Search results for: Naïve Bayesian

Authors: Baladitya Swaika, Rahul Khatry

Abstract:

Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

Procedia PDF Downloads 153

Authors: Roengchai Tansuchat, Woraphon Yamaka, Paravee Maneejuk

Abstract:

From the past recent, the global financial crisis, economic instability, and large fluctuation in agricultural commodity price have led to increased concerns about the volatility transmission among them. The problem is further exacerbated by commodities volatility caused by other commodity price fluctuations, hence the decision on hedging strategy has become both costly and useless. Thus, this paper is conducted to analysis the volatility spillover effect among major agriculture including corn, soybeans, wheat and rice, to help the commodity suppliers hedge their portfolios, and manage the risk and co-volatility of them. We provide a switching regime approach to analyzing the issue of volatility spillovers in different economic conditions, namely upturn and downturn economic. In particular, we investigate relationships and volatility transmissions between these commodities in different economic conditions. We purposed a Copula-based multivariate Markov Switching GARCH model with two regimes that depend on an economic conditions and perform simulation study to check the accuracy of our proposed model. In this study, the correlation term in the cross-hedge ratio is obtained from six copula families – two elliptical copulas (Gaussian and Student-t) and four Archimedean copulas (Clayton, Gumbel, Frank, and Joe). We use one-step maximum likelihood estimation techniques to estimate our models and compare the performance of these copula using Akaike information criterion (AIC) and Bayesian information criteria (BIC). In the application study of agriculture commodities, the weekly data used are conducted from 4 January 2005 to 1 September 2016, covering 612 observations. The empirical results indicate that the volatility spillover effects among cereal futures are different, as response of different economic condition. In addition, the results of hedge effectiveness will also suggest the optimal cross hedge strategies in different economic condition especially upturn and downturn economic.

Keywords: agricultural commodity futures, cereal, cross-hedge, spillover effect, switching regime approach

Procedia PDF Downloads 169

Authors: Elcin Timur Cakmak, Ayse Oguzlar

Abstract:

This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

Procedia PDF Downloads 51

Authors: Aishi Kundu, Elhan Raza

Abstract:

"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

Procedia PDF Downloads 75

Authors: L. Guevara, Glória L. S., Corea E. E, A. Ramírez-Zamora M., Salinas-Martinez J. A., Angeles-Hernandez J. C.

Abstract:

Mathematical models are useful for identifying the characteristics of sheep lactation curves to develop and implement improved strategies. However, the accuracy of these models is influenced by factors such as the recording regime, mainly the intervals between test day records (TDR). The current study aimed to evaluate the effect of different TDR intervals on the goodness of fit of the Wood model (WM) applied to dairy sheep lactations. A total of 4,494 weekly TDRs from 156 lactations of dairy crossbred sheep were analyzed. Three new databases were generated from the original weekly TDR data (7D), comprising intervals of 14(14D), 21(21D), and 28(28D) days. The parameters of WM were estimated using the “minpack.lm” package in the R software. The shape of the lactation curve (typical and atypical) was defined based on the WM parameters. The goodness of fit was evaluated using the mean square of prediction error (MSPE), Root of MSPE (RMSPE), Akaike´s Information Criterion (AIC), Bayesian´s Information Criterion (BIC), and the coefficient of correlation (r) between the actual and estimated total milk yield (TMY). WM showed an adequate estimate of TMY regardless of the TDR interval (P=0.21) and shape of the lactation curve (P=0.42). However, we found higher values of r for typical curves compared to atypical curves (0.9vs.0.74), with the highest values for the 28D interval (r=0.95). In the same way, we observed an overestimated peak yield (0.92vs.6.6 l) and underestimated time of peak yield (21.5vs.1.46) in atypical curves. The best values of RMSPE were observed for the 28D interval in both lactation curve shapes. The significant lowest values of AIC (P=0.001) and BIC (P=0.001) were shown by the 7D interval for typical and atypical curves. These results represent the first approach to define the adequate interval to record the regime of dairy sheep in Latin America and showed a better fitting for the Wood model using a 7D interval. However, it is possible to obtain good estimates of TMY using a 28D interval, which reduces the sampling frequency and would save additional costs to dairy sheep producers.

Keywords: gamma incomplete, ewes, shape curves, modeling

Procedia PDF Downloads 40

Authors: Balasooriya B. M. C. M., Sujeeva N., Thowfeek Z., Siddiqa Omo, Liyanagunawardana J. E., Jayawardana Saiu, Manathunga S. S., Katulanda G. W.

Abstract:

Introduction and objectives: Adrenal venous sampling (AVS) is the gold standard to discriminate unilateral primary aldosteronism (UPA) from bilateral disease (BPA). AVS is technically demanding and only performed in a limited number of centers worldwide. To the best of our knowledge, Except for one study conducted in India, no other research studies on this area have been conducted in South Asia. This study aimed to evaluate the effectiveness of AVS in the management of primary aldosteronism. Methods: A total of 32 patients who underwent AVS at the National Hospital of Sri Lanka from April 2021 to April 2023 were enrolled. Demographic, clinical and laboratory data were obtained retrospectively. A procedure was considered successful when adequate cannulation of both adrenal veins was demonstrated. Cortisol gradient across the adrenal vein (AV) and the peripheral vein was used to establish the success of venous cannulation. Lateralization was determined by the aldosterone gradient between the two sides. Continuous and categorical variables were summarized with mean, SD, and proportions, respectively. The mean and standard deviation of the contralateral suppression index (CSI) were estimated with an intercept-only Bayesian inference model. Results: Of the 32 patients, the average age was 52.47 +26.14 and 19 (59.4%) were males. Both AVs were successfully cannulated in 12 (37.5%). Among them, lateralization was demonstrated in 11(91.7%), and one was diagnosed as a bilateral disease. There were no total failures. Right AV cannulation was unsuccessful in 18 (56.25%), of which lateralization was demonstrated in 9 (50%), and others were inconclusive. Left AV cannulation was unsuccessful only in 2 (6.25%); one was lateralized, and the other remained inconclusive. The estimated mean of the CSI was 0.33 (89% credible interval 0.11-0.86). Seven patients underwent unilateral adrenalectomy and demonstrated significant improvement in blood pressure during follow-up. Two patients await surgery. Others were treated medically. Conclusions: Despite failure due to procedural difficulties, AVS remained useful in the management of patients with PA. Moreover, the success of the procedure needs experienced hands and advanced equipment to achieve optimal outcomes in PA.

