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4. A case report on dietary needs of the rowing athlete winner of the triple crown

Author

Giovanni Ficarra, Carmen Mannucci, Alessio Franco, Alessandra Bitto, Fabio Trimarchi, and Debora Di Mauro

Subjects
Rowing, Coastal rowing, Ergometer, Nutrition, Dietary habits, Case report, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Sports nutrition is a topic that is often misinterpreted and sometimes abused. The often-used ''shot-gun'' method to meeting caloric needs by simply increasing caloric intake disregarding nutrient source is scientifically contrary to the need for a more careful study of the nutritional requirements of rowers.Rowing by itself is a very demanding sport as typical training sessions use exercise intensities that range from very low submaximal work to short term supramaximal efforts. The intake in calories should take into account also the type of race that the athlete will afford.In the case of the rower Giovanni Ficarra who won, in 16 months, three world competitions in the three different rowing disciplines, his dietary needs and training schedule significantly changed according to the type of competition, to be prepared for the extreme load of physical effort required to get the podium in such different conditions. This article in intended to provide recommendation for both coaches and athletes that are approaching rowing in its whole meaning and disciplines as Giovanni Ficarra did.

Published
2024
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5. Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors

Author

Verena Bitto, Pia Hönscheid, María José Besso, Christian Sperling, Ina Kurth, Michael Baumann, and Benedikt Brors

Subjects
Biology (General), QH301-705.5
Abstract

Abstract Mass spectrometry imaging (MSI) allows to study cancer’s intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery.

Published
2024
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6. Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning

Author

Steinparz, Christian, Schmied, Thomas, Paischer, Fabian, Dinu, Marius-Constantin, Patil, Vihang, Bitto-Nemling, Angela, Eghbal-zadeh, Hamid, and Hochreiter, Sepp

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a plethora of research problems such as continual domain shifts, which result in non-stationary rewards and environment dynamics. These non-stationarities are difficult to detect and cope with due to their continuous nature. Therefore, exploration strategies and learning methods are required that are capable of tracking the steady domain shifts, and adapting to them. We propose Reactive Exploration to track and react to continual domain shifts in lifelong reinforcement learning, and to update the policy correspondingly. To this end, we conduct experiments in order to investigate different exploration strategies. We empirically show that representatives of the policy-gradient family are better suited for lifelong learning, as they adapt more quickly to distribution shifts than Q-learning. Thereby, policy-gradient methods profit the most from Reactive Exploration and show good results in lifelong learning with continual domain shifts. Our code is available at: https://github.com/ml-jku/reactive-exploration., Comment: CoLLAs 2022

Published
2022

7. Hopular: Modern Hopfield Networks for Tabular Data

Author

Schäfl, Bernhard, Gruber, Lukas, Bitto-Nemling, Angela, and Hochreiter, Sepp

Subjects
Computer Science - Machine Learning
Abstract

While Deep Learning excels in structured data as encountered in vision and natural language processing, it failed to meet its expectations on tabular data. For tabular data, Support Vector Machines (SVMs), Random Forests, and Gradient Boosting are the best performing techniques with Gradient Boosting in the lead. Recently, we saw a surge of Deep Learning methods that were tailored to tabular data but still underperform compared to Gradient Boosting on small-sized datasets. We suggest "Hopular", a novel Deep Learning architecture for medium- and small-sized datasets, where each layer is equipped with continuous modern Hopfield networks. The modern Hopfield networks use stored data to identify feature-feature, feature-target, and sample-sample dependencies. Hopular's novelty is that every layer can directly access the original input as well as the whole training set via stored data in the Hopfield networks. Therefore, Hopular can step-wise update its current model and the resulting prediction at every layer like standard iterative learning algorithms. In experiments on small-sized tabular datasets with less than 1,000 samples, Hopular surpasses Gradient Boosting, Random Forests, SVMs, and in particular several Deep Learning methods. In experiments on medium-sized tabular data with about 10,000 samples, Hopular outperforms XGBoost, CatBoost, LightGBM and a state-of-the art Deep Learning method designed for tabular data. Thus, Hopular is a strong alternative to these methods on tabular data., Comment: 9 pages (+ appendix); 5 figures; source code available at: https://github.com/ml-jku/hopular ; blog post available at: https://ml-jku.github.io/hopular/

