362 results on '"Ozlem Ozmen"'
Search Results
2. A rare clinical presentation of lung cancer: two cases of solitary digital metastasis on Tc-99m MDP bone scan
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Inci Uslu Biner, Pinar Akin Kabalak, Tuba Inal Cengiz, Ulkü Yilmaz, Derya Kizilgoz, Metehan Karaca, Fatma Canbay, Yetkin Agackiran, Ebru Tatci, and Ozlem Ozmen
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Acrometastasis ,Digital metastasis ,Lung cancer ,Tc-99m MDP bone scan ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 - Abstract
Abstract Background Acrometastases are accounted for a very small proportion of bone metastases. Lung cancer is the most common acrometastasis origin, and it often has a poor prognosis. The aim of the present cases is to describe the probability of an acrometastasis in the differential diagnosis of finger lesions and to emphasize the importance of taking local views of extremity of complaint for proper interpretation in such patients. Case presentation Here we reported two patients with metastases to the fingers with occult primary lung carcinoma and a history of prior lung malignancy, respectively. First case was a 58-year-old man with history of pain and swelling in the fourth finger of his left hand. He underwent an amputation from the metacarpophalangeal level and the pathological diagnosis was metastatic NSCLC. The second case was a 65-year-old ex-smoker man with a history of prior lung cancer (adenocarcinoma) suffered from a swollen, erythematous, painful tip of the right third finger. Phalangectomy was performed and the histological examination of the amputated part revealed the presence of a metastatic differentiated carcinoma of pulmonary origin. Conclusions When assessing the masses located at fingers, possibility of a solitary metastatic lung lesion should be considered.
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- 2023
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3. Inulin protects against the harmful effects of dietary emulsifiers on mice gut microbiome
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Cansu Bekar, Ozlem Ozmen, Ceren Ozkul, and Aylin Ayaz
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Emulsifiers ,Inulin ,Lecithin ,Carboxymethyl cellulose ,Microbiota ,Inflammation ,Medicine ,Biology (General) ,QH301-705.5 - Abstract
Background The prevalence of inflammatory bowel diseases is increasing, especially in developing countries, with adoption of Western-style diet. This study aimed to investigate the effects of two emulsifiers including lecithin and carboxymethyl cellulose (CMC) on the gut microbiota, intestinal inflammation and the potential of inulin as a means to protect against the harmful effects of emulsifiers. Methods In this study, male C57Bl/6 mice were divided into five groups (n:6/group) (control, CMC, lecithin, CMC+inulin, and lecithin+inulin). Lecithin and CMC were diluted in drinking water (1% w/v) and inulin was administered daily at 5 g/kg for 12 weeks. Histological examination of the ileum and colon, serum IL-10, IL-6, and fecal lipocalin-2 levels were analyzed. 16S rRNA gene V3-V4 region amplicon sequencing was performed on stool samples. Results In the CMC and lecithin groups, shortening of the villus and a decrease in goblet cells were observed in the ileum and colon, whereas inulin reversed this effect. The lipocalin level, which was 9.7 ± 3.29 ng in the CMC group, decreased to 4.1 ± 2.98 ng with the administration of inulin. Bifidobacteria and Akkermansia were lower in the CMC group than the control, while they were higher in the CMC+inulin group. In conclusion, emulsifiers affect intestinal health negatively by disrupting the epithelial integrity and altering the composition of the microbiota. Inulin is protective on their harmful effects. In addition, it was found that CMC was more detrimental to microbiota composition than lecithin.
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- 2024
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4. Protective effect of melatonin on learning and memory impairment and hippocampal dysfunction in rats induced by high-fructose corn syrup
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Arzu Yalcin, Mustafa Saygin, Ozlem Ozmen, Oguzhan Kavrik, and Hikmet Orhan
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high fructose corn syrup ,hippocampus ,learning ,memory ,melatonin ,Medicine - Abstract
Objective(s): We investigated the harmful effects of high fructose corn syrup (HFCS) on learning and memory in the hippocampus and the ameliorative effects of melatonin (Mel). Materials and Methods: Thirty-six adult male Sprague Dawley rats were divided into three groups: Group I, control; Group II, HFCS; and Group III, HFCS+Mel. HFCS form F55 was prepared as a 20% fructose syrup solution. Rats in HFCS and HFCS+Mel groups were given drinking water for 10 weeks. Rats in the HFCS+Mel group have been given 10 mg/kg/day melatonin orally for the 6 weeks, in addition to HFCS 55. The Morris water maze (MWM) test was applied to all animals for 5 days to determine their learning and memory levels. After decapitation, one-half of the hippocampus samples were collected for western blot analysis, and another half of the tissues were collected for histopathological and immunohistochemical analyses. Results: In the HFCS group, there was a significant difference between the time to find the platform in the MWM test and time spent in the quadrant between days 1 and 5 (P=0.037 and P=0.001, respectively). In addition, a decreased level of MT1A receptor, TNF-α, iNOS, osteopontin (OPN), and interleukin-6 (IL-6) expressions were significantly increased in the HFCS group. Melatonin treatment reversed MT1A receptor levels and TNF-α, iNOS, OPN, and IL-6 expressions. During the histopathological examination, increased neuronal degenerations were observed in the HFCS group. Melatonin ameliorated these changes.Conclusion: Consumption of HFCS caused deterioration of learning and memory in adult rats. We suggest that melatonin is effective against learning and memory disorders.
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- 2023
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5. TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks
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Amirarsalan Rajabi and Ozlem Ozmen Garibay
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fairness in artificial intelligence ,generative adversarial networks ,fair data generation ,Computer engineering. Computer hardware ,TK7885-7895 - Abstract
With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Network for tabular data generation. The model includes two phases of training. In the first phase, the model is trained to accurately generate synthetic data similar to the reference dataset. In the second phase we modify the value function to add fairness constraint, and continue training the network to generate data that is both accurate and fair. We test our results in both cases of unconstrained, and constrained fair data generation. We show that using a fairly simple architecture and applying quantile transformation of numerical attributes the model achieves promising performance. In the unconstrained case, i.e., when the model is only trained in the first phase and is only meant to generate accurate data following the same joint probability distribution of the real data, the results show that the model beats the state-of-the-art GANs proposed in the literature to produce synthetic tabular data. Furthermore, in the constrained case in which the first phase of training is followed by the second phase, we train the network and test it on four datasets studied in the fairness literature and compare our results with another state-of-the-art pre-processing method, and present the promising results that it achieves. Comparing to other studies utilizing GANs for fair data generation, our model is comparably more stable by using only one critic, and also by avoiding major problems of original GAN model, such as mode-dropping and non-convergence.
