Search results for: brain tumor classification

Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

Procedia PDF Downloads 100

Authors: Sadaf Salek

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Introduction: Cats are susceptible to various health conditions, and one such ailment that can affect their oral health is epulis in their lips. Epulis refers to a benign tumor or growth that can develop in different areas of a cat's mouth, including the lips. While epulis is not life-threatening, it can still cause discomfort and affect a cat's overall quality of life. This essay aims to delve into the causes, symptoms, and treatment options for epulis in cat's lips, shedding light on this lesser-known oral condition. Causes: Epulis in a cat's lips can have several causes. Firstly, genetic predisposition plays a significant role, with certain breeds being more prone to developing these growths. Secondly, chronic irritation to the mouth, such as from dental diseases or foreign objects, can also contribute to the development of epulis. Lastly, hormonal imbalances, specifically an excess of estrogen, have been associated with the occurrence of these tumors in cats. Understanding these causes can help cat owners take preventive measures to reduce the risk of epulis in their feline companions. Symptoms: Identifying the symptoms of epulis in a cat's lips is vital for early intervention and effective treatment. The most common symptoms include swelling, redness, and the presence of a visible growth or lump on the lip. Cats with epulis may also exhibit drooling, difficulty eating, and a reluctance to groom themselves. Any change in eating habits or oral behavior should not be overlooked and prompt a visit to the veterinarian for a thorough examination. Treatment ptions: When it comes to treating epulis in a cat's lips, various options are available, depending on the size, location, and characteristics of the growth. The primary treatment involves surgical removal of the tumor. This procedure should be performed by a qualified veterinarian, ensuring complete excision of the mass while preserving as much healthy tissue as possible. In some cases, radiation therapy may be necessary, especially if the tumor is large or aggressive. Additionally, a veterinarian may recommend oral hygiene care and regular dental cleaning to prevent further growths and maintain the cat's oral health. Prevention and Care: Preventing epulis in a cat's lips is not always possible, especially if genetic factors are involved. However, certain preventive measures can minimize the risk of these growths. Maintaining good oral hygiene through regular brushing and the use of appropriate dental products can help prevent chronic irritation and dental diseases. Routine veterinary check-ups should also include thorough oral examinations to detect any abnormal growths or changes in the mouth at an early stage. Pet owners should be observant and seek veterinary care promptly for any signs of discomfort or changes in eating habits. Conclusion: Epulis in a cat's lips is a condition that requires attention and proper treatment. Understanding the causes, identifying symptoms, and exploring treatment options are of utmost importance to help improve a cat's oral health and overall well-being.

Keywords: fibroma, cat, lip, epulis

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Liu Wei-Hsien, Chuang Hsiao-Li, Huang Yen-Te, Wu Chien-Chen, Chou Geng-Ting, Tsai Ying-Chieh

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Intestinal microflora play an important role in communication along the gut-brain axis. Probiotics, defined as live bacteria or bacterial products, confer a significant health benefit to the host. Here we administered Lactobacillus plantarum PS128 (PS128) to the germ-free (GF) mouse to investigate the impact of the gut-brain axis on emotional behavior. Administration of live PS128 significantly increased the total distance traveled in the open field test; it decreased the time spent in the closed arm and increased the time spent and total entries into the open arm in the elevated plus maze. In contrast, heat-killed PS128 caused no significant changes in the GF mice. Treatment with live PS128 significantly increased levels of both serotonin and dopamine in the striatum, but not in the prefrontal cortex or hippocampus. However, live PS128 did not alter pro- or anti-inflammatory cytokine production by mitogen-stimulated splenocytes. The above data indicate that the normalization of emotional behavior correlated with monoamine neurotransmission, but not with immune activity. Our findings suggest that daily intake of the probiotic PS128 could ameliorate neuropsychiatric disorders such as anxiety and excessive psychological stress.

Keywords: dopamine, hypothalamic-pituitary-adrenal axis, intestinal microflora, serotonin

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Authors: Zainuddin Zakaria, Sun Woh Lye

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Despite the continuous advancements in automated conflict resolution tools, there is still a low rate of adoption of automation from Air Traffic Control Officers (ATCOs). Trust or acceptance in these tools and conformance to the individual ATCO preferences in strategy execution for conflict resolution are two key factors that impact their use. This paper proposes a methodology to unearth and classify ATCO conflict resolution strategies from simulator data of trained and qualified ATCOs. The methodology involves the extraction of ATCO executive control actions and the establishment of a system of strategy resolution classification based on ATCO radar commands and prevailing flight parameters in deconflicting a pair of aircraft. Six main strategies used to handle various categories of conflict were identified and discussed. It was found that ATCOs were about twice more likely to choose only vertical maneuvers in conflict resolution compared to horizontal maneuvers or a combination of both vertical and horizontal maneuvers.

