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1. Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

Author

da Silva, Rafael Ferreira, Bard, Deborah, Chard, Kyle, de Witt, Shaun, Foster, Ian T., Gibbs, Tom, Goble, Carole, Godoy, William, Gustafsson, Johan, Haus, Utz-Uwe, Hudson, Stephen, Jha, Shantenu, Los, Laila, Paine, Drew, Suter, Frédéric, Ward, Logan, Wilkinson, Sean, Amaris, Marcos, Babuji, Yadu, Bader, Jonathan, Balin, Riccardo, Balouek, Daniel, Beecroft, Sarah, Belhajjame, Khalid, Bhattarai, Rajat, Brewer, Wes, Brunk, Paul, Caino-Lores, Silvina, Casanova, Henri, Cassol, Daniela, Coleman, Jared, Coleman, Taina, Colonnelli, Iacopo, Da Silva, Anderson Andrei, de Oliveira, Daniel, Elahi, Pascal, Elfaramawy, Nour, Elwasif, Wael, Etz, Brian, Fahringer, Thomas, Ferreira, Wesley, Filgueira, Rosa, Tande, Jacob Fosso, Gadelha, Luiz, Gallo, Andy, Garijo, Daniel, Georgiou, Yiannis, Gritsch, Philipp, Grubel, Patricia, Gueroudji, Amal, Guilloteau, Quentin, Hamalainen, Carlo, Enriquez, Rolando Hong, Huet, Lauren, Kesling, Kevin Hunter, Iborra, Paula, Jahangiri, Shiva, Janssen, Jan, Jordan, Joe, Kanwal, Sehrish, Kunstmann, Liliane, Lehmann, Fabian, Leser, Ulf, Li, Chen, Liu, Peini, Luettgau, Jakob, Lupat, Richard, Fernandez, Jose M., Maheshwari, Ketan, Malik, Tanu, Marquez, Jack, Matsuda, Motohiko, Medic, Doriana, Mohammadi, Somayeh, Mulone, Alberto, Navarro, John-Luke, Ng, Kin Wai, Noelp, Klaus, Kinoshita, Bruno P., Prout, Ryan, Crusoe, Michael R., Ristov, Sashko, Robila, Stefan, Rosendo, Daniel, Rowell, Billy, Rybicki, Jedrzej, Sanchez, Hector, Saurabh, Nishant, Saurav, Sumit Kumar, Scogland, Tom, Senanayake, Dinindu, Shin, Woong, Sirvent, Raul, Skluzacek, Tyler, Sly-Delgado, Barry, Soiland-Reyes, Stian, Souza, Abel, Souza, Renan, Talia, Domenico, Tallent, Nathan, Thamsen, Lauritz, Titov, Mikhail, Tovar, Benjamin, Vahi, Karan, Vardar-Irrgang, Eric, Vartina, Edite, Wang, Yuandou, Wouters, Merridee, Yu, Qi, Bkhetan, Ziad Al, and Zulfiqar, Mahnoor

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

Published
2024
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2. Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling

Author

Barwey, Shivam, Balin, Riccardo, Lusch, Bethany, Patel, Saumil, Balakrishnan, Ramesh, Pal, Pinaki, Maulik, Romit, and Vishwanath, Venkatram

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning, Physics - Computational Physics
Abstract

This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is arithmetically equivalent to evaluations on multiple ranks (a partitioned graph). This concept is demonstrated by interfacing GNNs with NekRS, a GPU-capable exascale CFD solver developed at Argonne National Laboratory. It is shown how the NekRS mesh partitioning can be linked to the distributed GNN training and inference routines, resulting in a scalable mesh-based data-driven modeling workflow. We study the impact of consistency on the scalability of mesh-based GNNs, demonstrating efficient scaling in consistent GNNs for up to O(1B) graph nodes on the Frontier exascale supercomputer.

Published
2024

3. Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks

Author

Barwey, Shivam, Pal, Pinaki, Patel, Saumil, Balin, Riccardo, Lusch, Bethany, Vishwanath, Venkatram, Maulik, Romit, and Balakrishnan, Ramesh

Subjects
Physics - Fluid Dynamics, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Physics - Computational Physics
Abstract

A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN representations in a manner similar to spectral (or finite) element discretizations, a baseline GNN layer (termed a message passing layer, which updates local node properties) is modified to account for synchronization of coincident graph nodes, rendering compatibility with commonly used element-based mesh connectivities. The architecture is multiscale in nature, and is comprised of a combination of coarse-scale and fine-scale message passing layer sequences (termed processors) separated by a graph unpooling layer. The coarse-scale processor embeds a query element (alongside a set number of neighboring coarse elements) into a single latent graph representation using coarse-scale synchronized message passing over the element neighborhood, and the fine-scale processor leverages additional message passing operations on this latent graph to correct for interpolation errors. Demonstration studies are performed using hexahedral mesh-based data from Taylor-Green Vortex flow simulations at Reynolds numbers of 1600 and 3200. Through analysis of both global and local errors, the results ultimately show how the GNN is able to produce accurate super-resolved fields compared to targets in both coarse-scale and multiscale model configurations.