Published
2022

8. History Compression via Language Models in Reinforcement Learning

Author

Paischer, Fabian, Adler, Thomas, Patil, Vihang, Bitto-Nemling, Angela, Holzleitner, Markus, Lehner, Sebastian, Eghbal-zadeh, Hamid, and Hochreiter, Sepp

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Statistics - Machine Learning
Abstract

In a partially observable Markov decision process (POMDP), an agent typically uses a representation of the past to approximate the underlying MDP. We propose to utilize a frozen Pretrained Language Transformer (PLT) for history representation and compression to improve sample efficiency. To avoid training of the Transformer, we introduce FrozenHopfield, which automatically associates observations with pretrained token embeddings. To form these associations, a modern Hopfield network stores these token embeddings, which are retrieved by queries that are obtained by a random but fixed projection of observations. Our new method, HELM, enables actor-critic network architectures that contain a pretrained language Transformer for history representation as a memory module. Since a representation of the past need not be learned, HELM is much more sample efficient than competitors. On Minigrid and Procgen environments HELM achieves new state-of-the-art results. Our code is available at https://github.com/ml-jku/helm., Comment: ICML 2022

Published
2022

10. A Dataset Perspective on Offline Reinforcement Learning

Author

Schweighofer, Kajetan, Radler, Andreas, Dinu, Marius-Constantin, Hofmarcher, Markus, Patil, Vihang, Bitto-Nemling, Angela, Eghbal-zadeh, Hamid, and Hochreiter, Sepp

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

The application of Reinforcement Learning (RL) in real world environments can be expensive or risky due to sub-optimal policies during training. In Offline RL, this problem is avoided since interactions with an environment are prohibited. Policies are learned from a given dataset, which solely determines their performance. Despite this fact, how dataset characteristics influence Offline RL algorithms is still hardly investigated. The dataset characteristics are determined by the behavioral policy that samples this dataset. Therefore, we define characteristics of behavioral policies as exploratory for yielding high expected information in their interaction with the Markov Decision Process (MDP) and as exploitative for having high expected return. We implement two corresponding empirical measures for the datasets sampled by the behavioral policy in deterministic MDPs. The first empirical measure SACo is defined by the normalized unique state-action pairs and captures exploration. The second empirical measure TQ is defined by the normalized average trajectory return and captures exploitation. Empirical evaluations show the effectiveness of TQ and SACo. In large-scale experiments using our proposed measures, we show that the unconstrained off-policy Deep Q-Network family requires datasets with high SACo to find a good policy. Furthermore, experiments show that policy constraint algorithms perform well on datasets with high TQ and SACo. Finally, the experiments show, that purely dataset-constrained Behavioral Cloning performs competitively to the best Offline RL algorithms for datasets with high TQ., Comment: Code: https://github.com/ml-jku/OfflineRL

Published
2021

11. CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

Author

Fürst, Andreas, Rumetshofer, Elisabeth, Lehner, Johannes, Tran, Viet, Tang, Fei, Ramsauer, Hubert, Kreil, David, Kopp, Michael, Klambauer, Günter, Bitto-Nemling, Angela, and Hochreiter, Sepp