Keywords: air traffic control strategies, conflict resolution, simulator data, strategy classification system

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Authors: Eashwari Dahoe, Jody Scheuer, Harm-Jan Vink

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Alzheimer's disease is a progressive brain condition and is the most common form of dementia. Dementia is a global concern. It is an increasing crisis due to the worldwide aging population. The disease alters the body in different stages leading to several issues. The most common issues result in memory loss, responsive decline, and social decline. During the various stages, the dementia patient must be supported more in performing daily tasks. Eventually, the patient will have to be cared for entirely. There are many efforts in various domains to support this brain condition. This study focuses on the connection between three generations of solutions in the domain of technology and the struggles they tackle. To gather information regarding the struggles seniors with dementia face data has been acknowledged through reading scientific articles. The struggles are extracted from these articles and classified into various category struggles. To gather information regarding the three generations of technology data has been acknowledged through reading scientific articles regarding the generations. After understanding the difference between the three generations, international technological solutions from the past 20 years are connected to the generation they fit. This info is mainly collected through research on companies that aim to improve the lives of senior citizens with early stages of dementia. Eventually, the technological solutions (divided by generations) are linked to the struggles they tackle. By connecting the struggles and the solutions , it is hoped that this paper contributes to an informative overview of the currently available technological solutions and the struggles they tackle.

Keywords: Alzheimer’s disease, technological solutions to support dementia, struggles of seniors with dementia, struggles of dementia

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Authors: Lola T. Alimkhodjaeva, Lola T. Zakirova, Soniya S. Ziyavidenova

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Background: Breast cancer occupies the first place among the cancer in women in the world. It is urgent today to study the role of molecular markers which are capable of predicting the dynamics and outcome of the disease. The aim of this study is to define the prognostic value of the content of estrogen receptor (ER), progesterone receptor (PgR), and amplification of HER-2 / neu oncoprotein by studying 3 and 5-year overall and relapse-free survival in 470 patients with primary operable and 280 patients with locally–advanced breast cancer. Materials and methods: Study results of 3 and 5-year overall and relapse-free survival, depending on the content of RE, PgR in primary operable patients showed that ER positive (+) and PgR (+) survival was 100 (96.2%) and 97.3 (94.6%), for ER negative (-) and PgR (-) - 69.2 (60.3%) and 65.4 (57.7%), for ER positive (+) and negative PgR (-) 87.4 (80.1%) and 81.5 (79.3%), for ER negative (-) and positive PgR (+) - 97.4 (93.4%) and 90.4 (88.5%), respectively. Survival results depended also on the level of HER-2 / neu expression. In patients with HER-2 / neu negative the survival rates were as follows: 98.6 (94.7%) and 96.2 (92.3%). In group of patients with the level of HER-2 / neu (2+) expression these figures were: 45.3 (44.3%) and 45.1 (40.2%), and in group of patients with the level of HER-2 / neu (3+) expression - 41.2 (33.1%) and 34.3 (29.4%). The combination of ER negative (-), PgR (-), HER-2 / neu (-) they were 27.2 (25.4%) and 19.5 (15.3%), respectively. In patients with locally-advanced breast cancer the results of 3 and 5-year OS and RFS for ER (+) and PgR (+) were 76.3 (69.3%) and 62.2 (61.4%), for ER (-) and RP (-) 29.1 (23.7%) and 18.3 (12.6%), for ER (+) and PgR (-) 61.2 (47.2%) and 39.4 (25.6%), for ER (-) and PgR (+) 54.3 (43.1%) and 41.3 (18.3%), respectively. The level of HER-2 / neu expression also affected the survival results. Therefore, in HER-2/ neu negative patients the survival rate was 74.1 (67.6%) and 65.1 (57.3%), with the level of expression (2+) 20.4 (14.2%) and 8.6 (6.4%), with the level of expression (3+) 6.2 (3.1%) and 1.2 (1.5%), respectively. The combination for ER, PgR, HER-2 / neu negative was 22.1 (14.3%) and 8.4 (1.2%). Conclusion: Thus, the presence of steroid hormone receptors in breast tumor tissues at primary operable and locally- advanced process as the lack of HER-2/neu oncoprotein correlates with the highest rates of 3- and 5-year overall and relapse-free survival. The absence of steroid hormone receptors as well as of HER-2/neu overexpression in malignant breast tissues significantly degrades the 3- and 5-year overall and relapse-free survival. Tumors with ER, PgR and HER-2/neu negative have the most unfavorable prognostics.