Published
2024

4. A Scalable and Effective Alternative to Graph Transformers

Author

Sancak, Kaan, Hua, Zhigang, Fang, Jin, Xie, Yan, Malevich, Andrey, Long, Bo, Balin, Muhammed Fatih, and Çatalyürek, Ümit V.

Subjects
Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power. To address this, Graph Transformers (GTs) were introduced, utilizing self-attention mechanism to effectively model pairwise node relationships. Despite their advantages, GTs suffer from quadratic complexity w.r.t. the number of nodes in the graph, hindering their applicability to large graphs. In this work, we present Graph-Enhanced Contextual Operator (GECO), a scalable and effective alternative to GTs that leverages neighborhood propagation and global convolutions to effectively capture local and global dependencies in quasilinear time. Our study on synthetic datasets reveals that GECO reaches 169x speedup on a graph with 2M nodes w.r.t. optimized attention. Further evaluations on diverse range of benchmarks showcase that GECO scales to large graphs where traditional GTs often face memory and time limitations. Notably, GECO consistently achieves comparable or superior quality compared to baselines, improving the SOTA up to 4.5%, and offering a scalable and effective solution for large-scale graph learning., Comment: Under submission

Published
2024

6. Cooperative Minibatching in Graph Neural Networks

Author

Balin, Muhammed Fatih, LaSalle, Dominique, and Çatalyürek, Ümit V.

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Training large scale Graph Neural Networks (GNNs) requires significant computational resources, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch training coupled with graph sampling. GNNs have the unique property that items in a minibatch have overlapping data. However, the commonly implemented Independent Minibatching approach assigns each Processing Element (PE, i.e., cores and/or GPUs) its own minibatch to process, leading to duplicated computations and input data access across PEs. This amplifies the Neighborhood Explosion Phenomenon (NEP), which is the main bottleneck limiting scaling. To reduce the effects of NEP in the multi-PE setting, we propose a new approach called Cooperative Minibatching. Our approach capitalizes on the fact that the size of the sampled subgraph is a concave function of the batch size, leading to significant reductions in the amount of work as batch sizes increase. Hence, it is favorable for processors equipped with a fast interconnect to work on a large minibatch together as a single larger processor, instead of working on separate smaller minibatches, even though global batch size is identical. We also show how to take advantage of the same phenomenon in serial execution by generating dependent consecutive minibatches. Our experimental evaluations show up to 4x bandwidth savings for fetching vertex embeddings, by simply increasing this dependency without harming model convergence. Combining our proposed approaches, we achieve up to 64% speedup over Independent Minibatching on single-node multi-GPU systems, using same resources., Comment: Under submission. Was first proposed in Aug 2022 in https://github.com/dmlc/dgl/pull/4337 and submitted to ICML in Jan 2023

Published
2023

7. SGORP: A Subgradient-based Method for d-Dimensional Rectilinear Partitioning

Author

Balin, Muhammed Fatih, An, Xiaojing, Yaşar, Abdurrahman, and Çatalyürek, Ümit V.

Subjects
Computer Science - Data Structures and Algorithms
Abstract

Partitioning for load balancing is a crucial first step to parallelize any type of computation. In this work, we propose SGORP, a new spatial partitioning method based on Subgradient Optimization, to solve the $d$-dimensional Rectilinear Partitioning Problem (RPP). Our proposed method allows the use of customizable objective functions as well as some user-specific constraints, such as symmetric partitioning on selected dimensions. Extensive experimental evaluation using over 600 test matrices shows that our algorithm achieves favorable performance against the state-of-the-art RPP and Symmetric RPP algorithms. Additionally, we show the effectiveness of our algorithm to do application-specific load balancing using two applications as motivation: Triangle Counting and Sparse Matrix Multiplication (SpGEMM), where we model their load-balancing problems as $3$-dimensional RPPs.

Published
2023

8. In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD

Author

Balin, Riccardo, Simini, Filippo, Simpson, Cooper, Shao, Andrew, Rigazzi, Alessandro, Ellis, Matthew, Becker, Stephen, Doostan, Alireza, Evans, John A., and Jansen, Kenneth E.

Subjects
Computer Science - Machine Learning, Physics - Fluid Dynamics
Abstract

Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. Additionally, performing inference at runtime requires non-trivial coupling of ML framework libraries with simulation codes. This work offers a solution to both limitations by simplifying this coupling and enabling in situ training and inference workflows on heterogeneous clusters. Leveraging SmartSim, the presented framework deploys a database to store data and ML models in memory, thus circumventing the file system. On the Polaris supercomputer, we demonstrate perfect scaling efficiency to the full machine size of the data transfer and inference costs thanks to a novel co-located deployment of the database. Moreover, we train an autoencoder in situ from a turbulent flow simulation, showing that the framework overhead is negligible relative to a solver time step and training epoch.