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- 2022
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6. The impact of moderate-intensity swimming exercise on learning and memory in aged rats: The role of Sirtuin-1
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Ulker Tunca, Mustafa Saygin, Ozlem Ozmen, Rahime Aslankoc, and Arzu Yalcin
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bdnf ,creb ,learning-memory ,sirtuin-1 ,swimming exercise ,Medicine - Abstract
Objective(s): The purpose of this study was to evaluate the effect of moderate-intensity swimming exercise on learning and memory by the Morris water maze test. Changes in the expressions of cyclic AMP-response element-binding protein (CREB) and brain-derived neurotrophic factor (BDNF) proteins alternative pathway which were activated by sirtuin-1 (SIRT-1) were investigated. Materials and Methods: The study included thirty-two male Sprague-Dawley rats (350-500 g, 11-12 and 15–16 months old). The rats were randomly divided into four groups with 8 rats in each group. The groups were designed as follows: Control-1 (11-12 months), Exercise-1 (11-12 months), Control-2 (15-16 months), Exercise-2 (15-16 months). Moderate-intensity exercise was assigned for 30 min/day, 5 days/week, for the whole training period of 8 weeks. Results: There were statistically significant differences between the groups on the third day (P=0.005) when swim speeds increased in the exercise groups. There was a statistically significant difference between Exercise 1 and Exercise 2 groups, the entries in the platform zone decreased in Exercise 2 group (P=0.026). While there were no histopathological findings observed in any group, increased SIRT-1, BNDF, and CREB expressions were seen in exercise groups compared with control groups.Conclusion: In aged rats exercising at moderate intensity, increased expression of CREB and BDNF, and SIRT-1 could improve hippocampal-dependent memory.
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- 2021
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7. Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium
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Yazdani-Jahromi, Mehdi, Yalabadi, Ali Khodabandeh, Rajabi, AmirArsalan, Tayebi, Aida, Garibay, Ivan, and Garibay, Ozlem Ozmen
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Computer Science - Machine Learning - Abstract
The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification tasks. Current methods for mitigating bias often result in information loss and an inadequate balance between accuracy and fairness. To address this, we propose a novel methodology grounded in bilevel optimization principles. Our deep learning-based approach concurrently optimizes for both accuracy and fairness objectives, and under certain assumptions, achieving proven Pareto optimal solutions while mitigating bias in the trained model. Theoretical analysis indicates that the upper bound on the loss incurred by this method is less than or equal to the loss of the Lagrangian approach, which involves adding a regularization term to the loss function. We demonstrate the efficacy of our model primarily on tabular datasets such as UCI Adult and Heritage Health. When benchmarked against state-of-the-art fairness methods, our model exhibits superior performance, advancing fairness-aware machine learning solutions and bridging the accuracy-fairness gap. The implementation of FairBiNN is available on https://github.com/yazdanimehdi/FairBiNN., Comment: Accepted to NeurIPS 2024
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- 2024
8. LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
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Kowsher, Md, Sobuj, Md. Shohanur Islam, Prottasha, Nusrat Jahan, Alanis, E. Alejandro, Garibay, Ozlem Ozmen, and Yousefi, Niloofar
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Computer Science - Machine Learning ,Computer Science - Computation and Language - Abstract
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs (Large Language Models). LLM-Mixer captures both short-term fluctuations and long-term trends by decomposing the data into multiple temporal resolutions and processing them with a frozen LLM, guided by a textual prompt specifically designed for time-series data. Extensive experiments conducted on multivariate and univariate datasets demonstrate that LLM-Mixer achieves competitive performance, outperforming recent state-of-the-art models across various forecasting horizons. This work highlights the potential of combining multiscale analysis and LLMs for effective and scalable time-series forecasting., Comment: Time series forecasting using LLMs
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- 2024
9. Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning
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Prottasha, Nusrat Jahan, Mahmud, Asif, Sobuj, Md. Shohanur Islam, Bhat, Prakash, Kowsher, Md, Yousefi, Niloofar, and Garibay, Ozlem Ozmen
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Computer Science - Computation and Language - Abstract
Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefix tuning utilize special, modifiable tokens that lack semantic meaning and require extensive training for best performance, often falling short. In this context, we propose a novel method called Semantic Knowledge Tuning (SK-Tuning) for prompt and prefix tuning that employs meaningful words instead of random tokens. This method involves using a fixed LLM to understand and process the semantic content of the prompt through zero-shot capabilities. Following this, it integrates the processed prompt with the input text to improve the model's performance on particular tasks. Our experimental results show that SK-Tuning exhibits faster training times, fewer parameters, and superior performance on tasks such as text classification and understanding compared to other tuning methods. This approach offers a promising method for optimizing the efficiency and effectiveness of LLMs in processing language tasks., Comment: Accepted in Nature Scientific Reports
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- 2024
10. Analyzing X's Web of Influence: Dissecting News Sharing Dynamics through Credibility and Popularity with Transfer Entropy and Multiplex Network Measures
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Abdidizaji, Sina, Baekey, Alexander, Jayalath, Chathura, Mantzaris, Alexander, Garibay, Ozlem Ozmen, and Garibay, Ivan
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Computer Science - Social and Information Networks - Abstract