Keywords: breast cancer, estrogen receptor, oncoprotein, progesterone receptor

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Authors: Mohammadjavad Sotoudeheian

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COVID-19, which began in December 2019, uses the angiotensin-converting enzyme 2 (ACE2) receptor to enter and spread through the cells. ACE2 mRNA is present in almost every organ, including nasopharynx, lung, as well as the brain. Ports of entry of SARS-CoV-2 into the central nervous system (CNS) may include arterial circulation, while viremia is remarkable. However, it is imperious to develop neurological symptoms evaluation CSF analysis in patients with COVID-19, but theoretically, ACE2 receptors are expressed in cerebellar cells and may be a target for SARS-CoV-2 infection in the brain. Recent evidence agrees that SARS-CoV-2 can impact the brain through direct and indirect injury. Two biomarkers for CNS injury, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL) detected in the plasma of patients with COVID-19. NFL, an axonal protein expressed in neurons, is related to axonal neurodegeneration, and GFAP is over-expressed in CNS inflammation. GFAP cytoplasmic accumulation causes Schwan cells to misfunction, so affects myelin generation, reduces neuroskeletal support over NfLs during CNS inflammation, and leads to axonal degeneration. Interleukin-6 (IL-6), which extensively over-express due to interleukin storm during COVID-19 inflammation, regulates gene expression, as well as GFAP through STAT molecular pathway. IL-6 also impresses the phosphoinositide 3-kinase (PI3K)/STAT/smads pathway. The PI3K/ protein kinase B (Akt) pathway is the main modulator upstream of the mammalian target of rapamycin (mTOR), and alterations in this pathway are common in neurodegenerative diseases. Most neurodegenerative diseases show a disruption of autophagic function and display an abnormal increase in protein aggregation that promotes cellular death. Therefore, induction of autophagy has been recommended as a rational approach to help neurons clear abnormal protein aggregates and survive. The mTOR is a major regulator of the autophagic process and is regulated by cellular stressors. The mTORC1 pathway and mTORC2, as complementary and important elements in mTORC1 signaling, have become relevant in the regulation of the autophagic process and cellular survival through the extracellular signal-regulated kinase (ERK) pathway.

Keywords: mTORC1, COVID-19, PI3K, autophagy, neurodegeneration

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Authors: Esra Turker, Umit Hakan Yildiz, Ahu Arslan Yildiz

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The key of regenerative medicine is mimicking natural three dimensional (3D) microenvironment of tissues by utilizing appropriate biomaterials. In this study, a synthetic biodegradable polymer; poly (L-lactide-co-ε-caprolactone) (PLLCL) and a natural polymer; collagen was used to mimic the biochemical structure of the natural extracellular matrix (ECM), and by means of electrospinning technique the real physical structure of ECM has mimicked. PLLCL/Collagen biocomposite scaffolds enables cell attachment, proliferation and nutrient transport through fabrication of micro to nanometer scale nanofibers. Biocomposite materials are commonly preferred due to limitations of physical and biocompatible properties of natural and synthetic materials. Combination of both materials improves the strength, degradation and biocompatibility of scaffold. Literature studies have shown that collagen is mostly solved with heavy chemicals, which is not suitable for cell culturing. To overcome this problem, a new approach has been developed in this study where polyvinylpyrrolidone (PVP) is used as co-electrospinning agent. PVP is preferred due to its water solubility, so PLLCL/collagen biocomposite scaffold can be easily and rapidly produced. Hydrolytic and enzymatic biodegradation as well as mechanical strength of scaffolds were examined in vitro. Cell adhesion, proliferation and cell morphology characterization studies have been performed as well. Further, on-chip drug screening analysis has been performed over 3D tumor models. Overall, the developed biocomposite scaffold was used for 3D tumor model formation and obtained results confirmed that developed model could be used for drug screening studies to predict clinical efficacy of a drug.

