated in triggering several metabolic disorders and playing role in increasing cancers incidence and susceptibility to different infections. During COVID-19 pandemic, it was verified that patients with pre-existing obesity being at greater risk of suffering severe and fatal clinical outcomes. Beside obese people suffer from increased airway resistance and reduced lung volume, ACE2 expression in adipose tissue seems to be high and even higher than that in lungs, which spike infection incidence. In essence, obesity with pre-existence of pro-inflammatory cytokines such as LI-6 is a risk factor for cytokine storm and coagulopathy among COVID-19 patients. Summary: It is well documented that obesity is associated with chronic systemic low-grade inflammation, which sparks and alter different pillars of the immune system and triggers different metabolic disorders, and increases susceptibility of infections and cancer incidence. The pre-existing chronic inflammation in obese patients with the augmented inflammatory response against the viral infection seems to increase the susceptibility of these patients to developing severe COVID-19. Although the new weight loss drugs and bariatric surgery are considered as breakthrough news for obesity treatment, but preventing is easier than treating it once it has taken hold. However, obesity and immune system link new insights dispute the role of immunotherapy and regulating immune cells treating diet-induced obesity.

Keywords: immunity, metabolic disorders, cancer, COVID-19

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Anjan Naidu

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In this paper we first discuss the minimum spanning tree, then we use the Kruskal algorithm to obtain minimum spanning tree. Based on Kruskal algorithm we propose Kruskal algorithm to apply an application to find minimum cost applying the concept of spanning tree.

Keywords: Minimum Spanning tree, algorithm, Heuxistic, application, classification of Sub 97K90

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Authors: A. O. Ogunkeyede, D. Amuchi, A. A. Adebayo

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Heavy metal contaminations in soil have received great attention. Anthropogenic activities such as vehicular emission, industrial activities and constructions have resulted in elevated concentration of heavy metals in the surface soils. The metal particles can be free from the surface soil when they are disturbed and re-entrained in air, which necessitated the need to investigate surface soil at market environment where adults and children are present on daily basis. This study assesses concentration of heavy metal pollution, ecological and health risk factors in surface soil at Effurun market. 8 samples were collected at household material (EMH), fish (EMFs), fish and commodities (EMF-C), Abattoir (EMA 1 & 2), fruit sections (EMF 1 & 2) and lastly main road (EMMR). The samples were digested and analyzed in triplicate for contents of Lead (Pb), Nickel (Ni), Cadmium (Cd) and Copper (Cu). The mean concentration of the Pb mg/kg (112.27 ± 1.12) and Cu mg/kg (156.14 ± 1.10) were highest in the abattoir section (EMA 1). The mean concentrations of the heavy metal were then used to calculate the ecological and health risk for people within the market. Pb contamination at EMMR, EMF 2, EMFs were moderately while Pb shows considerable contamination at EMH, EMA 1, EMA 2 and EMF-C sections of the Effurun market. The ecological risk factor varies between low to moderate pollution for Pb and EMA 1 has the highest potential ecological risk that falls within moderate pollution. The hazard quotient results show that dermal exposure pathway is the possible means of heavy metal exposure to the traders while ingestion is the least sources of exposure to adult. The ingestion suggested that children around the EMA 1 have the highest possible exposure to children due to hand-to-mouth and object-to-mouth behaviour. The results further show that adults at the EMA1 will have the highest exposure to Pb due to inhalation during burning of cow with tyre that contained Pb and Cu. The carcinogenic risk values of most sections were higher than acceptable values, while Ni at EMMR, EMF 1 & 2, EMFs and EMF-C sections that were below the acceptable values. The cancer risk for inhalation exposure pathway for Pb (1.01E+17) shows a significant level of contamination than all the other sections of the market. It suggested that the people working at the Abattoir were very prone to cancer risk.