The dissemination of news articles on social media platforms significantly impacts the public's perception of global issues, with the nature of these articles varying in credibility and popularity. The challenge of measuring this influence and identifying key propagators is formidable. Traditional graph-based metrics such as different centrality measures and node degree methods offer some insights into information flow but prove insufficient for identifying hidden influencers in large-scale social media networks such as X (previously known as Twitter). This study adopts and enhances a non-parametric framework based on Transfer Entropy to elucidate the influence relationships among X users. It further categorizes the distribution of influence exerted by these actors through the innovative use of multiplex network measures within a social media context, aiming to pinpoint influential actors during significant world events. The methodology was applied to three distinct events, and the findings revealed that actors in different events leveraged different types of news articles and influenced distinct sets of actors based on the news category. Notably, we found that actors disseminating trustworthy news articles to influence others occasionally resort to untrustworthy sources. However, the converse scenario, wherein actors predominantly using untrustworthy news types switch to trustworthy sources for influence, is less prevalent. This asymmetry suggests a discernible pattern in the strategic use of news articles for influence across social media networks, highlighting the nuanced roles of trustworthiness and popularity in the spread of information and influence., Comment: Accepted at the Advances in Social Networks Analysis and Mining (ASONAM) - 2024, Annual Conference
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- 2024
11. Denovirus Antigen Detection in Paraffinized Lung Sections of Pneumonic Goat Lungs Using Immunohistochemistry
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Keyvan Jamshidi, Ozlem Ozmen, Mehrdad Rahmani, Rashid Marvaki, and Mehdi Soltanmohammadi
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adenovirus ,goat ,histopatholog ,immunohistochemistryy ,lung ,Veterinary medicine ,SF600-1100 - Abstract
BACKGROUNDS: Diseases affecting the respiratory tract of sheep and goats are one of the most important factors which limit production of these species on a world-wide basis. OBJECTIVES: The main goal of this study was to determine Adenovirus (AdV) antigen in formalin-fixed paraffin-embedded lung tissue of pneumonic goats, using immunohistochemistry (IHC) staining method. METHODS: For this purpose, the lungs of 402 goats, which were raised in various farms in the Garmsar district and surrounding areas and were brought to the local abattoir for slaughtering between April and September 2016, were examined. RESULTS: Macroscopic pneumonia findings were detected in different lobes particularly in the apical and cardiac lobes of the lungs of 26 goats (%6.46). The rates of mild, moderate and severe consolidations observed in the pneumonic lungs were 59.8%, 26.3% and 11.6%, respectively. Pneumonias were microscopically classified in goats as interstitial pneumonia (n=15) (57.69%), suppurative bronchopneumonia (n=4) (15.38%), bronchointerstitial pneumonia (n=3) (11.53%), and parasitic pneumonia (n=4) (15.38%). A total of 22 pneumonic lungs, excluding parasitic pneumonia, examination with immunohistochemistry (IH) in terms of AdV antigen, were considered. AdV antigen was determined to be (13.63 %) (n=3) by the immunohistochemistry (IHC) method. CONCLUSIONS: In conclusion, the presence of viral antigen in lung tissues of goats may indicate that natural pneumonia may be induced by AdV or possibly other species-specific AdVs. Moreover, it is suggested that AdV might have a role in predisposing this species to secondary bacterial infections.
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- 2019
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12. UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning
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Aida Tayebi, Niloofar Yousefi, Mehdi Yazdani-Jahromi, Elayaraja Kolanthai, Craig J. Neal, Sudipta Seal, and Ozlem Ozmen Garibay
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drug-target interaction ,ensemble learning ,deep learning ,machine learning ,spike protein ,SARS-CoV-2 ,Organic chemistry ,QD241-441 - Abstract
Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.
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- 2022
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13. A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19
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Ece C. Mutlu, Toktam Oghaz, Jasser Jasser, Ege Tutunculer, Amirarsalan Rajabi, Aida Tayebi, Ozlem Ozmen, and Ivan Garibay
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Coronavirus ,COVID-19 ,Hydroxychloroquine ,Opinion mining ,Polarity ,Social media ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Science (General) ,Q1-390 - Abstract
At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risks, financial difficulties, etc. the online media, especially the Twitter platform, is experiencing a high volume of activity related to this pandemic. Among the hot topics, the polarized debates about unconfirmed medicines for the treatment and prevention of the disease have attracted significant attention from online media users. In this work, we present a stance data set, COVID-CQ, of user-generated content on Twitter in the context of COVID-19. We investigated more than 14 thousand tweets and manually annotated the tweet initiators’ opinions regarding the use of “chloroquine” and “hydroxychloroquine” for the treatment or prevention of COVID-19. To the best of our knowledge, COVID-CQ is the first data set of Twitter users’ stances in the context of the COVID-19 pandemic, and the largest Twitter data set on users’ stances towards a claim, in any domain. We have made this data set available to the research community via the Mendeley Data repository. We expect this data set to be useful for many research purposes, including stance detection, evolution and dynamics of opinions regarding this outbreak, and changes in opinions in response to the exogenous shocks such as policy decisions and events.
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- 2020
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14. BHV-1 Antigen Detection in Paraffinized Lung Sections of Pneumonic Sheep Lung Using Immunohistochemistry
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Keivan Jamshidi and Ozlem Ozmen
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bhv1 ,immunohistochemistry ,lung ,pneumonia ,sheep ,Veterinary medicine ,SF600-1100 - Abstract
Background: Respiratory tract infections caused by some viruses with cattle origin have been demonstrated in sheep and goats. OBJECTIVES: The main goal of this study was to determine Bovine Herpes virus type 1 BHV1antigen in formalin-fixed paraffin-embedded lung tissue of pneumonic sheep, using immunohistochemistry (IHC) staining method. METHODS: For this purpose, the lungs of 4079 sheep, which were raised in various farms in the Garmsar district and surrounding areas and were brought to the local abattoir for slaughtering between April and September 2016, were examined. RESULTS: Macroscopic pneumonia findings were detected in different lobes particularly in the apical and cardiac lobes of the lungs of259 sheep (6.35%). The rates of mild, moderate and severe consolidations observed in the pneumonic lungs were 59.8%, 26.3 % and 11.6 %, respectively. Pneumonias were microscopically classified in sheep as interstitial pneumonia (49.8%), suppurative bronchopneumonia (15.7%), bronchointerstitial pneumonia (11.1 %), and parasitic pneumonia (14.3%). A total of 220 pneumonic lungs, excluding parasitic pneumonia, examination with immunohistochemistry (IH) in terms of BHV1 antigen, were considered. BHV1 antigen was determined to be 8.63 % by the immunohistochemistry (IHC) method. CONCLUSIONS: In conclusion, the presence of viral antigen in lung tissues of sheep may indicate that natural pneumonia may be induced by BHV1 or possibly other species-specific herpesviruses. Moreover, it is suggested that sheep might have a role in the transmission of this virus to cattle.