Keywords: biomaterials, 3D cell culture, drug screening, electrospinning, lab-on-a-chip, tissue engineering

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Authors: Sara Assi, Berthe Hayar, Claudio Pisano, Nadine Darwiche, Walid Saad

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Nanomedicine, the application of nanotechnology to medicine, is an emerging discipline that has gained significant attention in recent years. Current breakthroughs in nanomedicine have paved the way to develop effective drug delivery systems that can be used to target cancer. The use of nanotechnology provides effective drug delivery, enhanced stability, bioavailability, and permeability, thereby minimizing drug dosage and toxicity. As such, the use of nanoparticle (NP) formulations in drug delivery has been applied in various cancer models and have shown to improve the ability of drugs to reach specific targeted sites in a controlled manner. Cancer is one of the major causes of death worldwide; in particular, colorectal cancer (CRC) is the third most common type of cancer diagnosed amongst men and women and the second leading cause of cancer related deaths, highlighting the need for novel therapies. Retinoids, consisting of natural and synthetic derivatives, are a class of chemical compounds that have shown promise in preclinical and clinical cancer settings. However, retinoids are limited by their toxicity and resistance to treatment. To overcome this resistance, various synthetic retinoids have been developed, including the adamantyl retinoid ST1926, which is a potent anti-cancer agent. However, due to its limited bioavailability, the development of ST1926 has been restricted in phase I clinical trials. We have previously investigated the preclinical efficacy of ST1926 in CRC models. ST1926 displayed potent inhibitory and apoptotic effects in CRC cell lines by inducing early DNA damage and apoptosis. ST1926 significantly reduced the tumor doubling time and tumor burden in a xenograft CRC model. Therefore, we developed ST1926-NPs and assessed their efficacy in CRC models. ST1926-NPs were produced using Flash NanoPrecipitation with the amphiphilic diblock copolymer polystyrene-b-ethylene oxide and cholesterol as a co-stabilizer. ST1926 was formulated into NPs with a drug to polymer mass ratio of 1:2, providing a stable formulation for one week. The contin ST1926-NP diameter was 100 nm, with a polydispersity index of 0.245. Using the MTT cell viability assay, ST1926-NP exhibited potent anti-growth activities as naked ST1926 in HCT116 cells, at pharmacologically achievable concentrations. Future studies will be performed to study the anti-tumor activities and mechanism of action of ST1926-NPs in a xenograft mouse model and to detect the compound and its glucuroconjugated form in the plasma of mice. Ultimately, our studies will support the use of ST1926-NP formulations in enhancing the stability and bioavailability of ST1926 in CRC.

Keywords: nanoparticles, drug delivery, colorectal cancer, retinoids

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Authors: Razak Zakariya, Fazliana Mustajap, Lenny Sharinee Sakai

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A sediment map is quite important in the marine environment. The sediment itself contains thousands of information that can be used for other research. This study was conducted by using a multibeam echo sounder Reson T20 on 15 August 2020 at the Karang Sela (coral reef area) at Pulau Bidong. The study aims to identify the sediment type around the coral reef by using bathymetry and backscatter data. The sediment in the study area was collected as ground truthing data to verify the classification of the seabed. A dry sieving method was used to analyze the sediment sample by using a sieve shaker. PDS 2000 software was used for data acquisition, and Qimera QPS version 2.4.5 was used for processing the bathymetry data. Meanwhile, FMGT QPS version 7.10 processes the backscatter data. Then, backscatter data were analyzed by using the maximum likelihood classification tool in ArcGIS version 10.8 software. The result identified three types of sediments around the coral which were very coarse sand, coarse sand, and medium sand.

Keywords: sediment type, MBES echo sounder, backscatter, ArcGIS

Procedia PDF Downloads 80

Authors: Sarpong Boateng, Rohit Balasundaram, Akua Afrah Amoah

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Introduction: Racial and Gender disparities have been found to be independently associated with Salivary Gland Cancers (SGCs) survival; however, to our best knowledge, there are no previous studies on the interplay of these social determinants on the prognosis of SGCs. The objective of this study was to examine the joint effect of race and gender on the survival of SGCs. Methods: We analyzed survival outcomes of 13,547 histologically confirmed cases of SGCs using the Surveillance Epidemiology and End Results (SEER) database (2004 to 2015). Multivariable Cox regression analysis and Kaplan-Meier curves were used to estimate hazard ratios (HR) after controlling for age, tumor characteristics, treatment type and year of diagnosis. Results: 73.5% of the participants were whites, 8.5% were blacks, 10.1% were Hispanics and 58.5% were males. Overall, males had poorer survival than females (HR = 1.16, p=0.003). In the adjusted multivariable model, there were no significant differences in survival by race. However, the interaction of gender and race was statistically significant (p=0.01) in Hispanic males. Thus, compared to White females (reference), Hispanic females had significantly better survival (HR=0.53), whiles Hispanic males had worse survival outcomes (HR=1.82) for SGCs. Conclusions: Our results show significant interactions between race and gender, with racial disparities varying across the different genders for SGCs survival. This study indicates that racial and gender differences are crucial factors to be considered in the prognostic counseling and management of patients with SGCs. Biologic factors, tumor genetic characteristics, chemotherapy, lifestyle, environmental exposures, and socioeconomic and dietary factors are potential yet proven reasons that could account for racial and gender differences in the survival of SGCs.