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

CLIP yielded impressive results on zero-shot transfer learning tasks and is considered as a foundation model like BERT or GPT3. CLIP vision models that have a rich representation are pre-trained using the InfoNCE objective and natural language supervision before they are fine-tuned on particular tasks. Though CLIP excels at zero-shot transfer learning, it suffers from an explaining away problem, that is, it focuses on one or few features, while neglecting other relevant features. This problem is caused by insufficiently extracting the covariance structure in the original multi-modal data. We suggest to use modern Hopfield networks to tackle the problem of explaining away. Their retrieved embeddings have an enriched covariance structure derived from co-occurrences of features in the stored embeddings. However, modern Hopfield networks increase the saturation effect of the InfoNCE objective which hampers learning. We propose to use the InfoLOOB objective to mitigate this saturation effect. We introduce the novel "Contrastive Leave One Out Boost" (CLOOB), which uses modern Hopfield networks for covariance enrichment together with the InfoLOOB objective. In experiments we compare CLOOB to CLIP after pre-training on the Conceptual Captions and the YFCC dataset with respect to their zero-shot transfer learning performance on other datasets. CLOOB consistently outperforms CLIP at zero-shot transfer learning across all considered architectures and datasets., Comment: Published at NeurIPS 2022; Blog: https://ml-jku.github.io/cloob; GitHub: https://github.com/ml-jku/cloob

Published
2021

16. Acarbose suppresses symptoms of mitochondrial disease in a mouse model of Leigh syndrome

Author

Bitto, Alessandro, Grillo, Anthony S., Ito, Takashi K., Stanaway, Ian B., Nguyen, Bao M. G., Ying, Kejun, Tung, Herman, Smith, Kaleb, Tran, Ngoc, Velikanje, Gunnar, Urfer, Silvan R., Snyder, Jessica M., Barton, Jacob, Sharma, Ayush, Kayser, Ernst-Bernhard, Wang, Lu, Smith, Jr., Daniel L., Thompson, J. Will, DuBois, Laura, DePaolo, William, and Kaeberlein, Matt

Published
2023
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18. Lycopene and Garcinia cambogia Induce White-to-Brown Adipose Differentiation: An Innovative Strategy to Curb Obesity

Author

Federica Mannino, Vincenzo Arcoraci, Giovanna Vermiglio, Davide Labellarte, Igor Pirrotta, Domenico Antonio Giorgi, Alessandro Scarfone, Alessandra Bitto, Letteria Minutoli, Mario Vaccaro, Mariarosaria Galeano, Giovanni Pallio, and Natasha Irrera

Subjects
adipocyte differentiation, browning process, Garcinia cambogia, lycopene, obesity, uncoupling protein 1 (UCP1), Medicine, Pharmacy and materia medica, RS1-441
Abstract

Obesity is considered one of the main risk factors for cardiovascular diseases. The browning process has been recently recognized as a promising anti-obesity therapy. Lycopene (LYC) and Garcinia cambogia fruit extract (GE) might be important resources for anti-obesity drugs; therefore, the aim of this study was to investigate the anti-obesity effects of LYC and GE on 3T3-L1 adipocytes and Zucker rats. Mouse 3T3-L1 pre-adipocytes were differentiated in mature adipocytes and then treated with LYC (0.5 μM), GE (30 mg/mL) or LYC + GE for 24 h. Moreover, male Zucker Crl:ZUC-Leprfa rats were randomly assigned to 5 groups of 10 animals to orally receive Vehicle (Ctrl), Orlistat (20 mg/kg), LYC (5 mg/kg), GE (1000 mg/kg) or LYC + GE for 28 days. LYC, GC extracts and even more LYC + GE stimulated the mRNA and protein expression of thermogenic genes UCP1, CIDEA and DIO2, significantly reduced lipid droplet size and increased lipid droplet number in adipocytes. UCP1 mRNA and protein expression was also increased in the visceral adipose tissue of the rats that received the dietary intake of LYC, GE and even more LYC + GE. Moreover, LYC + GE induced the reorganization of visceral fat depots that showed a great number of small adipocytes and a significant reduction in weight gain and food intake compared to the control group. The obtained results demonstrated that LYC + GE might be used as new approaches for obesity management in order to induce the browning process and achieve a metabolically active tissue instead of a tissue characterized by lipid depot accumulation.