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- 2018
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15. Agent-Based Modeling of C. Difficile Spread in Hospitals: Assessing Contribution of High-Touch vs. Low-Touch Surfaces and Inoculations' Containment Impact
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Abdidizaji, Sina, Yalabadi, Ali Khodabandeh, Yazdani-Jahromi, Mehdi, Garibay, Ozlem Ozmen, and Garibay, Ivan
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Computer Science - Multiagent Systems - Abstract
Health issues and pandemics remain paramount concerns in the contemporary era. Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of infection dissemination within healthcare units and hospitals is imperative to implement targeted containment measures. In this study, we address the limitations of prior research by Sulyok et al., where they delineated two distinct categories of surfaces as high-touch and low-touch fomites, and subsequently evaluated the viral spread contribution of each surface utilizing mathematical modeling and Ordinary Differential Equations (ODE). Acknowledging the indispensable role of spatial features and heterogeneity in the modeling of hospital and healthcare settings, we employ agent-based modeling to capture new insights. By incorporating spatial considerations and heterogeneous patients, we explore the impact of high-touch and low-touch surfaces on contamination transmission between patients. Furthermore, the study encompasses a comprehensive assessment of various cleaning protocols, with differing intervals and detergent cleaning efficacies, in order to identify the most optimal cleaning strategy and the most important factor amidst the array of alternatives. Our results indicate that, among various factors, the frequency of cleaning intervals is the most critical element for controlling the spread of CDI in a hospital environment., Comment: Accepted and presented at the Computational Social Science Society of the Americas Conference (CSS 2023)
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- 2024
16. Controlling the Misinformation Diffusion in Social Media by the Effect of Different Classes of Agents
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Yalabadi, Ali Khodabandeh, Yazdani-Jahromi, Mehdi, Abdidizaji, Sina, Garibay, Ivan, and Garibay, Ozlem Ozmen
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Computer Science - Multiagent Systems ,Computer Science - Social and Information Networks - Abstract
The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based models. Our approach is an extension of a model called 'SBFC' which is a SIR-based model. This model has three states, Susceptible, Believer, and Fact-Checker. The dynamics and transition between states are based on neighbors' beliefs, hoax credibility, spreading rate, probability of verifying the news, and probability of forgetting the current state. Our contribution is to push this model to real social networks by considering different classes of agents with their characteristics. We proposed two main strategies for confronting misinformation diffusion. First, we can educate a minor class, like scholars or influencers, to improve their ability to verify the news or remember their state longer. The second strategy is adding fact-checker bots to the network to spread the facts and influence their neighbors' states. Our result shows that both of these approaches can effectively control the misinformation spread., Comment: Accepted at The Computational Social Science Society of the Americas (CSS) - 2023, Annual Conference
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- 2024
17. Pathological examination of deep pectoral myopathy in house reared broilers
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Ozlem OZMEN
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deep pectoral myopathy (dpm) ,pathology ,broiler ,Veterinary medicine ,SF600-1100 - Abstract
Deep pectoral myopathy (DPM) is a disease characterized by focal necrosis, hemorrhages, and green discoloration in the pectoral muscle of broilers and turkeys. The lesions of the affected muscles are usually detected during dissection after slaughter. DPM causes significant economic losses in the poultry meat industry. The purpose of this study was to investigate the gross and microscopic findings in a housereared broiler flock with DPM. In this study, the pathological findings of 12 house reared 100-120-day-old broilers with DPM were examined. All birds were clinically healthy but hemorrhages and green discoloration were detected on the pectoral muscle mass during dissection. Samples were collected from the lesioned muscles for a histopathological examination, which revealed necrosis, hyalinization, and hemorrhage. Inflammatory cell infiltration and atrophy of breast muscles was present in some cases. DPM was diagnosed based on gross characteristics and microscopic findings.
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- 2017
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18. The ameliorative effect of gallic acid on pancreas lesions induced by 2.45 GHz electromagnetic radiation (Wi-Fi) in young rats
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Senay Topsakal, Ozlem Ozmen, Ekrem Cicek, and Selcuk Comlekci
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Electromagnetic radiation (EMR) ,Wi-Fi ,Gallic acid ,Pancreas ,Pathology ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 ,Nuclear engineering. Atomic power ,TK9001-9401 - Abstract
The aim of this study was to investigate the effects of electromagnetic radiation (EMR) on the pancreas tissue of young rats and the ameliorative effect of Gallic acid (GA). Six-week-old, 48 male rats were equally divided into four groups: Sham group, EMR group (2.45 GHz), EMR (2.45 GHz)+GA group (30 mg/kg/daily) orally and GA group (30 mg/kg/daily). After 30 days, serum and pancreatic tissue samples were harvested for biochemical, histopathological and immunohistochemical analysis. Serum amylase, lipase, glucose, and tissue malondialdehyde, total oxidant status and oxidative stress index were increased, whereas total antioxidant status decreased in the EMR group. The histopathological examination of the pancreases indicated slight degenerative changes in some pancreatic endocrine and exocrine cells and slight inflammatory cell infiltrations in the EMR group. At the immunohistochemical examination, marked increase was observed in calcitonin gene related protein and Prostaglandin E2 expressions in pancreatic cells in this group. There were no changes in interleukin-6 expirations. GA ameliorated biochemical and pathological findings in the EMR+GA group. These findings clearly demonstrate that EMR can cause degenerative changes in both endocrine and exocrine pancreas cells in rats during the developmental period and GA has an ameliorative effect.
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- 2017
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19. Paclitaxel-induced dermal hypersensitivity lesions: 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography
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Inci Uslu Biner, Ebru Tatci, Berna Akinci Ozyurek, and Ozlem Ozmen
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2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ,hypersensitivity reaction ,lung cancer ,paclitaxel ,Diseases of the respiratory system ,RC705-779 - Abstract
Paclitaxel is frequently used for the treatment of patients with nonsmall cell lung cancer. Hypersensitivity reactions (HSRs) have been one of the toxicities observed with administration of paclitaxel. Here, we presented a case of a 49-year-old man with a history of right lung mass proven by biopsy to be a nonsmall cell lung cancer (squamous cell carcinoma) who developed HSR during therapy. In addition to the hypermetabolic primary malignancy, a positron emission tomography/computed tomography (PET/CT) scan showed multiple hypermetabolic skin lesions at several parts of the body. These cutaneous lesions were resolved in the restaging PET/CT scan performed after completion of the six cycles of chemotherapy. This is the first documented case of comparative PET/CT findings of a paclitaxel-induced hypersensitivity.