Keywords: salivary, cancer, survival, disparity, race, gender, SEER

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Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

Procedia PDF Downloads 378

Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: clustering, fake news detection, feature selection, machine learning, social media, support vector machine

Procedia PDF Downloads 172

Authors: Akanksha Bhatnagar, Sandhya Kortegare, Felice Elefant

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Context: Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by progressive cognitive decline and memory loss. The cause of AD is not fully understood, but it is thought to be caused by a combination of genetic, environmental, and lifestyle factors. One of the hallmarks of AD is the loss of neurons in the hippocampus, a brain region that is important for memory and learning. This loss of neurons is thought to be caused by a decrease in histone acetylation, which is a process that regulates gene expression. Research Aim: The research aim of the study was to develop mall molecule compounds that can enhance the activity of Tip60, a histone acetyltransferase that is important for memory and learning. Methodology/Analysis: The researchers used in silico structural modeling and a pharmacophore-based virtual screening approach to design and synthesize small molecule compounds strongly predicted to target and enhance Tip60’s HAT activity. The compounds were then tested in vitro and in vivo to assess their ability to enhance Tip60 activity and rescue cognitive deficits in AD models. Findings: The researchers found that several of the compounds were able to enhance Tip60 activity and rescue cognitive deficits in AD models. The compounds were also developed to cross the blood-brain barrier, which is an important factor for the development of potential AD therapeutics. Theoretical Importance: The findings of this study suggest that Tip60 HAT activators have the potential to be developed as therapeutic agents for AD. The compounds are specific to Tip60, which suggests that they may have fewer side effects than other HDAC inhibitors. Additionally, the compounds are able to cross the blood-brain barrier, which is a major hurdle for the development of AD therapeutics. Data Collection: The study collected data from a variety of sources, including in vitro assays and animal models. The in vitro assays assessed the ability of compounds to enhance Tip60 activity using histone acetyltransferase (HAT) enzyme assays and chromatin immunoprecipitation assays. Animal models were used to assess the ability of the compounds to rescue cognitive deficits in AD models using a variety of behavioral tests, including locomotor ability, sensory learning, and recognition tasks. The human clinical trials will be used to assess the safety and efficacy of the compounds in humans. Questions: The question addressed by this study was whether Tip60 HAT activators could be developed as therapeutic agents for AD. Conclusions: The findings of this study suggest that Tip60 HAT activators have the potential to be developed as therapeutic agents for AD. The compounds are specific to Tip60, which suggests that they may have fewer side effects than other HDAC inhibitors. Additionally, the compounds are able to cross the blood-brain barrier, which is a major hurdle for the development of AD therapeutics. Further research is needed to confirm the safety and efficacy of these compounds in humans.

Keywords: Alzheimer's disease, cognition, neuroepigenetics, drug discovery

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Farhan Sohail, Gul Shahnaz Irshad Hussain, Shoaib Sarwar, Ibrahim Javed, Zajif Hussain, Akhtar Nadhman

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The present study was aimed to develop a hybrid nanocarrier (NC) system with enhanced membrane permeability, bioavailability and targeted delivery of Docetaxel (DTX) in breast cancer. Hybrid NC’s based on folic acid (FA) grafted thiolated chitosan (TCS) enveloped liposomes were prepared with DTX and evaluated in-vitro and in-vivo for their enhanced permeability and bioavailability. Physicochemical characterization of NC’s including particle size, morphology, zeta potential, FTIR, DSC, PXRD, encapsulation efficiency and drug release from NC’s was determined in vitro. Permeation enhancement and p-gp inhibition were performed through everted sac method on freshly excised rat intestine which indicated that permeation was enhanced 5 times as compared to pure DTX and the hybrid NC’s were strongly able to inhibit the p-gp activity as well. In-vitro cytotoxicity and tumor targeting was done using MDA-MB-231 cell line. The stability study of the formulations performed for 3 months showed the improved stability of FA-TCS enveloped liposomes in terms of its particles size, zeta potential and encapsulation efficiency as compared to TCS NP’s and liposomes. The pharmacokinetic study was performed in vivo using rabbits. The oral bioavailability and AUC0-96 was increased 10.07 folds with hybrid NC’s as compared to positive control. Half-life (t1/2) was increased 4 times (58.76 hrs) as compared to positive control (17.72 hrs). Conclusively, it is suggested that FA-TCS enveloped liposomes have strong potential to enhance permeability and bioavailability of hydrophobic drugs after oral administration and tumor targeting.