Keywords: carcinogenic, ecological, heavy metal, risk

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Authors: Ahlem Bouziri, Chiraz Latiri, Eric Gaussier

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The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to extract dependencies between terms, namely a generic basis of association rules between terms. The key feature of our approach is a better trade off between the size of the mining result and the conveyed knowledge. Thus, face to the huge number of derived association rules and in order to select the optimal combination of query terms from the generic basis, we propose to model the problem as a classification problem and solve it using a supervised learning algorithm such as SVM or k-means. For this purpose, we first generate a training set using a genetic algorithm based approach that explores the association rules space in order to find an optimal set of expansion terms, improving the MAP of the search results. The experiments were performed on SDA 95 collection, a data collection for information retrieval. It was found that the results were better in both terms of MAP and NDCG. The main observation is that the hybridization of text mining techniques and query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing the proposed method.

Keywords: supervised leaning, classification, query expansion, association rules

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Authors: Mohamed Moussaoui, Mouna Baassi, Soukayna Baammi, Hatim Soufi, Mohammed Salah, Rachid Daoud, Achraf EL Allali, Mohammed Elalaoui Belghiti, Said Belaaouad

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The present study aims to investigate the quantitative structure-activity relationship (QSAR) of a series of Thiazole derivatives reported as anticancer agents (hepatocellular carcinoma), using principally the electronic descriptors calculated by the density functional theory (DFT) method and by applying the multiple linear regression method. The developed model showed good statistical parameters (R²= 0.725, R²ₐ𝒹ⱼ= 0.653, MSE = 0.060, R²ₜₑₛₜ= 0.827, Q²𝒸ᵥ = 0.536). The energy of the highest occupied molecular orbital (EHOMO) orbital, electronic energy (TE), shape coefficient (I), number of rotatable bonds (NROT), and index of refraction (n) were revealed to be the main descriptors influencing the anti-cancer activity. Additional Thiazole derivatives were then designed and their activities and pharmacokinetic properties were predicted using the validated QSAR model. These designed molecules underwent evaluation through molecular docking (MD) and molecular dynamic (MD) simulations, with binding affinity calculated using the MMPBSA script according to a 100 ns simulation trajectory. This process aimed to study both their affinity and stability towards Cyclin-Dependent Kinase 2 (CDK2), a target protein for cancer disease treatment. The research concluded by identifying four CDK2 inhibitors - A1, A3, A5, and A6 - displaying satisfactory pharmacokinetic properties. MDs results indicated that the designed compound A5 remained stable in the active center of the CDK2 protein, suggesting its potential as an effective inhibitor for the treatment of hepatocellular carcinoma. The findings of this study could contribute significantly to the development of effective CDK2 inhibitors.

Keywords: QSAR, ADMET, Thiazole, anticancer, molecular docking, molecular dynamic simulations, MMPBSA calculation

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Authors: Jyoti Bharti, Navneet Manjhi, M. K.Gupta, Bimi Jain

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A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a simple method for gait identification which is based on moments. Moment values are extracted on different number of frames of gray scale and silhouette images of CASIA database. These moment values are considered as feature values. Fuzzy logic and nearest neighbour classifier are used for classification. Both achieved higher recognition.

Keywords: gait, fuzzy logic, nearest neighbour, recognition rate, moments

Procedia PDF Downloads 756

Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

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Authors: AnjalI A. Prasad

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Nowadays, social media played an important role in spreading misinformation or fake news. This study analyzes the fake news related to the COVID-19 pandemic spread in social media. This paper aims at evaluating and comparing different approaches that are used to mitigate this issue, including popular deep learning approaches, such as CNN, RNN, LSTM, and BERT algorithm for classification. To evaluate models’ performance, we used accuracy, precision, recall, and F1-score as the evaluation metrics. And finally, compare which algorithm shows better result among the four algorithms.