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- 2018
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20. Quantum Contagion: A Quantum-Like Approach for the Analysis of Social Contagion Dynamics with Heterogeneous Adoption Thresholds
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Ece C. Mutlu and Ozlem Ozmen Garibay
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complex networks ,heterogeneous adoption thresholds ,information diffusion ,phase transitions ,quantum-like social contagion ,technology adoption ,Science ,Astrophysics ,QB460-466 ,Physics ,QC1-999 - Abstract
Modeling the information of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent-systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information, there are no studies that take into account the individuals’ uncertainty during their adoption decision-making. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We employed the Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory, and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks.
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- 2021
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21. FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation
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Yalabadi, Ali Khodabandeh, Yazdani-Jahromi, Mehdi, Yousefi, Niloofar, Tayebi, Aida, Abdidizaji, Sina, and Garibay, Ozlem Ozmen
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is the first DTI model to simultaneously leverage drug molecule fragments and protein pockets. Our information-rich representations for both proteins and drugs offer a detailed perspective on their interaction. Inspired by the Perceiver IO framework, our model features a learnable latent array, initially interacting with protein binding site embeddings using cross-attention and later refined through self-attention and used as a query to the drug fragments in the drug's cross-attention transformer block. This learnable query array serves as a mediator and enables seamless information translation, preserving critical nuances in drug-protein interactions. Our computational results on three benchmarking datasets demonstrate the superior predictive power of our model over several state-of-the-art models. We also show the interpretability of our model in terms of the critical components of both target proteins and drug molecules within drug-target pairs., Comment: Accepted at the NeurIPS workshop (AI4D3) - 2023
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- 2023
22. Contribution of Organ Vasculature in Rat Renal Analysis for Ochratoxin A: Relevance to Toxicology of Nephrotoxins
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Peter Mantle, Mehmet A. Kilic, Firdevs Mor, and Ozlem Ozmen
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perfusion in vivo ,ochratoxin A ,renal vasculature ,ochratoxin accumulation ,DNA adducts ,aristolochic acid ,Medicine - Abstract
Assumptions surrounding the kidney as a target for accumulation of ochratoxin A (OTA) are addressed because the contribution of the toxin in blood seems invariably to have been ignored. Adult rats were maintained for several weeks on toxin-contaminated feed. Using standard perfusion techniques, animals were anaesthetised, a blood sample was taken, one kidney was ligated, and the other kidney perfused with physiological saline in situ under normal blood pressure. Comparative analysis of OTA in pairs of kidneys showed marked reduction in the perfused organ in the range 37%–98% (mean 75%), demonstrating the general efficiency of perfusion supported also by histology, and implying a major role of blood in the total OTA content of kidney. Translation of OTA values in plasma to whole blood, and its predicted contribution as a 25% vascular compartment in kidney gave values similar to those in non-perfused kidneys. Thus, apparent ‘accumulation’ of OTA in kidney is due to binding to plasma proteins and long half-life in plasma. Attention should be re-focused on whole animal pharmacokinetics during chronic OTA exposure. Similar principles may be applied to DNA-OTA adducts which are now recognised as occurring in blood; application could also extend to other nephrotoxins such as aristolochic acid. Thus, at least, quantitative reassessment in urological tissues seems necessary in attributing adducts specifically as markers of potentially-tumourigenic exposure.
- Published
- 2015
- Full Text
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23. 18F-FDG PET/CT rarely provides additional information other than primary tumor detection in patients with pulmonary carcinoid tumors
- Author
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Ebru Tatci, Ozlem Ozmen, Atila Gokcek, Inci Uslu Biner, Esra Ozaydin, Sadi Kaya, and Nuri Arslan
- Subjects
FDG PET/CT ,pulmonary carcinoid tumor ,synchronous pulmonary carcinoids ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 ,Diseases of the respiratory system ,RC705-779 - Abstract
Aim: The purpose of this study was to assess the contribution of 18 F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET)/Computed Tomography (CT) in detection and staging of pulmonary carcinoid tumors. Methods: A total of 22 patients with pulmonary carcinoid tumors (14 typical, 8 atypical) were reviewed in this retrospective study. PET/CT images of all patients were evaluated for primary tumor as well as metastatic regional lymph nodes, bone and other distant metastases. PET/CT positivity of primary tumors was determined by visual interpretation. Tumor size, SUVmax and Hounsfield Unit (HU) values of the tumors were used to test for differences between tumor groups (typical carcinoids and atypical carcinoids). Results: SUVmax of carcinoids ranged from 1.24 to 11.1 (mean, 5.0; median, 2.67). The mean largest diameter of primary tumors was 2.7 ± 1.3 cm, ranging from 1 to 5.5 cm. The overall sensitivity of FDG PET/CT for detection of pulmonary carcinoid tumors was 81.8%. Tumor size, SUVmax and Hounsfield Unit (HU) values of the atypical carcinoids were higher than those for typical carcinoids. However, the results were not statistically meaningful (P > 0.05). The sensitivity and specificity of FDG PET/CT in the detection of mediastinal and hilar lymph nodes metastases were 25% and 83% respectively. One patient had bone metastasis. Conclusion: Although FDG PET/CT can be a useful tool for the detection of pulmonary carcinoid tumors and distant metastasis, it cannot discriminate typical carcinoids from atypical ones and absence of an FDG avid lesion cannot exclude pulmonary carcinoid tumors. Moreover, PET/CT is not a reliable tool in the staging of mediastinal and hilar lymph nodes especially for those patients with typical carcinoids.