Published
2024
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20. Potato-Net: Classifying Potato Leaf Diseases Using Transfer Learning Approach

Author

Bitto, Abu Kowshir, Bijoy, Md.Hasan Imam, Das, Aka, Rahman, Md.Ashikur, Rabbani, Masud, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Satu, Md. Shahriare, editor, Moni, Mohammad Ali, editor, Kaiser, M. Shamim, editor, and Arefin, Mohammad Shamsul, editor

Published
2023
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21. Tumor-TL: A Transfer Learning Approach for Classifying Brain Tumors from MRI Images

Author

Bitto, Abu Kowshir, Bijoy, Md. Hasan Imam, Yesmin, Sabina, Mia, Md. Jueal, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Satu, Md. Shahriare, editor, Moni, Mohammad Ali, editor, Kaiser, M. Shamim, editor, and Arefin, Mohammad Shamsul, editor

Published
2023
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22. Approach of Different Classification Algorithms to Compare in N-gram Feature Between Bangla Good and Bad Text Discourses

Author

Bitto, Abu Kowshir, Bijoy, Md. Hasan Imam, Khan, Saima, Mahmud, Imran, Biplob, Khalid Been Badruzzaman, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Sisodia, Dilip Singh, editor, Garg, Lalit, editor, Pachori, Ram Bilas, editor, and Tanveer, M., editor

Published
2023
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23. A Data-Driven Approach to Forecasting Bangladesh Next-Generation Economy

Author

Shohug, Md. Mahfuj Hasan, Bitto, Abu Kowshir, Rubi, Maksuda Akter, Bijoy, Md. Hasan Imam, Rahaman, Ashikur, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep, editor, Singh, Deepak, editor, Tiwari, Vivek, editor, and Misra, Sanjay, editor

Published
2023
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24. Predicting Bangladesh Life Expectancy Using Multiple Depend Features and Regression Models

Author

Tuj Jannat, Fatema, Biplob, Khalid Been Md. Badruzzaman, Bitto, Abu Kowshir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep, editor, Singh, Deepak, editor, Tiwari, Vivek, editor, and Misra, Sanjay, editor

Published
2023
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25. Acquiring a Stance on Equity and Social Justice within an Online Mathematics Specialist Program

Author

Baker, Courtney K., Galanti, Terrie M., Bitto, Laura, Wills, Theresa, and Eatmon, Cassandra Cook

Abstract

This research report will discuss mathematics specialists and the way in which they communicate ideas of equity and social justice within a master's program that leads to a state endorsement in mathematics education leadership. Many mathematics specialist programs focus on developing their mathematical content knowledge for teaching, but these specialized leaders also need to learn about curriculum, assessment, pedagogy, and leadership for school change. This research describes one mathematics specialist program's initial efforts in developing mathematics teacher leaders who take action within their K-12 school contexts. Preliminary findings suggest that the mathematics specialists within this program are aligning their views of equitable mathematics instruction with Principles to Actions (National Council of Teachers of Mathematics, 2014) and the Teaching for Robust Understanding Framework (Schoenfeld, 2015). Implications for the program are that we are still exploring how to incorporate these ideas and facilitate critical conversations as teacher educators within our classrooms. [For the complete proceedings, see ED606556.]