Keywords: BERT, CNN, LSTM, RNN

Procedia PDF Downloads 203

Authors: Grâce Chidikofan, François Pinta, A. Benoist, G. Volle, J. Valette

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Statement of the Problem: biomass gasification systems may be relevant for decentralized power generation from recoverable agricultural and wood residues available in rural areas. In recent years, many systems have been implemented in all over the world as especially in Cambodgia, India. Although they have many positive effects, these systems can also affect the environment and human health. Indeed, during the process of biomass gasification, black wastewater containing tars are produced and generally discharged in the local environment either into the rivers or on soil. However, in most environmental assessment studies of biomass gasification systems, the impact of these releases are underestimated, due to the difficulty of identification of their chemical substances. This work deal with the analysis of the environmental impacts of tars from wood gasification in terms of human toxicity cancer effect, human toxicity non-cancer effect, and freshwater ecotoxicity. Methodology: A Life Cycle Assessment (LCA) approach was adopted. The inventory of tars chemicals substances was based on experimental data from a downdraft gasification system. The composition of six samples from two batches of raw materials: one batch made of tree wood species (oak+ plane tree +pine) at 25 % moisture content and the second batch made of oak at 11% moisture content. The tests were carried out for different gasifier load rates, respectively in the range 50-75% and 50-100%. To choose the environmental impacts assessment method, we compared the methods available in SIMAPRO tool (8.2.0) which are taking into account most of the chemical substances. The environmental impacts for 1kg of tars discharged were characterized by ILCD 2011+ method (V.1.08). Findings Experimental results revealed 38 important chemical substances in varying proportion from one test to another. Only 30 are characterized by ILCD 2011+ method, which is one of the best performing methods. The results show that wood species or moisture content have no significant impact on human toxicity noncancer effect (HTNCE) and freshwater ecotoxicity (FWE) for water release. For human toxicity cancer effect (HTCE), a small gap is observed between impact factors of the two batches, either 3.08E-7 CTUh/kg against 6.58E-7 CTUh/kg. On the other hand, it was found that the risk of negative effects is higher in case of tar release into water than on soil for all impact categories. Indeed, considering the set of samples, the average impact factor obtained for HTNCE varies respectively from 1.64 E-7 to 1.60E-8 CTUh/kg. For HTCE, the impact factor varies between 4.83E-07 CTUh/kg and 2.43E-08 CTUh/kg. The variability of those impact factors is relatively low for these two impact categories. Concerning FWE, the variability of impact factor is very high. It is 1.3E+03 CTUe/kg for tars release into water against 2.01E+01 CTUe/kg for tars release on soil. Statement concluding: The results of this study show that the environmental impacts of tars emission of biomass gasification systems can be consequent and it is important to investigate the ways to reduce them. For environmental research, these results represent an important step of a global environmental assessment of the studied systems. It could be used to better manage the wastewater containing tars to reduce as possible the impacts of numerous still running systems all over the world.

Keywords: biomass gasification, life cycle analysis, LCA, environmental impact, tars

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Authors: J. Chamorro-Servent, S. Tassani, M. A. Gonzalez-Ballester, L. J. Roca, J. Romeu, O. Camara

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Electromagnetic and microwave imaging (MWI) have been used in medical imaging in the last years, being the most common applications of breast cancer and stroke detection or monitoring. In those applications, the subject or zone to observe is surrounded by a number of antennas, and the Nyquist criterium can be satisfied. Additionally, the space between the antennas (transmitting and receiving the electromagnetic fields) and the zone to study can be prepared in a homogeneous scenario. However, this may differ in other cases as could be intracardiac catheters, stomach monitoring devices, pelvic organ systems, liver ablation monitoring devices, or uterine fibroids’ ablation systems. In this work, we analyzed different MWI algorithms to find the most suitable method for dealing with an intrabody scenario. Due to the space limitations usually confronted on those applications, the device would have a cylindrical configuration of a maximum of eight transmitters and eight receiver antennas. This together with the positioning of the supposed device inside a body tract impose additional constraints in order to choose a reconstruction method; for instance, it inhabitants the use of well-known algorithms such as filtered backpropagation for diffraction tomography (due to the unusual configuration with probes enclosed by the imaging region). Finally, the difficulty of simulating a realistic non-homogeneous background inside the body (due to the incomplete knowledge of the dielectric properties of other tissues between the antennas’ position and the zone to observe), also prevents the use of Born and Rytov algorithms due to their limitations with a heterogeneous background. Instead, we decided to use a time-reversed algorithm (mostly used in geophysics) due to its characteristics of ignoring heterogeneities in the background medium, and of focusing its generated field onto the scatters. Therefore, a 2D time-reversed finite difference time domain was developed based on the time-reversed approach for microwave breast cancer detection. Simultaneously an in-silico testbed was also developed to compare ground-truth dielectric properties with corresponding microwave imaging reconstruction. Forward and inverse problems were computed varying: the frequency used related to a small zone to observe (7, 7.5 and 8 GHz); a small polyp diameter (5, 7 and 10 mm); two polyp positions with respect to the closest antenna (aligned or disaligned); and the (transmitters-to-receivers) antenna combination used for the reconstruction (1-1, 8-1, 8-8 or 8-3). Results indicate that when using the existent time-reversed method for breast cancer here for the different combinations of transmitters and receivers, we found false positives due to the high degrees of freedom and unusual configuration (and the possible violation of Nyquist criterium). Those false positives founded in 8-1 and 8-8 combinations, highly reduced with the 1-1 and 8-3 combination, being the 8-3 configuration de most suitable (three neighboring receivers at each time). The 8-3 configuration creates a region-of-interest reduced problem, decreasing the ill-posedness of the inverse problem. To conclude, the proposed algorithm solves the main limitations of the described intrabody application, successfully detecting the angular position of targets inside the body tract.