- Published
- 2014
- Full Text
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24. Bir keçide bilateral konjenital hidrosalpinks olgusu
- Author
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Mehmet HALIGÜR, AYŞE HALIGÜR, MESİH KOCAMÜFTÜOĞLU, and OZLEM OZMEN
- Subjects
Anahtar ,Veterinary medicine ,SF600-1100 ,Medicine (General) ,R5-920 - Abstract
Bu çalışmada bir keçide saptanan bilateral konjenital hidrosalpinks olgusunun patomorfolojik ve jinekolojik olarak değerlendirilmesi amaçlanmıştır. Çalışma materyali Antalya mezbahanesinde kesilen 1 yaşlı, dişi bir kıl keçisine aitti. Makroskobik olarak her iki fallopian tüpünün içinde aşırı miktarda sıvı olduğu tesbit edildi. Tuba uterinanın duvarı oldukça incelmiş olup, tüp gergin görünümdeydi. Salpinksler açıldığında; berrak, seröz kıvamlı, şeffaf ve toplamda 50 cc kadar sıvı birikimi saptandı. Sağ taraftaki fallopian tüpünün eni 14.56–16.87 mm arasında değişirken boyu 71.53 mm ölçüldü. Sol tarafın ise eninin 1.17–12.06 mm arasında değişirken boyunun 45.75 mm olduğu saptandı. Sağ kornu uterinin tuba uterinaya bağlandığı yerde 29.32 mm’lik, sol kornunun tuba uterinaya bağlandığı yerde ise 27.16 mm’lik bir alanın tıkalı olduğu tespit edildi. Histopatolojik olarak ampulla ve istmus kısımlarında epitel tabakalarında değişiklikler ile bazı alanlarda epitel tabakalarında dökülmeler görüldü. Epitel hücreleri yassılaşmış ve salpinks duvarı oldukça incelmişti. Bu incelme kas tabakasında daha belirgin olup, birçok bölgede fibröz dokunun kas tabakasının yerini aldığı saptandı. Hayvanın hiç gebe kalmamış olması ve histopatolojik incelemede yangısal reaksiyonun görülmemesi olgunun konjenital olduğunu düşündürdü.
- Published
- 2013
25. Occipital Hypometabolism on FDG PET/CT Scan in a Child with Hodgkin’s Lymphoma
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Inci Uslu Biner, Ebru Tatci, Ozlem Ozmen, Atila Gokcek, Haci Ahmet Demir, and Nadide Basak Gulleroglu
- Subjects
Medical physics. Medical radiology. Nuclear medicine ,R895-920 - Abstract
It is known that Fluorodeoxyglucose (FDG) Positron Emission/Computed Tomography (PET/CT) images may be helpful for evaluation of brain function in newborns. Here we described the fluorine-18 [18-F] FDG PET/CT imaging findings of encephalomalacia due to perinatal asphyxia in a child with refractory Hodgkin’s Lymphoma (HL) who underwent PET/CT scan to stage the primary disease. Prominent hypometabolism was incidentally detected in the occipital regions bilaterally apart from the FDG uptakes in the malign lymphatic infiltrations. This case highlights the potential coexistence of a malignancy and a functional brain disorder.
- Published
- 2016
- Full Text
- View/download PDF
26. Learning Fair Representations: Mitigating Statistical Dependencies
- Author
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Tayebi, Aida, Yazdani-Jahromi, Mehdi, Yalabadi, Ali Khodabandeh, Yousefi, Niloofar, Garibay, Ozlem Ozmen, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Degen, Helmut, editor, and Ntoa, Stavroula, editor
- Published
- 2024
- Full Text
- View/download PDF
27. FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation
- Author
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Khodabandeh Yalabadi, Ali, Yazdani-Jahromi, Mehdi, Yousefi, Niloofar, Tayebi, Aida, Abdidizaji, Sina, Garibay, Ozlem Ozmen, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, and Ma, Jian, editor
- Published
- 2024
- Full Text
- View/download PDF
28. Through a fair looking-glass: mitigating bias in image datasets
- Author
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Rajabi, Amirarsalan, Yazdani-Jahromi, Mehdi, Garibay, Ozlem Ozmen, and Sukthankar, Gita
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
With the recent growth in computer vision applications, the question of how fair and unbiased they are has yet to be explored. There is abundant evidence that the bias present in training data is reflected in the models, or even amplified. Many previous methods for image dataset de-biasing, including models based on augmenting datasets, are computationally expensive to implement. In this study, we present a fast and effective model to de-bias an image dataset through reconstruction and minimizing the statistical dependence between intended variables. Our architecture includes a U-net to reconstruct images, combined with a pre-trained classifier which penalizes the statistical dependence between target attribute and the protected attribute. We evaluate our proposed model on CelebA dataset, compare the results with a state-of-the-art de-biasing method, and show that the model achieves a promising fairness-accuracy combination.
- Published
- 2022
29. Distraction is All You Need for Fairness
- Author
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Yazdani-Jahromi, Mehdi, Rajabi, AmirArsalan, Yalabadi, Ali Khodabandeh, Tayebi, Aida, and Garibay, Ozlem Ozmen
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Bias in training datasets must be managed for various groups in classification tasks to ensure parity or equal treatment. With the recent growth in artificial intelligence models and their expanding role in automated decision-making, ensuring that these models are not biased is vital. There is an abundance of evidence suggesting that these models could contain or even amplify the bias present in the data on which they are trained, inherent to their objective function and learning algorithms; Many researchers direct their attention to this issue in different directions, namely, changing data to be statistically independent, adversarial training for restricting the capabilities of a particular competitor who aims to maximize parity, etc. These methods result in information loss and do not provide a suitable balance between accuracy and fairness or do not ensure limiting the biases in training. To this end, we propose a powerful strategy for training deep learning models called the Distraction module, which can be theoretically proven effective in controlling bias from affecting the classification results. This method can be utilized with different data types (e.g., Tabular, images, graphs, etc.). We demonstrate the potency of the proposed method by testing it on UCI Adult and Heritage Health datasets (tabular), POKEC-Z, POKEC-N and NBA datasets (graph), and CelebA dataset (vision). Using state-of-the-art methods proposed in the fairness literature for each dataset, we exhibit our model is superior to these proposed methods in minimizing bias and maintaining accuracy.