Keywords: adrenal venous sampling, lateralization, contralateral suppression index, primary aldosteronism

Procedia PDF Downloads 28

Authors: Michael Hast

Abstract:

Within their first year, infants can differentiate between objects based on their weight. By at least 5 years children hold consistent weight-related misconceptions about the physical world, such as that heavy things fall faster than lighter ones because of their weight. Such misconceptions are seen as a challenge for science education since they are often highly resistant to change through instruction. Understanding the time point of emergence of such ideas could, therefore, be crucial for early science pedagogy. The paper thus discusses two studies that jointly address the issue by examining young children’s search behaviour in hidden displacement tasks under consideration of relative object weight. In both studies, they were tested with a heavy or a light ball, and they either had information about one of the balls only or both. In Study 1, 88 toddlers aged 2 to 3½ years watched a ball being dropped into a curved tube and were then allowed to search for the ball in three locations – one straight beneath the tube entrance, one where the curved tube lead to, and one that corresponded to neither of the previous outcomes. Success and failure at the task were not impacted by weight of the balls alone in any particular way. However, from around 3 years onwards, relative lightness, gained through having tactile experience of both balls beforehand, enhanced search success. Conversely, relative heaviness increased search errors such that children increasingly searched in the location immediately beneath the tube entry – known as the gravity bias. In Study 2, 60 toddlers aged 2, 2½ and 3 years watched a ball roll down a ramp and behind a screen with four doors, with a barrier placed along the ramp after one of four doors. Toddlers were allowed to open the doors to find the ball. While search accuracy generally increased with age, relative weight did not play a role in 2-year-olds’ search behaviour. Relative lightness improved 2½-year-olds’ searches. At 3 years, both relative lightness and relative heaviness had a significant impact, with the former improving search accuracy and the latter reducing it. Taken together, both studies suggest that between 2 and 3 years of age, relative object weight is increasingly taken into consideration in navigating naïve physical concepts. In particular, it appears to contribute to the early emergence of misconceptions relating to object weight. This insight from developmental psychology research may have consequences for early science education and related pedagogy towards early conceptual change.

Keywords: conceptual development, early science education, intuitive physics, misconceptions, object weight

Procedia PDF Downloads 170

Authors: Gaelle Candel, David Naccache

Abstract:

t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

Procedia PDF Downloads 112

Authors: Sridhar A. Malkaram, Tamer E. Fandy

Abstract:

Purpose: 5-Azacytidine (5AC) and decitabine (DC) are DNA hypomethylating agents. We recently demonstrated that both drugs increase the enzymatic activity of the histone deacetylase enzyme SIRT6. Accordingly, we are comparing the changes H3K9 acetylation changes in the whole genome induced by both drugs using leukemia cells. Description of Methods & Materials: Mononuclear cells from the bone marrow of six de-identified naive acute myeloid leukemia (AML) patients were cultured with either 500 nM of DC or 5AC for 72 h followed by ChIP-Seq analysis using a ChIP-validated acetylated-H3K9 (H3K9ac) antibody. Chip-Seq libraries were prepared from treated and untreated cells using SMARTer ThruPLEX DNA- seq kit (Takara Bio, USA) according to the manufacturer’s instructions. Libraries were purified and size-selected with AMPure XP beads at 1:1 (v/v) ratio. All libraries were pooled prior to sequencing on an Illumina HiSeq 1500. The dual-indexed single-read Rapid Run was performed with 1x120 cycles at 5 pM final concentration of the library pool. Sequence reads with average Phred quality < 20, with length < 35bp, PCR duplicates, and those aligning to blacklisted regions of the genome were filtered out using Trim Galore v0.4.4 and cutadapt v1.18. Reads were aligned to the reference human genome (hg38) using Bowtie v2.3.4.1 in end-to-end alignment mode. H3K9ac enriched (peak) regions were identified using diffReps v1.55.4 software using input samples for background correction. The statistical significance of differential peak counts was assessed using a negative binomial test using all individuals as replicates. Data & Results: The data from the six patients showed significant (Padj<0.05) acetylation changes at 925 loci after 5AC treatment versus 182 loci after DC treatment. Both drugs induced H3K9 acetylation changes at different chromosomal regions, including promoters, coding exons, introns, and distal intergenic regions. Ten common genes showed H3K9 acetylation changes by both drugs. Approximately 84% of the genes showed an H3K9 acetylation decrease by 5AC versus 54% only by DC. Figures 1 and 2 show the heatmaps for the top 100 genes and the 99 genes showing H3K9 acetylation decrease after 5AC treatment and DC treatment, respectively. Conclusion: Despite the similarity in hypomethylating activity and chemical structure, the effect of both drugs on H3K9 acetylation change was significantly different. More changes in H3K9 acetylation were observed after 5 AC treatments compared to DC. The impact of these changes on gene expression and the clinical efficacy of these drugs requires further investigation.

Keywords: DNA methylation, leukemia, decitabine, 5-Azacytidine, epigenetics

Procedia PDF Downloads 117

Authors: Katie E. Jennings

Abstract:

The interactions between family dynamics and environmental factors with mental health vulnerability in individuals are well known and are a theme for on-going research and debate. The impact upon mental health issues and forensic issues on family dynamics, experience, and emotional wellbeing cannot be over-Emphasised. For forensic patients with diagnosed mental disorders, these relationships and environments may have also been functionally linked to the development and maintenance of those disorders; with significant adverse childhood experiences being a common feature of many Patient’s histories. Mental health hospitals remove the patient from their home environments and provide treatment outside of these relationships and often outside of the home area. There is, therefore, a major focus on Services ensuring that patients are able to build and maintain relationships with family and friends, requiring services to involve families in Patients' care and treatment wherever possible. There are standards set by Government and clinical bodies that require absolute demonstration of the inclusion of family and friends in all aspects of the care and treatment of forensic patients. For some patients and family members, this push to take on a “role” in care can be unhelpful, extremely stressful, and has constant implications for the potential delicate reparation of relationships. Based on work undertaken for over 20 years in forensic mental health settings, this paper explores the positive psychology approach to a dimensional model to family inclusion in mental health care that learns from family court work and allows for the maintenance of relationships to be at both proximal and Distil levels; to prevent the replication of abuse, decrease the need for falsehoods and assist the recovery of all. The model is based on allowing families to choose to not be involved or be involved in different ways if this is seen to be more helpful. It also allows patients to choose the level of potential involvement that they would find helpful, and for this to be reviewed at a timeframe agreed by all parties, rather than when the next survey is due or the patient has a significant care meeting. This paper is significant as there is a lack of research to support services to use a positive psychology approach to work in this area, the assumption that being asked to be involved must be positive for all seems naïve at best for this patient group. Work relating to the psychology of family can significantly contribute to the development of knowledge in this area. The development of a dimensional model will support choice within families and assist in the development of more honest and open relationships.