Published
2023

9. Turbulent boundary layer with strong favorable pressure gradient and curvature effects: Streamline coordinate and scaling analysis

Author

Prakash, Aviral, Balin, Riccardo, Evans, John A., and Jansen, Kenneth E.

Subjects
Physics - Fluid Dynamics
Abstract

Direct numerical simulation (DNS) of a turbulent boundary layer over the Gaussian (Boeing) bump is performed. This boundary layer exhibits a series of adverse and favorable pressure gradients and convex and concave curvature effects before separating. These effects on turbulent boundary layers are characterized and compared to a lower Reynolds number flow over the same geometry. The momentum budgets are analyzed in the streamline-aligned coordinate system upstream of the separation region. These momentum budgets allow the simplification of equations to facilitate an integral analysis. Integral analysis-based scalings for Reynolds stresses in the inner and outer regions of the boundary layer are also formulated. These proposed scalings exhibit a better collapse of Reynolds stress profiles compared to friction velocity scaling and Zagarola-Smits scaling in the strong favorable pressure gradient region and in the mild adverse pressure region that precedes it in this flow.

Published
2023

10. Establishment of a consensus protocol to explore the brain pathobiome in patients with mild cognitive impairment and Alzheimers disease: Research outline and call for collaboration.

Author

Lathe, Richard, Schultek, Nikki, Balin, Brian, Ehrlich, Garth, Auber, Lavinia, Perry, George, Breitschwerdt, Edward, Corry, David, Doty, Richard, Nara, Peter, Itzhaki, Ruth, Eimer, William, Tanzi, Rudolph, and Rissman, Robert

Subjects
Alzheimers disease, antimicrobial, antiviral, bioinformatics, blood, cerebrospinal fluid, collaboration, dementia, diagnosis, methodology, microbiome, mild cognitive impairment, olfactory neuroepithelium, pathobiome, polymerase chain reaction, protocol, sequencing, Humans, Alzheimer Disease, Consensus, Cognitive Dysfunction, Brain
Abstract

Microbial infections of the brain can lead to dementia, and for many decades microbial infections have been implicated in Alzheimers disease (AD) pathology. However, a causal role for infection in AD remains contentious, and the lack of standardized detection methodologies has led to inconsistent detection/identification of microbes in AD brains. There is a need for a consensus methodology; the Alzheimers Pathobiome Initiative aims to perform comparative molecular analyses of microbes in post mortem brains versus cerebrospinal fluid, blood, olfactory neuroepithelium, oral/nasopharyngeal tissue, bronchoalveolar, urinary, and gut/stool samples. Diverse extraction methodologies, polymerase chain reaction and sequencing techniques, and bioinformatic tools will be evaluated, in addition to direct microbial culture and metabolomic techniques. The goal is to provide a roadmap for detecting infectious agents in patients with mild cognitive impairment or AD. Positive findings would then prompt tailoring of antimicrobial treatments that might attenuate or remit mounting clinical deficits in a subset of patients.

Published
2023

11. Expansion of memory Vδ2 T cells following SARS-CoV-2 vaccination revealed by temporal single-cell transcriptomics

Author

Terzoli, Sara, Marzano, Paolo, Cazzetta, Valentina, Piazza, Rocco, Sandrock, Inga, Ravens, Sarina, Tan, Likai, Prinz, Immo, Balin, Simone, Calvi, Michela, Carletti, Anna, Cancellara, Assunta, Coianiz, Nicolò, Franzese, Sara, Frigo, Alessandro, Voza, Antonio, Calcaterra, Francesca, Di Vito, Clara, Della Bella, Silvia, Mikulak, Joanna, and Mavilio, Domenico

Published
2024
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12. The clinical features, treatment and prognosis of neutropenic fever and Coronavirus disease 2019 results of the multicentre teos study

Author

Başkol Elik, Dilşah, Kaya, Şafak, Alkan, Sevil, Demirdal, Tuna, Sener, Alper, Kaya, Selçuk, Güzel Tunçcan, Özlem, Kayaaslan, Bircan, Güner, Rahmet, Eser, Fatma, Kahraman, Hasip, Birengel, Serhat, Sarıcaoğlu, Elif Mukime, Eroğlu, Esma, Çölkesen, Fatma, Öztürk, Erman, Berk Cam, Hande, Mermutluoğlu, Çiğdem, Özer Balin, Şafak, Sincan, Gülden, Altın, Nilgün, Sili, Uluhan, Suntur, Bedia Mutay, Arslan Gülen, Tuğba, Deveci, Burak, Saba, Rabin, İncecik, Şaban, Eser Karlıdağ, Gülden, Hakko, Elif, Akdağ, Damla, Erdem, Hüseyin Aytaç, Sipahi, Hilal, Çicek, Candan, Taşbakan, Mehmet Sezai, Taşbakan, Meltem, Pullukçu, Hüsnü, Yamazhan, Tansu, Arda, Bilgin, Ulusoy, Sercan, and Sipahi, Oguz Resat

Published
2024
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13. Are there associations between clinical and embryological factors with pregnancy loss following transfer of a single euploid embryo?