- Published
- 2022
30. TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks
- Author
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Rajabi, Amirarsalan and Garibay, Ozlem Ozmen
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Network for tabular data generation. The model includes two phases of training. In the first phase, the model is trained to accurately generate synthetic data similar to the reference dataset. In the second phase we modify the value function to add fairness constraint, and continue training the network to generate data that is both accurate and fair. We test our results in both cases of unconstrained, and constrained fair data generation. In the unconstrained case, i.e. when the model is only trained in the first phase and is only meant to generate accurate data following the same joint probability distribution of the real data, the results show that the model beats state-of-the-art GANs proposed in the literature to produce synthetic tabular data. Also, in the constrained case in which the first phase of training is followed by the second phase, we train the network and test it on four datasets studied in the fairness literature and compare our results with another state-of-the-art pre-processing method, and present the promising results that it achieves. Comparing to other studies utilizing GANs for fair data generation, our model is comparably more stable by using only one critic, and also by avoiding major problems of original GAN model, such as mode-dropping and non-convergence, by implementing a Wasserstein GAN.
- Published
- 2021
31. Approaching (super)human intent recognition in stag hunt with the Naïve Utility Calculus generative model
- Author
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Miranda, Lux and Garibary, Ozlem Ozmen
- Published
- 2023
- Full Text
- View/download PDF
32. Entropy-Based Heuristic Approach For The Quantum-Like Generalization of Social Contagion
- Author
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Mutlu, Ece Çiǧdem, Garibay, Ozlem Ozmen, Yang, Zining, editor, and Núñez-Corrales, Santiago, editor
- Published
- 2023
- Full Text
- View/download PDF
33. Improving Fairness via Deep Ensemble Framework Using Preprocessing Interventions
- Author
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Tayebi, Aida, Garibay, Ozlem Ozmen, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Degen, Helmut, editor, and Ntoa, Stavroula, editor
- Published
- 2023
- Full Text
- View/download PDF
34. Through a Fair Looking-Glass: Mitigating Bias in Image Datasets
- Author
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Rajabi, Amirarsalan, Yazdani-Jahromi, Mehdi, Garibay, Ozlem Ozmen, Sukthankar, Gita, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Degen, Helmut, editor, and Ntoa, Stavroula, editor
- Published
- 2023
- Full Text
- View/download PDF
35. Distance Correlation GAN: Fair Tabular Data Generation with Generative Adversarial Networks
- Author
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Rajabi, Amirarsalan, Garibay, Ozlem Ozmen, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Degen, Helmut, editor, and Ntoa, Stavroula, editor
- Published
- 2023
- Full Text
- View/download PDF
36. Approaching (super)human intent recognition in stag hunt with the Naïve Utility Calculus generative model.
- Author
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Lux Miranda and Ozlem Ozmen Garibary
- Published
- 2023
- Full Text
- View/download PDF
37. Entropy-Based Heuristic Approach For The Quantum-Like Generalization of Social Contagion
- Author
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Mutlu, Ece Çiǧdem, primary and Garibay, Ozlem Ozmen, additional
- Published
- 2023
- Full Text
- View/download PDF
38. Effects of Assortativity on Consensus Formation with Heterogeneous Agents
- Author
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Mutlu, Ece, Garibay, Ozlem Ozmen, Yang, Zining, editor, and von Briesen, Elizabeth, editor
- Published
- 2022
- Full Text
- View/download PDF
39. Strategies to Enhance University Economic Engagement: Evidence from US Universities
- Author
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Talebzadehhosseini, Seyyedmilad, Garibay, Ivan, Keathley-Herring, Heather, Al-Rawahi, Zahra Rashid Said, Garibay, Ozlem Ozmen, and Woodell, James K.
- Abstract
In an increasingly innovation-driven economic environment, universities serve as engines of economic growth by igniting innovation, fueling entrepreneurship, and inspiring the next generation of scientists and professionals. While universities are committed to enhancing their economic impact, university 'economic engagement' is in many ways an emerging field. This research investigates key strategies used by US research universities to drive economic engagement by analysing 55 successful applications for the Innovation and Economic Prosperity (IEP) University designation, which consist of extensive self-study exercises, using a grounded theory approach. Six key strategies emerge from this corpus: forming mutually beneficial partnerships with industry, developing collaboration networks with relevant communities, building an innovation culture, supporting researchers in bringing research outcomes to market, promoting the transfer of new technologies to industry, and encouraging entrepreneurial activities. These results can serve as a guide for universities seeking the best-practices to advance their economic engagement.
- Published
- 2021
- Full Text
- View/download PDF
40. A Quantum Leap for Fairness: Quantum Bayesian Approach for Fair Decision Making
- Author
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Mutlu, Ece, Garibay, Ozlem Ozmen, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Stephanidis, Constantine, editor, Kurosu, Masaaki, editor, Chen, Jessie Y. C., editor, Fragomeni, Gino, editor, Streitz, Norbert, editor, Konomi, Shin'ichi, editor, Degen, Helmut, editor, and Ntoa, Stavroula, editor
- Published
- 2021
- Full Text
- View/download PDF
41. Effects of Assortativity on Consensus Formation with Heterogeneous Agents
- Author
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Mutlu, Ece, primary and Garibay, Ozlem Ozmen, additional
- Published
- 2022
- Full Text
- View/download PDF
42. Examining sialic acid derivatives as potential inhibitors of SARS-CoV-2 spike protein receptor binding domain.
- Author
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Banerjee, Tanumoy, Gosai, Agnivo, Yousefi, Niloofar, Garibay, Ozlem Ozmen, Seal, Sudipta, and Balasubramanian, Ganesh
- Published
- 2024
- Full Text
- View/download PDF
43. A Quantum Leap for Fairness: Quantum Bayesian Approach for Fair Decision Making
- Author
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Mutlu, Ece, primary and Garibay, Ozlem Ozmen, additional
- Published
- 2021
- Full Text
- View/download PDF
44. Prophylaxis effects of water kefir on post‐infectious irritable bowel syndrome in rat model
- Author
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Çağlar Gökırmaklı, Zeki Erol, Ilhan Gun, Ozlem Ozmen, and Zeynep Banu Guzel‐Seydim
- Subjects
Industrial and Manufacturing Engineering ,Food Science - Published
- 2023
- Full Text
- View/download PDF
45. Na+/K+-ATPase and bone morphogenetic protein-2 expressions in parenchymal and microenvironmental cells of canine mammary tumours
- Author
-
Ozlem Ozmen
- Subjects
General Veterinary - Abstract
The most common canine tumour is mammary tumour, which resembles breast cancer in humans. Microenvironment is a crucial factor in the formation of breast cancers. In order to distinguish between benign and malignant canine mammary tumours, this study looked at the immunohistochemical expression of Na+/K+-ATPase and bone morphogenetic protein-2 (BMP-2) in tumour and microenvironmental cells. The aim of this study was to evaluate the expression of Na+/K+-ATPase and BMP-2 in canine mammary tumours and their relationship with malignancy. In this investigation, 10 normal breast tissues were used as controls, and 28 benign and 46 malignant mammary tumours were taken from the archives of the Department of Pathology. The findings showed that malignant tumours expressed more Na+/K+-ATPase and BMP-2 than did normal breast tissue. Both markers had a negative or slight expression in benign tumours, whereas they considerably increased in malignant tumours. Both tumour parenchymal and microenvironmental cells in malignancies expressed Na+/K+-ATPase and BMP-2. Na+/K+-ATPase expression was observed to be more prominent in cells when compared to BMP-2. These findings also suggest that Na+/K+-ATPase and BMP-2 could be employed in the future to help diagnose canine and possibly human breast cancers earlier or as possible targets for treatment.