Keywords: FDTD, time-reversed, medical imaging, microwave imaging

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Authors: Subham Ghosh, Banani Basu, Marami Das

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Background: The development of compact, low-power antenna sensors has resulted in hardware restructuring, allowing for wireless ubiquitous sensing. The antenna sensors can create wireless body-area networks (WBAN) by linking various wireless nodes across the human body. WBAN and IoT applications, such as remote health and fitness monitoring and rehabilitation, are becoming increasingly important. In particular, Parkinson’s disease (PD), a common neurodegenerative disorder, presents clinical features that can be easily misdiagnosed. As a mobility disease, it may greatly benefit from the antenna’s nearfield approach with a variety of activities that can use WBAN and IoT technologies to increase diagnosis accuracy and patient monitoring. Methodology: This study investigates the feasibility of leveraging a single patch antenna mounted (using cloth) on the wrist dorsal to differentiate actual Parkinson's disease (PD) from false PD using a small hardware platform. The semi-flexible antenna operates at the 2.4 GHz ISM band and collects reflection coefficient (Γ) data from patients performing five exercises designed for the classification of PD and other disorders such as essential tremor (ET) or those physiological disorders caused by anxiety or stress. The obtained data is normalized and converted into 2-D representations using the Gabor wavelet transform (GWT). Data augmentation is then used to expand the dataset size. A lightweight deep-learning (DL) model is developed to run on the GPU-enabled NVIDIA Jetson Nano platform. The DL model processes the 2-D images for feature extraction and classification. Findings: The DL model was trained and tested on both the original and augmented datasets, thus doubling the dataset size. To ensure robustness, a 5-fold stratified cross-validation (5-FSCV) method was used. The proposed framework, utilizing a DL model with 1.356 million parameters on the NVIDIA Jetson Nano, achieved optimal performance in terms of accuracy of 88.64%, F1-score of 88.54, and recall of 90.46%, with a latency of 33 seconds per epoch.

Keywords: antenna, deep-learning, GPU-hardware, Parkinson’s disease

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Authors: Ferran Paulí Pla, Pere Palacín Farré, Albert Fornells Herrera, Pol Toldrà Fernández

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Poor image acquisition is limiting the promising growth of industrial vision in food control. In recent years, the food industry has witnessed a significant increase in the implementation of automation in quality control through artificial vision, a trend that continues to grow. During the packaging process, some defects may appear, compromising the proper sealing of the products and diminishing their shelf life, sanitary conditions and overall properties. While failure to detect a defective product leads to major losses, food producers also aim to minimize over-rejection to avoid unnecessary waste. Thus, accuracy in the evaluation of the products is crucial, and, given the large production volumes, even small improvements have a significant impact. Recently, efforts have been focused on maximizing the performance of classification neural networks; nevertheless, their performance is limited by the quality of the input data. Monochrome linear backlight systems are most commonly used for online inspections of food packaging thermo-sealing zones. These simple acquisition systems fit the high cadence of the production lines imposed by the market demand. Nevertheless, they provide a limited amount of data, which negatively impacts classification algorithm training. A desired situation would be one where data quality is maximized in terms of obtaining the key information to detect defects while maintaining a fast working pace. This work presents a backlight hyperspectral imaging system designed and implemented replicating an industrial environment to better understand the relationship between visual data quality and spectral illumination range for a variety of packed food products. Furthermore, results led to the identification of advantageous spectral bands that significantly enhance image quality, providing clearer detection of defects.