Keywords: family dynamics, forensic, mental disorder, positive psychology

Procedia PDF Downloads 122

Authors: Laura Micheli, Majd Hijazi, Mahmoud Faytarouni

Abstract:

The damage reported by oil and gas industrial facilities revealed the utmost vulnerability of steel liquid storage tanks to seismic events. The failure of steel storage tanks may yield devastating and long-lasting consequences on built and natural environments, including the release of hazardous substances, uncontrolled fires, and soil contamination with hazardous materials. It is, therefore, fundamental to reliably predict the damage that steel liquid storage tanks will likely experience under future seismic hazard events. The seismic performance of steel liquid storage tanks is usually assessed using vulnerability curves obtained from the numerical simulation of a tank under different hazard scenarios. However, the computational demand of high-fidelity numerical simulation models, such as finite element models, makes the vulnerability assessment of liquid storage tanks time-consuming and often impractical. As a solution, this paper presents a surrogate model-based strategy for predicting seismic-induced damage in steel liquid storage tanks. In the proposed strategy, the surrogate model is leveraged to reduce the computational demand of time-consuming numerical simulations. To create the data set for training the surrogate model, field damage data from past earthquakes reconnaissance surveys and reports are collected. Features representative of steel liquid storage tank characteristics (e.g., diameter, height, liquid level, yielding stress) and seismic excitation parameters (e.g., peak ground acceleration, magnitude) are extracted from the field damage data. The collected data are then utilized to train a surrogate model that maps the relationship between tank characteristics, seismic hazard parameters, and seismic-induced damage via a data-driven surrogate model. Different types of surrogate algorithms, including naïve Bayes, k-nearest neighbors, decision tree, and random forest, are investigated, and results in terms of accuracy are reported. The model that yields the most accurate predictions is employed to predict future damage as a function of tank characteristics and seismic hazard intensity level. Results show that the proposed approach can be used to estimate the extent of damage in steel liquid storage tanks, where the use of data-driven surrogates represents a viable alternative to computationally expensive numerical simulation models.

Keywords: damage prediction , data-driven model, seismic performance, steel liquid storage tanks, surrogate model

Procedia PDF Downloads 121

Authors: Sara Tokhi Arab, Rozo Noguchi, Tofeal Ahamed

Abstract:

Drought is a natural disaster that occurs in a region due to a lack of precipitation and high temperatures over a continuous period or in a single season as a consequence of climate change. Precipitation deficits and prolonged high temperatures mostly affect the agricultural sector, water resources, socioeconomics, and the environment. Consequently, it causes agricultural product loss, food shortage, famines, migration, and natural resources degradation in a region. Agriculture is the first sector affected by drought. Therefore, it is important to develop an agricultural drought risk and loss assessment to mitigate the drought impact in the agriculture sector. In this context, the main purpose of this study was to assess yield loss using composite drought indices in the drought-affected vineyards. In this study, the CDI was developed for the years 2016 to 2020 by comprising five indices: the vegetation condition index (VCI), temperature condition index (TCI), deviation of NDVI from the long-term mean (NDVI DEV), normalized difference moisture index (NDMI) and precipitation condition index (PCI). Moreover, the quantitative principal component analysis (PCA) approach was used to assign a weight for each input parameter, and then the weights of all the indices were combined into one composite drought index. Finally, Bayesian regularized artificial neural networks (BRANNs) were used to evaluate the yield variation in each affected vineyard. The composite drought index result indicated the moderate to severe droughts were observed across the Kabul Province during 2016 and 2018. Moreover, the results showed that there was no vineyard in extreme drought conditions. Therefore, we only considered the severe and moderated condition. According to the BRANNs results R=0.87 and R=0.94 in severe drought conditions for the years of 2016 and 2018 and the R= 0.85 and R=0.91 in moderate drought conditions for the years of 2016 and 2018, respectively. In the Kabul Province within the two years drought periods, there was a significate deficit in the vineyards. According to the findings, 2018 had the highest rate of loss almost -7 ton/ha. However, in 2016 the loss rates were about – 1.2 ton/ha. This research will support stakeholders to identify drought affect vineyards and support farmers during severe drought.

Keywords: grapes, composite drought index, yield loss, satellite remote sensing

Procedia PDF Downloads 120

Authors: Lien Gysens, Bert Vanmechelen, Maarten Haspeslagh, Piet Maes, Ann Martens

Abstract:

Bovine papillomavirus (BPV) types 1 and 2 play a central role in the etiology of the most common neoplasm in horses, the equine sarcoid. The unknown mechanism behind the unique variety in a clinical presentation on the one hand and the host-dependent clinical outcome of BPV-1 infection, on the other hand, indicate the involvement of additional factors. Earlier studies have reported the potential functional significance of intratypic sequence variants, along with the existence of sarcoid-sourced BPV variants. Therefore, intratypic sequence variation seems to be an important emerging viral factor. This study aimed to give a broad insight in sarcoid-sourced BPV variation and explore its potential association with disease presentation. In order to do this, a nanopore sequencing approach was successfully optimized for screening a wide spectrum of clinical samples. Specimens of each tumour were initially screened for BPV-1/-2 by quantitative real-time PCR. A custom-designed primer set was used on BPV-positive samples to amplify the complete viral genome in two multiplex PCR reactions, resulting in a set of overlapping amplicons. For phylogenetic analysis, separate alignments were made of all available complete genome sequences for BPV-1/-2. The resulting alignments were used to infer Bayesian phylogenetic trees. We found substantial genetic variation among sarcoid-derived BPV-1, although this variation could not be linked to disease severity. Several of the BPV-1 genomes had multiple major deletions. Remarkably, the majority of the cluster within the region coding for late viral genes. Together with the extensiveness (up to 603 nucleotides) of the described deletions, this suggests an altered function of L1/L2 in disease pathogenesis. By generating a significant amount of complete-length BPV genomes, we succeeded in introducing next-generation sequencing into veterinary research focusing on the equine sarcoid, thus facilitating the first report of both nanopore-based sequencing of complete sarcoid-sourced BPV-1/-2 and the simultaneous nanopore sequencing of multiple complete genomes originating from a single clinical sample.

Keywords: Bovine papillomavirus, equine sarcoid, horse, nanopore sequencing, phylogenetic analysis

Procedia PDF Downloads 149

Authors: Yosep Chong, Yejin Kim, Jingyun Choi, Hwanjo Yu, Eun Jung Lee, Chang Suk Kang

Abstract:

For the past decades, immunohistochemistry (IHC) has been playing an important role in the diagnosis of human neoplasms, by helping pathologists to make a clearer decision on differential diagnosis, subtyping, personalized treatment plan, and finally prognosis prediction. However, the IHC performed in various tumors of daily practice often shows conflicting and very challenging results to interpret. Even comprehensive diagnosis synthesizing clinical, histologic and immunohistochemical findings can be helpless in some twisted cases. Another important issue is that the IHC data is increasing exponentially and more and more information have to be taken into account. For this reason, we reached an idea to develop an expert supporting system to help pathologists to make a better decision in diagnosing human neoplasms with IHC results. We gave probabilistic decision tree algorithm and tested the algorithm with real case data of lymphoid neoplasms, in which the IHC profile is more important to make a proper diagnosis than other human neoplasms. We designed probabilistic decision tree based on Bayesian theorem, program computational process using MATLAB (The MathWorks, Inc., USA) and prepared IHC profile database (about 104 disease category and 88 IHC antibodies) based on WHO classification by reviewing the literature. The initial probability of each neoplasm was set with the epidemiologic data of lymphoid neoplasm in Korea. With the IHC results of 131 patients sequentially selected, top three presumptive diagnoses for each case were made and compared with the original diagnoses. After the review of the data, 124 out of 131 were used for final analysis. As a result, the presumptive diagnoses were concordant with the original diagnoses in 118 cases (93.7%). The major reason of discordant cases was that the similarity of the IHC profile between two or three different neoplasms. The expert supporting system algorithm presented in this study is in its elementary stage and need more optimization using more advanced technology such as deep-learning with data of real cases, especially in differentiating T-cell lymphomas. Although it needs more refinement, it may be used to aid pathological decision making in future. A further application to determine IHC antibodies for a certain subset of differential diagnoses might be possible in near future.