Keywords: docetaxel, coated liposome, permeation enhancement, oral bioavailability

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Authors: Mojtaba Barzegar, Alireza Shirazi, Saied Rabi Mahdavi

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Intraoperative radiation therapy (IORT) is an innovative treatment modality that the delivery of a large single dose of radiation to the tumor bed during the surgery. The radiotherapy success depends on the absorbed dose delivered to the tumor. The achievement better accuracy in patient treatment depends upon the measured dose by standard dosimeter such as ionization chamber, but because of the high density of electric charge/pulse produced by the accelerator in the ionization chamber volume, the standard correction factor for ion recombination Ksat calculated with the classic two-voltage method is overestimated so the use of dose/pulse independent dosimeters such as chemical Fricke and ethanol chlorobenzene (ECB) dosimeters have been suggested. Dose measurement is usually calculated and calibrated in the Zmax. Ksat calculated by comparison of ion chamber response and ECB dosimeter at each applicator degree, size, and dose. The relative output factors for IORT applicators have been calculated and compared with experimentally determined values and the results simulated by Monte Carlo software. The absorbed doses have been calculated and measured with statistical uncertainties less than 0.7% and 2.5% consecutively. The relative differences between calculated and measured OF’s were up to 2.5%, for major OF’s the agreement was better. In these conditions, together with the relative absorbed dose calculations, the OF’s could be considered as an indication that the IORT electron beams have been well simulated. These investigations demonstrate the utility of the full Monte Carlo simulation of accelerator head with ECB dosimeter allow us to obtain detailed information of clinical IORT beams.

Keywords: intra operative radiotherapy, ethanol chlorobenzene, ksat, output factor, monte carlo simulation

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Authors: L. Lindsay, S. A. Coleman, D. Kerr, B. J. Taylor, A. Moorhead

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Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.

Keywords: classification, falls, health risk factors, machine learning, older adults

Procedia PDF Downloads 140

Authors: Ting-I Lin

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Purpose: This article explores the care experience of a 34-year-old female breast cancer patient who was admitted to the intensive care unit after undergoing a modified radical mastectomy. The patient discovered a lump in her right breast during a self-examination and, after mammography and ultrasound-guided biopsy, was diagnosed with a malignant tumor in the right breast. The tumor measured 1.5 x 1.4 x 2 cm, and the patient underwent a modified radical mastectomy. Postoperatively, she exhibited feelings of inferiority due to changes in her appearance. Method: During the care period, we engaged in conversations, observations, and active listening, using Gordon's Eleven Functional Health Patterns for a comprehensive assessment. In collaboration with the critical care team, a psychologist, and an oncology case manager, we conducted an interdisciplinary discussion and reached a consensus on key nursing issues. These included pain related to postoperative tumor excision and disturbed body image due to changes in appearance after surgery. Result: During the care period, a private space was provided to encourage the patient to express her feelings about her altered body image. Communication was conducted through active listening and a non-judgmental approach. The patient's anxiety level, as measured by the depression and anxiety scale, decreased from moderate to mild, and she was able to sleep for 6-8 hours at night. The oncology case manager was invited to provide education on breast reconstruction using breast models and videos to both the patient and her husband. This helped rebuild the patient's confidence. With the patient's consent, a support group was arranged where a peer with a similar experience shared her journey, offering emotional support and encouragement. This helped alleviate the psychological stress and shock caused by the cancer diagnosis. Additionally, pain management was achieved through adjusting the dosage of analgesics, administering Ultracet 37.5 mg/325 mg 1# Q6H PO, along with distraction techniques and acupressure therapy. These interventions helped the patient relax and alleviate discomfort, maintaining her pain score at a manageable level of 3, indicating mild pain. Conclusion: Disturbance in body image can cause significant psychological stress for patients. Through support group discussions, encouraging patients to express their feelings, and providing appropriate education on breast reconstruction and dressing techniques, the patient's self-concept was positively reinforced, and her emotions were stabilized. This led to renewed self-worth and confidence.