Keywords: artificial vision, food packaging, hyperspectral imaging, image acquisition, quality control

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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Authors: Loonibha Manandhar, Rajendra Bhandari, Kumud Raj Kafle

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Nepal’s Terai region is a part of the Ganges river basin which is one of the most disaster-prone areas of the world, with recurrent monsoon flooding causing millions in damage and the death and displacement of hundreds of people and households every year. The vulnerability of human settlements to natural disasters such as floods is increasing, and mapping changes in land use practices and hydro-geological parameters is essential in developing resilient communities and strong disaster management policies. The objective of this study was to develop a flood hazard zonation map of Orai Khola watershed and map the decadal land use/land cover dynamics of the watershed. The watershed area was delineated using SRTM DEM, and LANDSAT images were classified into five land use classes (forest, grassland, sediment and bare land, settlement area and cropland, and water body) using pixel-based semi-automated supervised maximum likelihood classification. Decadal changes in each class were then quantified using spatial modelling. Flood hazard mapping was performed by assigning weights to factors slope, rainfall distribution, distance from the river and land use/land cover on the basis of their estimated influence in causing flood hazard and performing weighed overlay analysis to identify areas that are highly vulnerable. The forest and grassland coverage increased by 11.53 km² (3.8%) and 1.43 km² (0.47%) from 1996 to 2016. The sediment and bare land areas decreased by 12.45 km² (4.12%) from 1996 to 2016 whereas settlement and cropland areas showed a consistent increase to 14.22 km² (4.7%). Waterbody coverage also increased to 0.3 km² (0.09%) from 1996-2016. 1.27% (3.65 km²) of total watershed area was categorized into very low hazard zone, 20.94% (60.31 km²) area into low hazard zone, 37.59% (108.3 km²) area into moderate hazard zone, 29.25% (84.27 km²) area into high hazard zone and 31 villages which comprised 10.95% (31.55 km²) were categorized into high hazard zone area.

Keywords: flood hazard, land use/land cover, Orai river, supervised maximum likelihood classification, weighed overlay analysis

Procedia PDF Downloads 351

Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: Prajna Paramita Naik, Sujit Kumar Bhutia