Keywords: database, expert supporting system, immunohistochemistry, probabilistic decision tree

Procedia PDF Downloads 204

Authors: Edoardo Schinco

Abstract:

Studying the Marxist intellectual tradition, it is possible to verify that there were numerous cases of philosophical regression, in which the important achievements of detailed studies have been replaced by naïve ideas and previous misunderstandings: one of most important example of this tendency is related to the question of ideology. According to a common Enlightenment approach, the ideology is essentially not a reality, i.e., a factor capable of having an effect on the reality itself; in other words, the ideology is a mere error without specific historical meaning, which is only due to ignorance or inability of subjects to understand the truth. From this point of view, the consequent and immediate practice against every form of ideology are the rational dialogue, the reasoning based on common sense, in order to dispel the obscurity of ignorance through the light of pure reason. The limits of this philosophical orientation are however both theoretical and practical: on the one hand, the Enlightenment criticism of ideology is not an historicistic thought, since it cannot grasp the inner connection that ties an historical context and its peculiar ideology together; moreover, on the other hand, when the Enlightenment approach fails to release people from their illusions (e.g., when the ideology persists, despite the explanation of its illusoriness), it usually becomes a racist or elitarian thought. Unlike this first conception of ideology, Gramsci attempts to recover Marx’s original thought and to valorize its dialectical methodology with respect to the reality of ideology. As Marx suggests, the ideology – in negative meaning – is surely an error, a misleading knowledge, which aims to defense the current state of things and to conceal social, political or moral contradictions; but, that is precisely why the ideological error is not casual: every ideology mediately roots in a particular material context, from which it takes its reason being. Gramsci avoids, however, any mechanistic interpretation of Marx and, for this reason; he underlines the dialectic relation that exists between material base and ideological superstructure; in this way, a specific ideology is not only a passive product of base but also an active factor that reacts on the base itself and modifies it. Therefore, there is a considerable revaluation of ideology’s role in maintenance of status quo and the consequent thematization of both ideology as objective force, active in history, and ideology as cultural hegemony of ruling class on subordinate groups. Among the Marxists, the French philosopher Louis Althusser also gives his contribution to this crucial question; as follower of Gramsci’s thought, he develops the idea of ideology as an objective force through the notions of Repressive State Apparatus (RSA) and Ideological State Apparatuses (ISA). In addition to this, his philosophy is characterized by the presence of structuralist elements, which must be studied, since they deeply change the theoretical foundation of his Marxist thought.

Keywords: Althusser, enlightenment, Gramsci, ideology

Procedia PDF Downloads 168

Authors: Susan C. Graham

Abstract:

Islands have a long established allure for travellers the world over. From earliest accounts of human history, travellers were drawn by the sense of islandness embodied by these destinations. The concept of islandness describes the essence of what makes islands unique relative to non-islands and extends beyond geographic interpretations by attempting to capture the specific sense of self-exhibited by islanders in relation to their connection to place. The themes most strongly associated with islandness include a) a strong connection to water as both the life blood and a physical barrier, b) a unique culture and robust arts community that is deeply linked to both the island and islanders, c) an appreciation of and for nature, d) a rich sense of history and tradition connected to the place, e) a sense of community and belonging that arose through shared triumphs and struggles, and f) a profound awareness of independence, separateness, and uniqueness derived from both physical and social experience. The island brand, like all brands, is a marketing tactic designed to succinctly express a specific value proposition in simplistic ways which might include a brand symbol, logo, slogan, or representation meant to distinguish one brand from another. If a value proposition is the identification of attributes that separate one brand from another by highlighting the brand’s uniqueness, then presumably island brands may, at least in part, emphasize islandness as part of the destination brand. Yet it may in naïve to expect all islands to brand themselves using similar themes when islands can differ so substantially in terms of population, geography, political climate, economy, culture, and history. Of particular interest is the increased focus on tourism among 'cold-water' islands. This paper will examine the incorporation of themes related to islandness in tourism branding among cold-water, warm-water, and temperate-water islands. The tourism logos of 83 islands were collected and assessed for the use of themes related to islandness, namely water, arts and culture, nature, history and tradition, community and belongingness, and independence, separateness, and uniqueness. The ratings for each theme related to islandness for each of the 83 island destinations were then analyzed to identify if differences exist between cold-water, warm-water, and temperate-water islands. A general consensus of what constitutes 'cold-water' destinations is lacking, therefore a water temperature of 15C was adopted using the guidelines from the National Center for Cold Water Safety. Among these 83 islands, the average high and average low water temperatures of 196 specific locations, including the capital, northern, and southern most points of each island, was recorded to determine if the location was a cold-water (average high and low below 15C), warm-water (average high and low above 15C), or temperate-water (average high above 15C and low below 15C) location.

Keywords: branding, cold-water, islands, tourism

Procedia PDF Downloads 191

Authors: Scott Andersen

Abstract:

Perhaps simply and rather unadornedly, consciousness is having multiple goals for action and the continuously adjudication of such goals to implement action, referred to as the Maps of Meaning (MoM) Consciousness Theory. The MoM theory triangulates through three parallel corollaries, action (behavior), mechanism (morphology/pathophysiology), and goals (teleology). (1) An organism’s consciousness contains a fluid, nested goals. These goals are not intentionality, but intersectionality, embodiment meeting the world. i.e., Darwinian inclusive fitness or randomization, then survival of the fittest. These goals form via gradual descent under inclusive fitness, the goals being the abstraction of a ‘match’ between the evolutionary environment and organism. Human consciousness implements the brain efficiency hypothesis, genetics, epigenetics, and experience crystallize efficiencies, not necessitating best or objective but fitness, i.e., perceived efficiency based on one’s adaptive environment. These efficiencies are objectively arbitrary, but determine the operation and level of one’s consciousness, termed extreme thrownness. Since inclusive fitness drives efficiencies in physiologic mechanism, morphology and behavior (action) and originates one’s goals, embodiment is necessarily entangled to human consciousness as its the intersection of mechanism or action (both necessitating embodiment) occurring in the world that determines fitness. Perception is the operant process of consciousness and is the consciousness’ de facto goal adjudication process. Goal operationalization is fundamentally efficiency-based via one’s unique neuronal mapping as a byproduct of genetics, epigenetics, and experience. Perception involves information intake and information discrimination, equally underpinned by efficiencies of inclusive fitness via extreme thrownness. Perception isn’t a ‘frame rate,’ but Bayesian priors of efficiency based on one’s extreme thrownness. Consciousness and human consciousness is a modular (i.e., a scalar level of richness, which builds up like building blocks) and dimensionalized (i.e., cognitive abilities become possibilities as emergent phenomena at various modularities, like stratified factors in factor analysis). The meta dimensions of human consciousness seemingly include intelligence quotient, personality (five-factor model), richness of perception intake, and richness of perception discrimination, among other potentialities. Future consciousness research should utilize factor analysis to parse modularities and dimensions of human consciousness and animal models.