- Published
- 2022
- Full Text
- View/download PDF
46. Mycobacteriosis in an appearantly healty atlantic mackerel (Scomber Scombrus, L.) and zoonotic potential
- Author
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Ozlem Ozmen
- Subjects
General Veterinary - Abstract
Mycobacteriosis was detected in seven out of one Atlantic mackerel (Scomber scombrus) that was purchased for human consumption from a fish market. The fish was apparently healthy but during cleaning, several granulomatous foci were noticed in the visceral organs. Histopathological examination of the lesions revealed numerous foci characterized by caseous necrosis in the center of the lesion surrounded by epithelioid giant cells. Ziehl-Neelsen staining revealed the presence of rod-shaped, acid-fast bacteria. Furthermore, immunohistochemical examination revealed the presence of a protein of mycobacterial origin in giant cells and macrophages. Based on gross and microscopic findings, mycobacteriosis was diagnosed. This report showed that due to its zoonotic potential, mycobacteriosis should be considered even in healthy-appearing fishes for human consumption.
- Published
- 2022
- Full Text
- View/download PDF
47. User interaction and cloud practices in mobile dairy farm management.
- Author
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Gonenc S. Tarakcioglu, Ali E. Demir, Cihan Bulbul, Cansu Karahasanoglu, Ozlem Ozmen Okur, and Yasemin Basatli
- Published
- 2015
- Full Text
- View/download PDF
48. Six human-centered artificial intelligence grand challenges
- Author
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Garibay, Ozlem Ozmen, Winslow, Brent, Andolina, Salvatore, Antona, Margherita, Bodenschatz, Anja, Coursaris, Constantinos K., Falco, Gregory, Fiore, Stephen, Garibay, Ivan, Grieman, Keri, Havens, John C., Jirotka, Marina, Kacorri, Hernisa, Karwowski, Waldemar, Kider, Joseph, Konstan, Joseph, Koon, Sean, Lopez-Gonzalez, Monica, Maifeld-Carucci, Iliana, McGregor, Sean, Salvendy, Gavriel, Shneiderman, Ben, Stephanidis, Constantine, Strobel, Christina, Holter, Carolyn Ten, Xu, Wei, Garibay, Ozlem Ozmen, Winslow, Brent, Andolina, Salvatore, Antona, Margherita, Bodenschatz, Anja, Coursaris, Constantinos K., Falco, Gregory, Fiore, Stephen, Garibay, Ivan, Grieman, Keri, Havens, John C., Jirotka, Marina, Kacorri, Hernisa, Karwowski, Waldemar, Kider, Joseph, Konstan, Joseph, Koon, Sean, Lopez-Gonzalez, Monica, Maifeld-Carucci, Iliana, McGregor, Sean, Salvendy, Gavriel, Shneiderman, Ben, Stephanidis, Constantine, Strobel, Christina, Holter, Carolyn Ten, and Xu, Wei
- Abstract
Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.
- Published
- 2023
49. Dexpanthenol protects against lipopolysaccharide-induced acute kidney injury by restoring AQP-2 levels via regulating SIRT-1 signaling pathway
- Author
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Eyyup S. Ozden, Halil Asci, Halil I. Buyukbayram, Mehmet A. Sevuk, Orhan B. Imeci, Hatice K. Dogan, and Ozlem Ozmen
- Subjects
Anesthesiology and Pain Medicine - Published
- 2023
- Full Text
- View/download PDF
50. BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing
- Author
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Niloofar Yousefi, Mehdi Yazdani-Jahromi, Aida Tayebi, Elayaraja Kolanthai, Craig J Neal, Tanumoy Banerjee, Agnivo Gosai, Ganesh Balasubramanian, Sudipta Seal, and Ozlem Ozmen Garibay
- Subjects
Molecular Biology ,Information Systems - Abstract
While research into drug–target interaction (DTI) prediction is fairly mature, generalizability and interpretability are not always addressed in the existing works in this field. In this paper, we propose a deep learning (DL)-based framework, called BindingSite-AugmentedDTA, which improves drug–target affinity (DTA) predictions by reducing the search space of potential-binding sites of the protein, thus making the binding affinity prediction more efficient and accurate. Our BindingSite-AugmentedDTA is highly generalizable as it can be integrated with any DL-based regression model, while it significantly improves their prediction performance. Also, unlike many existing models, our model is highly interpretable due to its architecture and self-attention mechanism, which can provide a deeper understanding of its underlying prediction mechanism by mapping attention weights back to protein-binding sites. The computational results confirm that our framework can enhance the prediction performance of seven state-of-the-art DTA prediction algorithms in terms of four widely used evaluation metrics, including concordance index, mean squared error, modified squared correlation coefficient ($r^2_m$) and the area under the precision curve. We also contribute to three benchmark drug–traget interaction datasets by including additional information on 3D structure of all proteins contained in those datasets, which include the two most commonly used datasets, namely Kiba and Davis, as well as the data from IDG-DREAM drug-kinase binding prediction challenge. Furthermore, we experimentally validate the practical potential of our proposed framework through in-lab experiments. The relatively high agreement between computationally predicted and experimentally observed binding interactions supports the potential of our framework as the next-generation pipeline for prediction models in drug repurposing.
- Published
- 2023
- Full Text
- View/download PDF
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