Published
2019

26. Porto-sinusoidal vascular liver disorder with portal hypertension: Natural history and long-term outcome

Author

Magaz, Marta, Giudicelli-Lett, Heloïse, Abraldes, Juan G., Nicoară-Farcău, Oana, Turon, Fanny, Rajoriya, Neil, Goel, Ashish, Raymenants, Karlien, Hillaire, Sophie, Téllez, Luis, Elkrief, Laure, Procopet, Bogdan, Orts, Lara, Nery, Filipe, Shukla, Akash, Larrue, Hélène, Degroote, Helena, Aguilera, Victoria, Llop, Elba, Turco, Laura, Indulti, Federica, Gioia, Stefania, Tosetti, Giulia, Bitto, Niccolò, Becchetti, Chiara, Alvarado, Edilmar, Roig, Cristina, Diaz, Raquel, Praktiknjo, Michael, Konicek, Anna-Lena, Olivas, Pol, Fortea, José Ignacio, Masnou, Helena, Puente, Ángela, Ardèvol, Alba, Navascués, Carmen A., Romero-Gutiérrez, Marta, Scheiner, Bernhard, Semmler, Georg, Mandorfer, Mattias, Damião, Filipe, Baiges, Anna, Ojeda, Asunción, Simón-Talero, Macarena, González-Alayón, Carlos, Díaz, Alba, García-Criado, Ángeles, De Gottardi, Andrea, Hernández-Guerra, Manuel, Genescà, Joan, Drilhon, Nicolas, Noronha Ferreira, Carlos, Reiberger, Thomas, Rodríguez, Manuel, Morillas, Rosa María, Crespo, Javier, Trebicka, Jonel, Bañares, Rafael, Villanueva, Càndid, Berzigotti, Annalisa, Primignani, Massimo, La Mura, Vincenzo, Riggio, Oliviero, Schepis, Filippo, Verhelst, Xavier, Calleja, José Luis, Bureau, Christophe, Albillos, Agustín, Nevens, Frederik, Hernández-Gea, Virginia, Tripathi, Dhiraj, Rautou, Pierre-Emmanuel, and García-Pagán, Juan Carlos

Published
2024
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27. Prognostic biomarkers for the response to the radiosensitizer nimorazole combined with RCTx: a pre-clinical trial in HNSCC xenografts

Author

Lydia Koi, Verena Bitto, Corina Weise, Lisa Möbius, Annett Linge, Steffen Löck, Ala Yaromina, María José Besso, Chiara Valentini, Manuel Pfeifer, Jens Overgaard, Daniel Zips, Ina Kurth, Mechthild Krause, and Michael Baumann

Subjects
HNSCC, Hypoxia, Radioresistance, Radiotherapy, Radiochemotherapy, Radiosensitizer, Medicine
Abstract

Abstract Background Tumor hypoxia is associated with resistance to radiotherapy and chemotherapy. In head and neck squamous cell carcinoma (HNSCC), nimorazole, an oxygen mimic, combined with radiotherapy (RT) enabled to improve loco-regional control (LRC) in some patients with hypoxic tumors but it is unknown whether this holds also for radiochemotherapy (RCTx). Here, we investigated the impact of nimorazole combined with RCTx in HNSCC xenografts and explored molecular biomarkers for its targeted use. Methods Irradiations were performed with 30 fractions in 6 weeks combined with weekly cisplatin. Nimorazole was applied before each fraction, beginning with the first or after ten fractions. Effect of RCTx with or without addition of nimorazole was quantified as permanent local control after irradiation. For histological evaluation and targeted gene expression analysis, tumors were excised untreated or after ten fractions. Using quantitative image analysis, micromilieu parameters were determined. Results Nimorazole combined with RCTx significantly improved permanent local control in two tumor models, and showed a potential improvement in two additional models. In these four models, pimonidazole hypoxic volume (pHV) was significantly reduced after ten fractions of RCTx alone. Our results suggest that nimorazole combined with RCTx might improve TCR compared to RCTx alone if hypoxia is decreased during the course of RCTx but further experiments are warranted to verify this association. Differential gene expression analysis revealed 12 genes as potential for RCTx response. When evaluated in patients with HNSCC who were treated with primary RCTx, these genes were predictive for LRC. Conclusions Nimorazole combined with RCTx improved local tumor control in some but not in all HNSCC xenografts. We identified prognostic biomarkers with the potential for translation to patients with HNSCC.

Published
2023
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28. Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images

Author

Abu Kowshir Bitto, Md. Hasan Imam Bijoy, Sabina Yesmin, Imran Mahmud, Md. Jueal Mia, and Khalid Been Badruzzaman Biplob

Subjects
brain tumor, mri images, vgg16, vgg19, resnet50., Electronic computers. Computer science, QA75.5-76.95
Abstract