Keywords: consciousness, perception, prospection, embodiment

Procedia PDF Downloads 5

Authors: Md. Khayrul Bashar, Md. Saiful Islam, Kimiko Yamashita, Yano Midori

Abstract:

Despite having some progress in human authentication, conventional biometrics (e.g., facial features, fingerprints, retinal scans, gait, voice patterns) are not robust against falsification because they are neither confidential nor secret to an individual. As a non-invasive tool, electrocardiogram (ECG) has recently shown a great potential in human recognition due to its unique rhythms characterizing the variability of human heart structures (chest geometry, sizes, and positions). Moreover, ECG has a real-time vitality characteristic that signifies the live signs, which ensure legitimate individual to be identified. However, the detection accuracy of the current ECG-based methods is not sufficient due to a high variability of the individual’s heartbeats at a different instance of time. These variations may occur due to muscle flexure, the change of mental or emotional states, and the change of sensor positions or long-term baseline shift during the recording of ECG signal. In this study, a new method is proposed for human identification, which is based on the extraction of the local roughness of ECG heartbeat signals. First ECG signal is preprocessed using a second order band-pass Butterworth filter having cut-off frequencies of 0.00025 and 0.04. A number of local binary patterns are then extracted by applying a moving neighborhood window along the ECG signal. At each instant of the ECG signal, the pattern is formed by comparing the ECG intensities at neighboring time points with the central intensity in the moving window. Then, binary weights are multiplied with the pattern to come up with the local roughness description of the signal. Finally, histograms are constructed that describe the heartbeat signals of individual subjects in the database. One advantage of the proposed feature is that it does not depend on the accuracy of detecting QRS complex, unlike the conventional methods. Supervised recognition methods are then designed using minimum distance to mean and Bayesian classifiers to identify authentic human subjects. An experiment with sixty (60) ECG signals from sixty adult subjects from National Metrology Institute of Germany (NMIG) - PTB database, showed that the proposed new method is promising compared to a conventional interval and amplitude feature-based method.

Keywords: human identification, ECG biometrics, local roughness patterns, supervised classification

Procedia PDF Downloads 375

Authors: Ipek Kivanc, Demet Ozgur-Unluakin

Abstract:

Over the past years, technological innovations and advancements have played an important role in the industrial world. Due to technological improvements, the degree of complexity of the systems has increased. Hence, all systems are getting more uncertain that emerges from increased complexity, resulting in more cost. It is challenging to cope with this situation. So, implementing efficient planning of maintenance activities in such systems are getting more essential. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for stochastic sequential decision problems under uncertainty. Although maintenance optimization in a dynamic environment can be modeled as such a sequential decision problem, POMDPs are not widely used for tackling maintenance problems. However, they can be well-suited frameworks for obtaining optimal maintenance policies. In the classical representation of the POMDP framework, the system is denoted by a single node which has multiple states. The main drawback of this classical approach is that the state space grows exponentially with the number of state variables. On the other side, factored representation of POMDPs enables to simplify the complexity of the states by taking advantage of the factored structure already available in the nature of the problem. The main idea of factored POMDPs is that they can be compactly modeled through dynamic Bayesian networks (DBNs), which are graphical representations for stochastic processes, by exploiting the structure of this representation. This study aims to demonstrate how maintenance planning of dynamic systems can be modeled with factored POMDPs. An empirical maintenance planning problem of a dynamic system consisting of four partially observable components deteriorating in time is designed. To solve the empirical model, we resort to Symbolic Perseus solver which is one of the state-of-the-art factored POMDP solvers enabling approximate solutions. We generate some more predefined policies based on corrective or proactive maintenance strategies. We execute the policies on the empirical problem for many replications and compare their performances under various scenarios. The results show that the computed policies from the POMDP model are superior to the others. Acknowledgment: This work is supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under grant no: 117M587.

Keywords: factored representation, maintenance, multi-component system, partially observable Markov decision processes

Procedia PDF Downloads 110

Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

Abstract:

Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

Procedia PDF Downloads 76

Authors: Claire Tranter, Coral Dando

Abstract:

Gathering information from others is a fundamental goal for those concerned with investigating crime, and protecting national and international security. Persuading an individual to move from an opposing to converging viewpoint, and an understanding on the cognitive style behind this change can serve to increase understanding of traditional face-to-face interactions, as well as synthetic environments (SEs) often used for communication across varying geographical locations. SEs are growing in usage, and with this increase comes an increase in crime being undertaken online. Communication technologies can allow people to mask their real identities, supporting anonymous communication which can raise significant challenges for investigators when monitoring and managing these conversations inside SEs. To date, the psychological literature concerning how to maximise information-gain in SEs for real-world interviewing purposes is sparse, and as such this aspect of social cognition is not well understood. Here, we introduce an overview of a novel programme of PhD research which seeks to enhance understanding of cross-cultural and cross-gender communication in SEs for maximising information gain. Utilising a dyadic jury paradigm, participants interacted with a confederate who attempted to persuade them to the opposing verdict across three distinct environments: face-to-face, instant messaging, and a novel virtual reality environment utilising avatars. Participants discussed a criminal scenario, acting as a two-person (male; female) jury. Persuasion was manipulated by the confederate claiming an opposing viewpoint (guilty v. not guilty) to the naïve participants from the outset. Pre and post discussion data, and observational digital recordings (voice and video) of participant’ discussion performance was collected. Information regarding cognitive style was also collected to ascertain participants need for cognitive closure and biases towards jumping to conclusions. Findings revealed that individuals communicating via an avatar in a virtual reality environment reacted in a similar way, and thus equally persuasive, when compared to individuals communicating face-to-face. Anonymous instant messaging however created a resistance to persuasion in participants, with males showing a significant decline in persuasive outcomes compared to face to face. The findings reveal new insights particularly regarding the interplay of persuasion on gender and modality, with anonymous instant messaging enhancing resistance to persuasion attempts. This study illuminates how varying SE can support new theoretical and applied understandings of how judgments are formed and modified in response to advocacy.