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Background: Regardless of the development multimodal treatment strategies, oral squamous cell carcinoma (OSCC) is often associated with a high rate of recurrence, metastasis and chemo- and radio- resistance. The present study inspected the relevance of CD44, ABCB1 and ADAM17 expression as a putative stem cell compartment in oral squamous cell carcinoma (OSCC) and deciphered the role of autophagy in regulating the expression of aforementioned proteins, stemness and chemoresistance. Methods: A retrospective analysis of CD44, ABCB1 and ADAM17 expression with respect to the various clinicopathological factors of sixty OSCC patients were determined via immunohistochemistry. The correlation among CD44, ABCB1 and ADAM17 expression was established. Sphere formation assay, flow cytometry and fluorescence microscopy were conducted to elucidate the stemness and chemoresistance nature of established cisplatin-resistant oral cancer cells (FaDu). The pattern of expression of CD44, ABCB1 and ADAM17 in parental (FaDu-P) and resistant FaDu cells (FaDu-CDDP-R) were investigated through fluorescence microscopy. Western blot analysis of autophagy marker proteins was performed to compare the status of autophagy in parental and resistant FaDu cell. To investigate the role of autophagy in chemoresistance and stemness, sphere formation assay, immunofluorescence and Western blot analysis was performed post transfection with siATG14 and the level of expression of autophagic proteins, mitochondrial protein and stemness-associated proteins were analyzed. The statistical analysis was performed by GraphPad Prism 4.0 software. p-value was defined as follows: not significant (n.s.): p > 0.05;*: p ≤ 0.05; **: p ≤ 0.01; ***: p ≤ 0.001; ****: p ≤ 0.0001 were considered statistically significant. Results: In OSCC, high CD44, ABCB1 and ADAM17 expression were significantly correlated with higher tumor grades and poor differentiation. However, the expression of these proteins was not related to the age and sex of OSCC patients. Moreover, the expression of CD44, ABCB1 and ADAM17 were positively correlated with each other. In vitro and OSCC tissue double labeling experiment data showed that CD44+ cells were highly associated with ABCB1 and ADAM17 expression. Further, FaDu-CDDP-R cells showed higher sphere forming capacity along with increased fraction of the CD44+ population and β-catenin expression FaDu-CDDP-R cells also showed accelerated expression of CD44, ABCB1 and ADAM17. A comparatively higher autophagic flux was observed in FaDu-CDDP-R against FaDu-P cells. The expression of mitochondrial proteins was noticeably reduced in resistant cells as compared to parental cells indicating the occurrence of autophagy-mediated mitochondrial degradation in oral cancer. Moreover, inhibition of autophagy was coupled with the decreased formation of orospheres suggesting autophagy-mediated stemness in oral cancer. Blockade of autophagy was also found to induce the restoration of mitochondrial proteins in FaDu-CDDP-R cells indicating the involvement of mitophagy in chemoresistance. Furthermore, a reduced expression of CD44, ABCB1 and ADAM17 was also observed in ATG14 deficient cells FaDu-P and FaDu-CDDP-R cells. Conclusion: The CD44+ ⁄ABCB1+ ⁄ADAM17+ expression in OSCC might be associated with chemoresistance and a putative CSC compartment. Further, the present study highlights the contribution of mitophagy in chemoresistance and confirms the potential involvement of autophagic regulation in acquisition of stem-like characteristics in OSCC.

Keywords: ABCB1, ADAM17, autophagy, CD44, chemoresistance, mitophagy, OSCC, stemness

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Authors: Miceska Simona, Škof Erik, Frković Grazio Snježana, Jeričević Anja, Smrkolj Špela, Cvjetićanin Branko, Novaković Srdjan, Grčar Kuzmanov Biljana, Kloboves-Prevodnik Veronika

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Objectives: High-grade serous ovarian cancer (HGSOC) is characterized by the dissemination of the tumor cells (TC) in the peritoneal cavity forming malignant ascites at the time of diagnosis or recurrence. Still, cytology itself has been underutilized as a modality for the diagnosis of HGSOC from ascites, and histological examination from the tumor tissue is yet the only validated method used. The objective of this study was to evaluate the reliability of cytology in the diagnosis of HGSOC in relation to the histopathological examination. Methods: The study included 42 patients with histologically confirmed HGSOC, accompanied by malignant ascites. To confirm the malignancy of the TC in the ascites and to define their immunophenotype, immunohistochemical reaction (IHC) of the following antigens: Calretinin, MOC, WT1, PAX8, p53, p16 & Ki-67 was evaluated on ascites cytospins and tissue blocks. For complete cytological determination of HGSOC, BRCA 1/2 gene mutation was determined from ascites, tissue block, and blood. BRCA1/2 mutation from blood was performed to define the type of mutation, somatic vs germline. Results: Among 42 patients, the immunophenotype of HGSOC from ascites was confirmed in 36 cases (86%). For more profound analysis, the patients were divided in 3 groups regarding the number of TC present in the ascites: patients with less than 10% TC, 10% TC, and more than 10% TC. From all included patients, in the group with less than 10% TC, there were 10 cases, and only 5 of them(50%) showed HGSOC phenotype; 12 cases had equally 10% of TC, and 11 cases (92%) showed HGSOC phenotype; 20 cases had more than 10% TC and all of them (100%) confirmed the HGSOC immunophenotype from ascites. Only 33 patients were eligible for further BRCA1/2 analysis. Eleven BRCA1/2 mutations were detected from thetissue block: 6 germline and 5 somatic. In 2 cases with less than 10% TC, BRCA1/2 mutation was not detected; 4 cases had 10% TC, and 2 of them (50%) confirmed the mutation; 4 cases had more than 10% TC, and all showed 100% reliability with the tumor tissue. Conclusions: Cytology is a highly reliable method for determining the immunophenotype of HGSOC and BRCA1/2 mutation if more than 10% of tumor cells are present in the ascites. This may present an additional non-invasive clinical approach for fast and effective diagnose in the future, especially in inoperable conditions or relapses.