Abnormal brain tissue or cell growth is known as a brain tumor. One of the body's most intricate organs is the brain, where billions of cells work together. As a head tumor grows, the brain suffers damage due to its increasingly dense core. Magnetic resonance imaging, or MRI, is a type of medical imaging that enables radiologists to view the inside of body structures without the need for surgery. The image-based medical diagnosis expert system is crucial for a brain tumor patient. In this study, we combined two Magnetic Resonance Imaging (MRI)-based image datasets from Figshare and Kaggle to identify brain tumor MRI using a variety of convolutional neural network designs. To achieve competitive performance, we employ several data preprocessing techniques, such as resizing and enhancing contrast. The image augmentation techniques (E.g., rotated, width shifted, height shifted, shear shifted, and horizontally flipped) are used to increase data size, and five pre-trained models employed, including VGG-16, VGG-19, ResNet-50, Xception, and Inception-V3. The model with the highest accuracy, ResNet-50, performs at 96.76 percent. The model with the highest precision overall is Inception V3, with a precision score of 98.83 percent. ResNet-50 performs at 96.96% for F1-Score. The prominent accuracy of the implemented model, i.e., ResNet-50, compared with several earlier studies to validate the consequence of this introspection. The outcome of this study can be used in the medical diagnosis of brain tumors with an MRI-based expert system.

Published
2023
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29. 'I could do almost nothing without digital technology': a qualitative exploration of adolescents’ perception of the risks and challenges of digital technology

Author

Laura Bitto Urbanova, Andrea Madarasova Geckova, Zuzana Dankulincova Veselska, Silvia Capikova, Jana Holubcikova, Jitse P. van Dijk, and Sijmen A. Reijneveld

Subjects
qualitative study, digital technology, subjective perceptions, risks, adolescents, Psychology, BF1-990
Abstract

BackgroundThe fast development of digital technology and of its use at even younger ages is significantly shaping the current generation of adolescents. This is leading to an almost unlimited accessibility that provides a large number of opportunities, but also to many challenges that adolescents have to face. The aim of our study was to explore the perceptions adolescents have of the risks of digital technology.MethodsWe conducted online semi-structured interviews as a part of the international Health Behaviour in School-aged Children study. The sample consisted of 15 Slovak adolescents (mean age: 15.33; 20% boys). To analyse our data, we used consensual qualitative research and thematic analysis.FindingsOur findings confirmed that adolescents are aware of the risks associated with the use of digital technology. Regarding their specific types of the perceived risks, we identified four main themes: 1. dependence on the functionality of technology; 2. problematic control; 3. vulnerability in the virtual environment; 4. health risks. Adolescents thus want technology that is functional, safe and does not endanger their health.ConclusionDespite the fact that adolescents know of the risks they may experience due the digital technology, they still use it. Preventive strategies should focus on functionality, safety and healthiness; furthermore, they should support the constant development of adolescents’ digital awareness and raising their awareness about effective and non-threating use of technology.

Published
2023
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30. 17‐a‐estradiol late in life extends lifespan in aging UM‐HET3 male mice; nicotinamide riboside and three other drugs do not affect lifespan in either sex

Author

Harrison, David E, Strong, Randy, Reifsnyder, Peter, Kumar, Navasuja, Fernandez, Elizabeth, Flurkey, Kevin, Javors, Martin A, Lopez‐Cruzan, Marisa, Macchiarini, Francesca, Nelson, James F, Markewych, Adrian, Bitto, Alessandro, Sindler, Amy L, Cortopassi, Gino, Kavanagh, Kylie, Leng, Lin, Bucala, Richard, Rosenthal, Nadia, Salmon, Adam, Stearns, Timothy M, Bogue, Molly, and Miller, Richard A

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Aging, Estrogen, Good Health and Well Being, Animals, Estradiol, Female, Longevity, Male, Mice, Niacinamide, Pyridinium Compounds, Sex Characteristics, 17&#8208, &#945, &#8208, estradiol, candesartan cilexetil, geranylgeranylacetone, heterogeneous mice, lifespan, macrophage migration inhibitory factor, nicotinamide riboside, 17-α-estradiol, Biological Sciences, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

In genetically heterogeneous mice produced by the CByB6F1 x C3D2F1 cross, the "non-feminizing" estrogen, 17-α-estradiol (17aE2), extended median male lifespan by 19% (p