Keywords: applied cognition, persuasion, social media, virtual reality

Procedia PDF Downloads 122

Authors: Yegnanew A. Shiferaw

Abstract:

Background: commodity price volatility originating from excessive commodity price fluctuation has been a global problem especially after the recent financial crises. Volatility is a measure of risk or uncertainty in financial analysis. It plays a vital role in risk management, portfolio management, and pricing equity. Objectives: the core objective of this paper is to examine the relationship between the prices of agricultural commodities with oil price, gas price, coal price and exchange rate (USD/Rand). In addition, the paper tries to fit an appropriate model that best describes the log return price volatility and estimate Value-at-Risk and expected shortfall. Data and methods: the data used in this study are the daily returns of agricultural commodity prices from 02 January 2007 to 31st October 2016. The data sets consists of the daily returns of agricultural commodity prices namely: white maize, yellow maize, wheat, sunflower, soya, corn, and sorghum. The paper applies the three-state Markov-switching (MS) regression, the standard single-regime GARCH and the two regime Markov-switching GARCH (MS-GARCH) models. Results: to choose the best fit model, the log-likelihood function, Akaike information criterion (AIC), Bayesian information criterion (BIC) and deviance information criterion (DIC) are employed under three distributions for innovations. The results indicate that: (i) the price of agricultural commodities was found to be significantly associated with the price of coal, price of natural gas, price of oil and exchange rate, (ii) for all agricultural commodities except sunflower, k=3 had higher log-likelihood values and lower AIC and BIC values. Thus, the three-state MS regression model outperformed the two-state MS regression model (iii) MS-GARCH(1,1) with generalized error distribution (ged) innovation performs best for white maize and yellow maize; MS-GARCH(1,1) with student-t distribution (std) innovation performs better for sorghum; MS-gjrGARCH(1,1) with ged innovation performs better for wheat, sunflower and soya and MS-GARCH(1,1) with std innovation performs better for corn. In conclusion, this paper provided a practical guide for modelling agricultural commodity prices by MS regression and MS-GARCH processes. This paper can be good as a reference when facing modelling agricultural commodity price problems.

Keywords: commodity prices, MS-GARCH model, MS regression model, South Africa, volatility

Procedia PDF Downloads 173

Authors: F. O’Dowd, G. Murphy, M. Roche, E. Shudell, F. Keane, M. O’Kane

Abstract:

Intoduction: Apremilast (Otezla®) is an oral phosphodiesterase-4 (PDE4) inhibitor indicated for treatment of adult patients with moderate to severe plaque psoriasis who have contraindications to have failed or intolerant of standard systemic therapy and/or phototherapy; and adult patients with active psoriatic arthritis. Apremilast influences intracellular regulation of inflammatory mediators. Two randomized, placebo-controlled trials evaluating apremilast in 1426 patients with moderate to severe plague psoriasis (ESTEEM 1 and 2) demonstrated that the commonest adverse reactions (AE’s) leading to discontinuation were nausea (1.6%), diarrhoea (1.0%), and headaches (0.8%). The overall proportion of subjects discontinuing due to adverse reactions was 6.1%. At week 16 these trials demonstrated significant more apremilast-treated patients (33.1%) achieved the primary end point PASI-75 than placebo (5.3%). We began prescribing apremilast in July 2015. Aim: To evaluate efficacy and tolerability of apremilast in an Irish teaching hospital psoriasis population. Methods: A proforma documenting clinical evaluation parameters, prior treatment experience and AE’s; was completed prospectively on all patients commenced on apremilast since July 2015 – July 2017. Data was collected at week 0,6,12,24,36 and week 52 with 20/71 patients having passed week 52. Efficacy was assessed using Psoriasis Area and Severity Index (PASI) and Dermatology Life Quality Index (DLQI). AE’s documented included GI effects, infections, changes in weight and mood. Retrospective chart review and telephone review was utilised for missing data. Results: A total of 71 adult subjects (38 male, 33 female; age range 23-57), with moderate to severe psoriasis, were evaluated. Prior treatment: 37/71 (52%) were systemic/biologic/phototherapy naïve; 14/71 (20%) has prior phototherapy alone;20/71 (28%) had previous systemic/biologic exposure; 12/71 (17%) had both psoriasis and psoriatic arthritis. PASI responses: mean baseline PASI was 10.1 and DLQI was 15.Week 6: N=71, n=15 (21%) achieved PASI 75. Week 12: N= 48, n=6 (13%) achieved a PASI 100%; n=16 (34.5%) achieved a PASI 75. Week 24: N=40, n=10 (25%) achieved a PASI 100; n=15 (37.5%) achieved a PASI 75. Week 52: N= 20, n=4 (20%) achieved a PASI 100; n= 16 (80%) achieved a PASI 75. (N= number of pts having passed the time point indicated, n= number of pts (out of N) achieving PASI or DLQI responses at that time). DLQI responses: week 24: N= 40, n=30 (75%) achieved a DLQI score of 0; n=5 (12.5%) achieved a DLQI score of 1; n=1 (2.5%) achieved a DLQI score of 10 (due to lack of efficacy). Adverse Events: The proportion of patients that discontinued treatment due to AE’s was n=7 (9.8%). One patient experienced nausea alleviated by dose reduction; another developed significant dysgeusia for certain foods, both continued therapy. Two patients lost 2-3 kg. Conclusion: Initial Irish patient experience of Apremilast appears comparable to that observed in trials with good efficacy and tolerability.

Keywords: Apremilast, introduction, Ireland, clinical audit

Procedia PDF Downloads 123

Authors: Xiaoyuan Chen

Abstract:

Background: This study aimed to characterize the epidemiological and clinical features of five rare subtypes of hepatocellular carcinoma (HCC) and to create a competing risk nomogram for predicting cancer-specific survival. Methods: This study used the Surveillance, Epidemiology, and End Results database to analyze the clinicopathological data of 50,218 patients with classic HCC and five rare subtypes (ICD-O-3 Histology Code=8170/3-8175/3) between 2004 and 2018. The annual percent change (APC) was calculated using Joinpoint regression, and a nomogram was developed based on multivariable competing risk survival analyses. The prognostic performance of the nomogram was evaluated using the Akaike information criterion, Bayesian information criterion, C-index, calibration curve, and area under the receiver operating characteristic curve. Decision curve analysis was used to assess the clinical value of the models. Results: The incidence of scirrhous carcinoma showed a decreasing trend (APC=-6.8%, P=0.025), while the morbidity of other rare subtypes remained stable from 2004 to 2018. The incidence-based mortality plateau in all subtypes during the period. Clear cell carcinoma was the most common subtype (n=551, 1.1%), followed by fibrolamellar (n=241, 0.5%), scirrhous (n=82, 0.2%), spindle cell (n=61, 0.1%), and pleomorphic (n=17, ~0%) carcinomas. Patients with fibrolamellar carcinoma were younger and more likely to have non-cirrhotic liver and better prognoses. Scirrhous carcinoma shared almost the same macro clinical characteristics and outcomes as classic HCC. Clear cell carcinoma tended to occur in the Asia-Pacific elderly male population, and more than half of them were large HCC (Size>5cm). Sarcomatoid (including spindle cell and pleomorphic) carcinoma was associated with larger tumor size, poorer differentiation, and more dismal prognoses. The pathological subtype, T stage, M stage, surgery, alpha-fetoprotein, and cancer history were identified as independent predictors in patients with rare subtypes. The nomogram showed good calibration, discrimination, and net benefits in clinical practice. Conclusion: The rare subtypes of HCC had distinct clinicopathological features and biological behaviors compared with classic HCC. Our findings could provide a valuable reference for clinicians. The constructed nomogram could accurately predict prognoses, which is beneficial for individualized management.