Keywords: breast cancer, modified radical mastectomy, acupressure therapy, Gordon's 11 functional health patterns

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Sarah Crawford, Gerry Lesley

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This research is a pre-clinical assessment of anti-cancer effects of novel non-steroidal diethyl stilbestrol-like estrogen analogs in estrogen-receptor positive/ progesterone-receptor positive human breast cancer microtumors of MCF 7 cell line. Tamoxifen analog formulation (Tam A1) was used as a single agent or in combination with therapeutic concentrations of 4-hydroxytamoxifen, currently used as a long-term treatment for the prevention of breast cancer recurrence in women with estrogen receptor positive/ progesterone receptor positive malignancies. At concentrations ranging from 30-50 microM, Tam A1 induced microtumor disaggregation and cell death. Incremental cytotoxic effects correlated with increasing concentrations of Tam A1. Live tumor microscopy showed that microtumos displayed diffuse borders and substrate-attached cells were rounded-up and poorly adherent. A complete cytotoxic effect was observed using 40-50 microM Tam A1 with time course kinetics similar to 4-hydroxytamoxifen. Combined treatment with TamA1 (30-50 microM) and 4-hydroxytamoxifen (10-15 microM) induced a highly cytotoxic, synergistic combined treatment response that was more rapid and complete than using 4-hydroxytamoxifen as a single agent therapeutic. Microtumors completely dispersed or formed necrotic foci indicating a highly cytotoxic combined treatment response. Moreover, breast cancer microtumors treated with both 4-hydroxytamoxifen and Tam A1 displayed lower levels of long-term post-treatment regrowth, a critical parameter of primary drug resistance, than observed for 4-hydroxytamoxifen when used as a single agent therapeutic. Tumor regrowth at 6 weeks post-treatment with either single agent 4-hydroxy tamoxifen, Tam A1 or a combined treatment was assessed for the development of drug resistance. Breast cancer cells treated with both 4-hydroxytamoxifen and Tam A1 displayed significantly lower levels of post-treatment regrowth, indicative of decreased drug resistance, than observed for either single treatment modality. The preclinical data suggest that combined treatment involving the use of tamoxifen analogs may be a novel clinical approach for long-term maintenance therapy in patients with estrogen-receptor positive/progesterone-receptor positive breast cancer receiving hormonal therapy to prevent disease recurrence. Detailed data on time-course, IC50 and tumor regrowth assays post- treatment as well as a proposed mechanism of action to account for observed synergistic drug effects will be presented.

Keywords: 4-hydroxytamoxifen, tamoxifen analog, drug-resistance, microtumors

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Authors: C. Román, M. Guevara, P. Salas, D. Duclap, J. Houenou, C. Poupon, J. F. Mangin, P. Guevara

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Diffusion Magnetic Resonance Imaging (dMRI) allows the non-invasive study of human brain white matter. From diffusion data, it is possible to reconstruct fiber trajectories using tractography algorithms. Our previous work consists in an automatic method for the identification of short association bundles of the superficial white matter (SWM), based on a whole brain inter-subject hierarchical clustering applied to a HARDI database. The method finds representative clusters of similar fibers, belonging to a group of subjects, according to a distance measure between fibers, using a non-linear registration (DTI-TK). The algorithm performs an automatic labeling based on the anatomy, defined by a cortex mesh parcelated with FreeSurfer software. The clustering was applied to two independent groups of 37 subjects. The clusters resulting from both groups were compared using a restrictive threshold of mean distance between each pair of bundles from different groups, in order to keep reproducible connections. In the left hemisphere, 48 reproducible bundles were found, while 43 bundles where found in the right hemisphere. An inter-hemispheric bundle correspondence was then applied. The symmetric horizontal reflection of the right bundles was calculated, in order to obtain the position of them in the left hemisphere. Next, the intersection between similar bundles was calculated. The pairs of bundles with a fiber intersection percentage higher than 50% were considered similar. The similar bundles between both hemispheres were fused and symmetrized. We obtained 30 common bundles between hemispheres. An atlas was created with the resulting bundles and used to segment 78 new subjects from another HARDI database, using a distance threshold between 6-8 mm according to the bundle length. Finally, a laterality index was calculated based on the bundle volume. Seven bundles of the atlas presented right laterality (IP_SP_1i, LO_LO_1i, Op_Tr_0i, PoC_PoC_0i, PoC_PreC_2i, PreC_SM_0i, y RoMF_RoMF_0i) and one presented left laterality (IP_SP_2i), there is no tendency of lateralization according to the brain region. Many factors can affect the results, like tractography artifacts, subject registration, and bundle segmentation. Further studies are necessary in order to establish the influence of these factors and evaluate SWM laterality.