Published
2021

31. Choking Effect, Learning School Performance. What Role do Emotional and Motivational Processes Play?

Author

Carmela Mento, Maria Catena Silvestri, Dominique Zanghi, Floriana Bitto, Abed Hadipour Lakmehsari, and Francesco Pira

Subjects
motivation, learning, choking effect, academic success, Education
Abstract

Introduction: In recent decades, the relationship between motivation and learning have attracted interest and attention among researchers with many studies examining the variables that characterize and influence them and their relationship. Research Aim: The aim of this review is to analyze the role of motivation in learning as well as useful strategies to increase motivation to study. Evidence-based Facts: This narrative review is based on a detailed analysis of the data collected from the empirical studies on the topic. The main theories on motivation to learn such as the theory of attribution and socio-cognitive theory that are centered on self-efficacy, expectation of results, and self-regulation are described and discussed focusing on the expectation of success and the value of the task. Particular importance has been given to the detection of learning motivation in relation to the suffocation effect as an important motivational construct in learning outcomes, being related to personality variables, emotional intelligence, and academic success. Finally, the strategic indicators for improving academic success are discussed. Summary: This review highlights the importance of motivation to learn a task, that is associated with personality, emotional control, and emotional intelligence that helps the individual in determining useful study strategies which can lead to greater academic success.

Published
2023
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35. Interrogating the Tensions of Becoming Antiracist Mathematics Teacher Educators via Critical Friendship and Rehearsals

Author

Baker, Courtney K. and Bitto, Laura E.

Abstract

In this article, we explore our experienced tensions as we accepted responsibility for the privilege of our lived experiences towards becoming antiracist mathematics teacher educators. We employed a collaborative self-study to examine and uncover the ways in which existing systemic barriers were mirrored in our own practices. Weekly dialogue centered on self-assigned readings as they pertained to our teaching. Qualitative data sources included agendas, reflections, transcripts of dialogue, and course materials. We experienced multiple tensions, and found that engaging in rehearsals afforded us opportunities to move beyond inaction and grapple with our internalized racism. While our critical friendship provided an avenue to monitor our (in)actions and apply dialogue to rehearse and lean into our tensions, we experienced many missteps and failed to consider our identities. Our experienced tensions may be common to other White teacher educators who also stand at this critical nexus.

Published
2022
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36. Testosterone:cortisol ratio as a predictor of podium in adolescent rowing athletes

Author

Giovanni Ficarra, Daniela Caccamo, Michelangelo Rottura, Alessandra Bitto, Fabio Trimarchi, and Debora Di Mauro

Subjects
Cortisol, Testosterone, Rowing, Biomarker, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Purpose: There is a lack of data regarding the stress and motivation response in adolescent athletes during competitions. The athletic performance can be highly influenced by stress rather than appropriate training, at this age. The aim of this investigation is to evaluate the level of stress markers in adolescent rowers in different competition settings that might alter their stress status and performance. Methods: Adolescent rowing athletes (12–18 yrs) have been tested for determining saliva content of stress biomarkers, cortisol and testosterone, before and after competitions that have been performed indoor and outdoor. Specifically, samples have been taken in the morning, before and after the race in 2 different settings: 1) an indoor rowing competition with an ergometer, 2) an outdoor rowing competition on boats. Results: A reduction in cortisol levels has been observed in athletes right before the outdoor race, while testosterone levels increased at the same time point before either the ergometer or boat competition and kept rising at the end of the race. Significant differences have been found comparing the testosterone/cortisol ratio between indoor and outdoor data, being higher in the indoor race at all considered time-point. Furthermore, the linear regression demonstrated that the increased ratio correlated with a better podium position in the indoor race. Conclusion: Despite the age differences among athletes might have an influence on their hormone levels, these data suggest that rowing athletes subjected to different kind of competitions show a different stress and motivation response profile that might influence their performance.