Keywords: hepatocellular carcinoma, pathological subtype, fibrolamellar carcinoma, scirrhous carcinoma, clear cell carcinoma, spindle cell carcinoma, pleomorphic carcinoma

Procedia PDF Downloads 47

Authors: Tzu-Ting Hu, Jung-Der Wang, Ming-Yeng Lin, Jin-Luh Chen, Perng-Jy Tsai

Abstract:

The vinyl chloride monomer (VCM) has been classified as group 1 (human) carcinogen by the IARC. Workers exposed to VCM are known associated with the development of the liver cancer and hence might cause economical and health losses. Particularly, for those work for the petrochemical industry have been seriously concerned in the environmental and occupational health field. Considering assessing workers’ health risks and their resultant economical and health losses requires the establishment of long-term VCM exposure data for any similar exposure group (SEG) of interest, the development of suitable technologies has become an urgent and important issue. In the present study, VCM exposures for petrochemical industry workers were determined firstly based on the database of the 'Workplace Environmental Monitoring Information Systems (WEMIS)' provided by Taiwan OSHA. Considering the existence of miss data, the reconstruction of historical exposure techniques were then used for completing the long-term exposure data for SEGs with routine operations. For SEGs with non-routine operations, exposure modeling techniques, together with their time/activity records, were adopted for determining their long-term exposure concentrations. The Bayesian decision analysis (BDA) was adopted for conducting exposure and health risk assessments for any given SEG in the petrochemical industry. The resultant excessive cancer risk was then used to determine the corresponding loss of quality-adjusted life expectancy (QALE). Results show that low average concentrations can be found for SEGs with routine operations (e.g., VCM rectification 0.0973 ppm, polymerization 0.306 ppm, reaction tank 0.33 ppm, VCM recovery 1.4 ppm, control room 0.14 ppm, VCM storage tanks 0.095 ppm and wastewater treatment 0.390 ppm), and the above values were much lower than that of the permissible exposure limit (PEL; 3 ppm) of VCM promulgated in Taiwan. For non-routine workers, though their high exposure concentrations, their low exposure time and frequencies result in low corresponding health risks. Through the consideration of exposure assessment results, health risk assessment results, and QALE results simultaneously, it is concluded that the proposed method was useful for prioritizing SEGs for conducting exposure abatement measurements. Particularly, the obtained QALE results further indicate the importance of reducing workers’ VCM exposures, though their exposures were low as in comparison with the PEL and the acceptable health risk.

Keywords: exposure assessment, health risk assessment, petrochemical industry, quality-adjusted life years, vinyl chloride monomer

Procedia PDF Downloads 164

Authors: Hesam Gholami

Abstract:

Elevated suspended sediment loads in riverine systems resulting from accelerated erosion due to human activities are a serious threat to the sustainable management of watersheds and ecosystem services therein worldwide. Therefore, mitigation of deleterious sediment effects as a distributed or non-point pollution source in the catchments requires reliable provenance information. Sediment tracing or sediment fingerprinting, as a combined process consisting of sampling, laboratory measurements, different statistical tests, and the application of mixing or unmixing models, is a useful technique for discriminating the sources of sediments. From 1996 to the present, different aspects of this technique, such as grouping the sources (spatial and individual sources), discriminating the potential sources by different statistical techniques, and modification of mixing and unmixing models, have been introduced and modified by many researchers worldwide, and have been applied to identify the provenance of fine materials in agricultural, rural, mountainous, and coastal catchments, and in large catchments with numerous lakes and reservoirs. In the last two decades, efforts exploring the uncertainties associated with sediment fingerprinting results have attracted increasing attention. The frameworks used to quantify the uncertainty associated with fingerprinting estimates can be divided into three groups comprising Monte Carlo simulation, Bayesian approaches and generalized likelihood uncertainty estimation (GLUE). Given the above background, the primary goal of this study was to apply geochemical fingerprinting within the GLUE framework in the estimation of sub-basin spatial sediment source contributions in the arid Mehran River catchment in southern Iran, which drains into the Persian Gulf. The accuracy of GLUE predictions generated using four different sets of statistical tests for discriminating three sub-basin spatial sources was evaluated using 10 virtual sediments (VS) samples with known source contributions using the root mean square error (RMSE) and mean absolute error (MAE). Based on the results, the contributions modeled by GLUE for the western, central and eastern sub-basins are 1-42% (overall mean 20%), 0.5-30% (overall mean 12%) and 55-84% (overall mean 68%), respectively. According to the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), our suggested modeling approach is an accurate technique to quantify the source of sediments in the catchments. Overall, the estimated source proportions can help watershed engineers plan the targeting of conservation programs for soil and water resources.

Keywords: sediment source tracing, generalized likelihood uncertainty estimation, virtual sediment mixtures, Iran

Procedia PDF Downloads 42

Authors: W. Staiano, S. Marcora

Abstract:

Introduction: It has been proposed that acute negative effect of mental fatigue (MF) could potentially become a training stimulus for the brain (Brain endurance training (BET)) to adapt and improve its ability to attenuate MF states during sport competitions. Purpose: The aim of this study was to test the efficacy of 6 weeks of BET on cognitive and cycling tests in a group of well-trained subjects. We hypothesised that combination of BET and standard physical training (SPT) would increase cognitive capacity and cycling performance by reducing rating of perceived exertion (RPE) and increase resilience to fatigue more than SPT alone. Methods: In a randomized controlled trial design, 26 well trained participants, after a familiarization session, cycled to exhaustion (TTE) at 80% peak power output (PPO) and, after 90 min rest, at 65% PPO, before and after random allocation to a 6 week BET or active placebo control. Cognitive performance was measured using 30 min of STROOP coloured task performed before cycling performance. During the training, BET group performed a series of cognitive tasks for a total of 30 sessions (5 sessions per week) with duration increasing from 30 to 60 min per session. Placebo engaged in a breathing relaxation training. Both groups were monitored for physical training and were naïve to the purpose of the study. Physiological and perceptual parameters of heart rate, lactate (LA) and RPE were recorded during cycling performances, while subjective workload (NASA TLX scale) was measured during the training. Results: Group (BET vs. Placebo) x Test (Pre-test vs. Post-test) mixed model ANOVA’s revealed significant interaction for performance at 80% PPO (p = .038) or 65% PPO (p = .011). In both tests, groups improved their TTE performance; however, BET group improved significantly more compared to placebo. No significant differences were found for heart rate during the TTE cycling tests. LA did not change significantly at rest in both groups. However, at completion of 65% TTE, it was significantly higher (p = 0.043) in the placebo condition compared to BET. RPE measured at ISO-time in BET was significantly lower (80% PPO, p = 0.041; 65% PPO p= 0.021) compared to placebo. Cognitive results in the STROOP task showed that reaction time in both groups decreased at post-test. However, BET decreased significantly (p = 0.01) more compared to placebo despite no differences accuracy. During training sessions, participants in the BET showed, through NASA TLX questionnaires, constantly significantly higher (p < 0.01) mental demand rates compared to placebo. No significant differences were found for physical demand. Conclusion: The results of this study provide evidences that combining BET and SPT seems to be more effective than SPT alone in increasing cognitive and cycling performance in well trained endurance participants. The cognitive overload produced during the 6-week training of BET can induce a reduction in perception of effort at a specific power, and thus improving cycling performance. Moreover, it provides evidence that including neurocognitive interventions will benefit athletes by increasing their mental resilience, without affecting their physical training load and routine.