Keywords: cytology, ascites, high-grade serous ovarian cancer, immunophenotype, BRCA1/2

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Authors: Ouahab Kadri, Leila Hayet Mouss

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In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system.

Keywords: ant colony algorithms, complex and dynamic systems, diagnosis, classification, optimization

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Authors: M. Budakoglu, M. Karaman

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Lake Acıgöl is located in area with limited influences from urban and industrial pollution sources, there is nevertheless a need to understand all potential lithological and anthropogenic sources of priority contaminants in this closed basin. This study discusses vertical and horizontal distribution pattern of major, trace elements of recent lake sediments to better understand their current geochemical analog with lithological units in the Lake Acıgöl basin. This study also provides reliable background levels for the region by the detailed surfaced lithological units data. The detail results of surface, subsurface and shallow core sediments from these relatively unperturbed ecosystems, highlight its importance as conservation area, despite the high-scale industrial salt production activity. While P2O5/TiO2 versus MgO/CaO classification diagram indicate magmatic and sedimentary origin of lake sediment, Log(SiO2/Al2O3) versus Log(Na2O/K2O) classification diagrams express lithological assemblages of shale, iron-shale, vacke and arkose. The plot between TiO2 vs. SiO2 and P2O5/TiO2 vs. MgO/CaO also supports the origin of the primary magma source. The average compositions of the 20 different lithological units used as a proxy for geochemical background in the study area. As expected from weathered rock materials, there is a large variation in the major element content for all analyzed lake samples. The A-CN-K and A-CNK-FM ternary diagrams were used to deduce weathering trends. Surface and subsurface sediments display an intense weathering history according to these ternary diagrams. The most of the sediments samples plot around UCC and TTG, suggesting a low to moderate weathering history for the provenance. The sediments plot in a region clearly suggesting relative similar contents in Al2O3, CaO, Na2O, and K2O from those of lithological samples.

Keywords: Lake Acıgöl, recent lake sediment, geochemical speciation of major and trace elements, heavy metals, Denizli, Turkey

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Authors: Samhita Mummadi, Sree Divya Nagalli, Harshini Vemuri, Saketh Charan Nakka, Sumesh K. J.

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One of the most significant challenges faced by people in today’s digital era is an alarming increase in fraudulent activities on online platforms. The fascination with online shopping to avoid long queues in shopping malls, the availability of a variety of products, and home delivery of goods have paved the way for a rapid increase in vast online shopping platforms. This has had a major impact on increasing fraudulent activities as well. This loop of online shopping and transactions has paved the way for fraudulent users to commit fraud. For instance, consider a store that orders thousands of products all at once, but what’s fishy about this is the massive number of items purchased and their transactions turning out to be fraud, leading to a huge loss for the seller. Considering scenarios like these underscores the urgent need to introduce machine learning approaches to combat fraud in online shopping. By leveraging robust algorithms, namely KNN, Decision Trees, and Random Forest, which are highly effective in generating accurate results, this research endeavors to discern patterns indicative of fraudulent behavior within transactional data. Introducing a comprehensive solution to this problem in order to empower e-commerce administrators in timely fraud detection and prevention is the primary motive and the main focus. In addition to that, sentiment analysis is harnessed in the model so that the e-commerce admin can tailor to the customer’s and consumer’s concerns, feedback, and comments, allowing the admin to improve the user’s experience. The ultimate objective of this study is to ramp up online shopping platforms against fraud and ensure a safer shopping experience. This paper underscores a model accuracy of 84%. All the findings and observations that were noted during our work lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as technologies continue to evolve.

Keywords: behavior analysis, feature selection, Fraudulent pattern recognition, imbalanced classification, transactional anomalies

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