Published
2023
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37. Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP

Author

Knaus, Peter, Bitto-Nemling, Angela, Cadonna, Annalisa, and Frühwirth-Schnatter, Sylvia

Subjects
Economics - Econometrics, Statistics - Computation
Abstract

Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting in TVP models is well known. This issue can be dealt with using appropriate global-local shrinkage priors, which pull time-varying parameters towards static ones. In this paper, we introduce the R package shrinkTVP (Knaus, Bitto-Nemling, Cadonna, and Fr\"uhwirth-Schnatter 2019), which provides a fully Bayesian implementation of shrinkage priors for TVP models, taking advantage of recent developments in the literature, in particular that of Bitto and Fr\"uhwirth-Schnatter (2019). The package shrinkTVP allows for posterior simulation of the parameters through an efficient Markov Chain Monte Carlo (MCMC) scheme. Moreover, summary and visualization methods, as well as the possibility of assessing predictive performance through log predictive density scores (LPDSs), are provided. The computationally intensive tasks have been implemented in C++ and interfaced with R. The paper includes a brief overview of the models and shrinkage priors implemented in the package. Furthermore, core functionalities are illustrated, both with simulated and real data.

Published
2019

38. Enhanced SARS-CoV-2 entry via UPR-dependent AMPK-related kinase NUAK2

Author

Prasad, Vibhu, Cerikan, Berati, Stahl, Yannick, Kopp, Katja, Magg, Vera, Acosta-Rivero, Nelson, Kim, Heeyoung, Klein, Katja, Funaya, Charlotta, Haselmann, Uta, Cortese, Mirko, Heigwer, Florian, Bageritz, Josephine, Bitto, David, Jargalsaikhan, Saruul, Neufeldt, Christopher, Pahmeier, Felix, Boutros, Michael, Yamauchi, Yohei, Ruggieri, Alessia, and Bartenschlager, Ralf

Published
2023
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50. Effects of genistein aglycone in glucocorticoid induced osteoporosis: A randomized clinical trial in comparison with alendronate

Author

Francesco Squadrito, Egidio Imbalzano, Michelangelo Rottura, Vincenzo Arcoraci, Giovanni Pallio, Antonino Catalano, Marco Atteritano, Natasha Irrera, Federica Mannino, Giovanni Squadrito, Mario Vaccaro, Pierangela Irrera, Igor Pirrotta, and Alessandra Bitto

Subjects
Bone remodeling, Clinical trials, Genistein, Osteoporosis, Therapeutics. Pharmacology, RM1-950
Abstract

Glucocorticoid-induced osteoporosis (GIO) complicates the clinical management of patients subjected to long-term glucocorticoid use. This study explored the effects of genistein on bone loss in a randomized double-blind alendronate-controlled trial in postmenopausal women with GIO. 200 postmenopausal women (taking at least 5 mg of prednisone equivalents) since 3 months, or more, and expected to continue for at least other 12 months, were randomized to receive genistein (54 mg/day daily) or alendronate (70 mg once a week) for 24 months. Both groups received also Calcium and Vitamin D3 supplementation. Median bone mineral density (BMD) at the antero-posterior lumbar spine significantly increased from 0.75 g/cm2 at baseline to 0.77 g/cm2 at 1 year and 0.79 g/cm2 at 2 years in alendronate-treated patients and from 0.77 g/cm2 at baseline to 0.79 g/cm2 at 12 months and to 0.80 g/cm2 at 24 months in genistein recipients. No difference was observed between the two treatments. Median BMD at the femoral neck increased from 0.67 g/cm2 at baseline to 0.68 g/cm2 at 1 year and 0.69 g/cm2 at 2 years in alendronate-treated patients and from 0.68 g/cm2 at baseline to 0.70 g/cm2 at 12 months and to 0.71 g/cm2 at 24 months in genistein recipients. No difference was observed between alendronate and genistein groups in BMD. Regarding bone markers genistein and alendronate statistically decreased c-terminal telopeptide, while osteocalcin, bone-ALP, and sclerostin showed greater changes in genistein treated patients. This randomized clinical trial suggests that genistein aglycone represents an additional therapeutic option for patients with GIO.

Published
2023
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