Keywords: cognitive training, perception of effort, endurance performance, neuro-performance

Procedia PDF Downloads 86

Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

Abstract:

Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

Procedia PDF Downloads 112

Authors: Angela O. Ugwu, Sunday Ocheni

Abstract:

Background: Cancer-associated thrombosis (CAT) is a cause of morbidity and mortality among cancer patients. Several risk factors for developing venous thromboembolism (VTE) also coexist with cancer patients, such as chemotherapy and immobilization, thus contributing to the higher risk of VTE in cancer patients when compared to non-cancer patients. This study aimed to determine if there is any correlation between levels of some inflammatory cytokines/haematological parameters and Khorana scores of newly diagnosed chemotherapy naïve ambulatory cancer patients (CNACP). Methods: This was a cross-sectional analytical study carried out from June 2021 to May 2022. Eligible newly diagnosed cancer patients 18 years and above (case group) were enrolled consecutively from the adult Oncology Clinics of the University of Nigeria Teaching Hospital, Ituku/Ozalla (UNTH). The control group was blood donors at UNTH Ituku/Ozalla, Enugu blood bank, and healthy members of the Medical and Dental Consultants Association of Nigeria (MDCAN), UNTH Chapter. Blood samples collected from the participants were assayed for IL-6, TNF-Alpha, and haematological parameters such as haemoglobin, white blood cell count (WBC), and platelet count. Data were entered into an Excel worksheet and were then analyzed using Statistical Package for Social Sciences (SPSS) computer software version 21.0 for windows. A P value of < 0.05 was considered statistically significant. Results: A total of 200 participants (100 cases and 100 controls) were included in the study. The overall mean age of the participants was 47.42 ±15.1 (range 20-76). The sociodemographic characteristics of the two groups, including age, sex, educational level, body mass index (BMI), and occupation, were similar (P > 0.05). Following One Way ANOVA, there were significant differences between the mean levels of interleukin-6 (IL-6) (p = 0.036) and tumor necrotic factor-α (TNF-α) (p = 0.001) in the three Khorana score groups of the case group. Pearson’s correlation analysis showed a significant positive correlation between the Khorana scores and IL-6 (r=0.28, p = 0.031), TNF-α (r= 0.254, p= 0.011), and PLR (r= 0.240, p=0.016). The mean serum levels of IL-6 were significantly higher in CNACP than in the healthy controls [8.98 (8-12) pg/ml vs. 8.43 (2-10) pg/ml, P=0.0005]. There were also significant differences in the mean levels of the haemoglobin (Hb) level (P < 0.001)); white blood cell (WBC) count ((P < 0.001), and platelet (PL) count (P = 0.005) between the two groups of participants. Conclusion: There is a significant positive correlation between the serum levels of IL-6, TNF-α, and PLR and the Khorana scores of CNACP. The mean serum levels of IL-6, TNF-α, PLR, WBC, and PL count were significantly higher in CNACP than in the healthy controls. Ambulatory cancer patients with high-risk Khorana scores may benefit from anti-inflammatory drugs because of the positive correlation with inflammatory cytokines. Recommendations: Ambulatory cancer patients with 2 Khorana scores may benefit from thromboprophylaxis since they have higher Khorana scores. A multicenter study with a heterogeneous population and larger sample size is recommended in the future to further elucidate the relationship between IL-6, TNF-α, PLR, and the Khorana scores among cancer patients in the Nigerian population.

Keywords: thromboprophylaxis, cancer, Khorana scores, inflammatory cytokines, haematological parameters

Procedia PDF Downloads 52

Authors: Kevin De Castro Cogle, Mirian Kubo, Maria Anastasiadi, Fady Mohareb, Claire Rossi

Abstract:

Background: The bioaccessibility of bioactive compounds is a critical determinant of the nutritional quality of various food products. Despite its importance, there is a limited number of comprehensive studies aimed at assessing how the composition of a food matrix influences the bioaccessibility of a compound of interest. This knowledge gap has prompted a growing need to investigate the intricate relationship between food matrix formulations and the bioaccessibility of bioactive compounds. One such class of bioactive compounds that has attracted considerable attention is curcuminoids. These naturally occurring phytochemicals, extracted from the roots of Curcuma longa, have gained popularity owing to their purported health benefits and also well known for their poor bioaccessibility Project aim: The primary objective of this research project is to systematically assess the influence of matrix composition on the bioaccessibility of curcuminoids. Additionally, this study aimed to develop a series of predictive models for bioaccessibility, providing valuable insights for optimising the formula for functional foods and provide more descriptive nutritional information to potential consumers. Methods: Food formulations enriched with curcuminoids were subjected to in vitro digestion simulation, and their bioaccessibility was characterized with chromatographic and spectrophotometric techniques. The resulting data served as the foundation for the development of predictive models capable of estimating bioaccessibility based on specific physicochemical properties of the food matrices. Results: One striking finding of this study was the strong correlation observed between the concentration of macronutrients within the food formulations and the bioaccessibility of curcuminoids. In fact, macronutrient content emerged as a very informative explanatory variable of bioaccessibility and was used, alongside other variables, as predictors in a Bayesian hierarchical model that predicted curcuminoid bioaccessibility accurately (optimisation performance of 0.97 R2) for the majority of cross-validated test formulations (LOOCV of 0.92 R2). These preliminary results open the door to further exploration, enabling researchers to investigate a broader spectrum of food matrix types and additional properties that may influence bioaccessibility. Conclusions: This research sheds light on the intricate interplay between food matrix composition and the bioaccessibility of curcuminoids. This study lays a foundation for future investigations, offering a promising avenue for advancing our understanding of bioactive compound bioaccessibility and its implications for the food industry and informed consumer choices.

Keywords: bioactive bioaccessibility, food formulation, food matrix, machine learning, probabilistic modelling

Procedia PDF Downloads 36