Keywords: dMRI, hierarchical clustering, lateralization index, tractography

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Aaron Walayat

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Protection of the environment in international law has been one of the most developed in the context of international humanitarian law. This paper examines the history of the protection of the environment in times of armed conflict, beginning with the traditional notion of restraint observed in antiquity towards the obligation to protect the environment, examining the treaties and agreements, both binding and non-binding which have contributed to environmental protection in war. The paper begins with a discussion of the ancient concept of restraint. This section examines the social norms in favor of protection of the environment as observed in the Bible, Greco-Roman mythology, and even more contemporary literature. The study of the traditional rejection of total war establishes the social foundation on which the current legal regime has stemmed. The paper then studies the principle of restraint as codified in international humanitarian law. It mainly examines Additional Protocol I of the Geneva Convention of 1949 and existing international law concerning civilian objects and the principles of international humanitarian law in the classification between civilian objects and military objectives. The paper then explores the environment’s classification as both a military objective and as a civilian object as well as explores arguments in favor of the classification of the whole environment as a civilian object. The paper will then discuss the current legal regime surrounding the protection of the environment, discussing some declarations and conventions including the 1868 Declaration of St. Petersburg, the 1907 Hague Convention No. IV, the Geneva Conventions, and the 1976 Environmental Modification Convention. The paper concludes with the outline noting the movement from codification of the principles of restraint into the various treaties, agreements, and declarations of the current regime of international humanitarian law. This paper provides an analysis of the history and significance of the relationship between international humanitarian law as a major contributor to the growing field of international environmental law.

Keywords: armed conflict, environment, legal regime, restraint

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Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

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Authors: Youhang Zhou, Weimin Zeng, Qi Xie

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Abstract: In order to solve the wear recognition problem of the machine tool guide surface, a new machine tool guide surface recognition method based on the radar-graph barycentre feature is presented in this paper. Firstly, the gray mean value, skewness, projection variance, flat degrees and kurtosis features of the guide surface image data are defined as primary characteristics. Secondly, data Visualization technology based on radar graph is used. The visual barycentre graphical feature is demonstrated based on the radar plot of multi-dimensional data. Thirdly, a classifier based on the support vector machine technology is used, the radar-graph barycentre feature and wear original feature are put into the classifier separately for classification and comparative analysis of classification and experiment results. The calculation and experimental results show that the method based on the radar-graph barycentre feature can detect the guide surface effectively.

Keywords: guide surface, wear defects, feature extraction, data visualization

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Authors: Mostafa Elbaba

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Because childhood renal diseases are grossly different compared to adult diseases, pediatric nephrology was founded as a specialty in 1965. Renal pathology specialty was introduced at the London Ciba Symposium in 1961. The history of renal pathology can be divided into two eras: one starting in the 1650s with the invention of the microscope, the second in the 1950s with the implementation of renal biopsy, and the presence of electron microscopy and immunofluorescence study. Prior to the 1950s, the study of diseased human kidneys was restricted to postmortem examination by gross pathology. In 1827, Richard Bright first described his triad of kidney disease, which was confirmed by morbid kidney changes at autopsy. In 1905 Friedrich Mueller coined the term “nephrosis” describing the inflammatory form of “degenerative” diseases, and later F. Munk added the term “lipoid nephrosis”. The most profound influence on renal diseases’ classification came from the publication of Volhard and Fahr in 1914. In 1899, Carl Max Wilhelm Wilms described Wilms' tumor of the kidneys in children. Chronic pyelonephritis was a popular renal diagnosis and the most common cause of uremia until the 1960s. Although kidney biopsy had been used early in the 1930s for renal tumors, the earliest reports of its use in the diagnosis of medical kidney disease were by Iversen and Brun in 1951, followed by Alwall in 1952, then by Pardo in 1953. The earliest intentional renal biopsies were done in 1944 by Nils Alwall, while the procedure was abandoned after the death of one of his 13 patients who biopsied. In 1950, Antonino Perez-Ara attempted renal biopsies, but his results were missed because of an unpopular journal publication. In the year 1951, Claus Brun and Poul Iverson developed the biopsy procedure using an aspiration technique. Popularizing renal biopsy practice is accredited to Robert Kark, who published his distinct work in 1954. He perfected the technique of renal biopsy in the prone position using the Vim-Silverman needle and used intravenous pyelography to improve the localization of the kidney.

Keywords: history, medicine, nephrology, pediatrics, pathology

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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