Author

Beril Yuksel, Gonul Ozer, Ipek Nur Balin Duzguner, Aysu Akca, Yesim Kumtepe, Hakan Yelke, Semra Kahraman, George Liperis, and Munevver Serdarogullari

Subjects
euploid embryo transfer, recurrent pregnancy loss, morphokinetic parameters, time-lapse imaging, preimplantation genetic testing, frozen embryo transfer, Reproduction, QH471-489, Gynecology and obstetrics, RG1-991
Abstract

First-trimester pregnancy losses are commonly attributed to chromosomal abnormalities. The causes of pregnancy loss following transfer of a euploid embryo are not fully elucidated. The aim of this study was to evaluate clinical and embryological parameters for pregnancy failure following the transfer of a single euploid embryo. Pregnancy outcomes of single euploid embryo transfers from a single centre between January 2017 and March 2020 were retrospectively evaluated. Several clinical and embryological parameters were evaluated in consideration to pregnancy outcomes; total pregnancy loss and live birth (LB). Endometrial preparation type, number of previous frozen embryo transfer cycles, history of recurrent pregnancy loss, higher body mass index, presence of endometriosis and/ or adenomyosis and embryo quality were found to be significantly different between two groups. Morphokinetic parameter analysis of 523 euploid embryos using time-lapse imaging did not show any statistical differences between the two groups; however, a significantly higher rate of uneven blastomeres in the cleavage stage was observed in the total pregnancy loss group. Evaluation of clinical and embryological data can reveal possible factors associated with pregnancy loss that can facilitate improved patient consultation. Feasible interventions can potentially increase the chance of achieving an LB.

Published
2024
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14. Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs

Author

Balın, Muhammed Fatih and Çatalyürek, Ümit V.

Subjects
Computer Science - Machine Learning
Abstract

Graph Neural Networks (GNNs) have received significant attention recently, but training them at a large scale remains a challenge. Mini-batch training coupled with sampling is used to alleviate this challenge. However, existing approaches either suffer from the neighborhood explosion phenomenon or have poor performance. To address these issues, we propose a new sampling algorithm called LAyer-neighBOR sampling (LABOR). It is designed to be a direct replacement for Neighbor Sampling (NS) with the same fanout hyperparameter while sampling up to 7 times fewer vertices, without sacrificing quality. By design, the variance of the estimator of each vertex matches NS from the point of view of a single vertex. Moreover, under the same vertex sampling budget constraints, LABOR converges faster than existing layer sampling approaches and can use up to 112 times larger batch sizes compared to NS.

Published
2022

19. Expansion of memory Vδ2 T cells following SARS-CoV-2 vaccination revealed by temporal single-cell transcriptomics

Author

Sara Terzoli, Paolo Marzano, Valentina Cazzetta, Rocco Piazza, Inga Sandrock, Sarina Ravens, Likai Tan, Immo Prinz, Simone Balin, Michela Calvi, Anna Carletti, Assunta Cancellara, Nicolò Coianiz, Sara Franzese, Alessandro Frigo, Antonio Voza, Francesca Calcaterra, Clara Di Vito, Silvia Della Bella, Joanna Mikulak, and Domenico Mavilio

Subjects
Immunologic diseases. Allergy, RC581-607, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract γδ T cells provide rapid cellular immunity against pathogens. Here, we conducted matched single-cell RNA-sequencing and γδ-TCR-sequencing to delineate the molecular changes in γδ T cells during a longitudinal study following mRNA SARS-CoV-2 vaccination. While the first dose of vaccine primes Vδ2 T cells, it is the second administration that significantly boosts their immune response. Specifically, the second vaccination uncovers memory features of Vδ2 T cells, shaped by the induction of AP-1 family transcription factors and characterized by a convergent central memory signature, clonal expansion, and an enhanced effector potential. This temporally distinct effector response of Vδ2 T cells was also confirmed in vitro upon stimulation with SARS-CoV-2 spike-peptides. Indeed, the second challenge triggers a significantly higher production of IFNγ by Vδ2 T cells. Collectively, our findings suggest that mRNA SARS-CoV-2 vaccination might benefit from the establishment of long-lasting central memory Vδ2 T cells to confer protection against SARS-CoV-2 infection.

Published
2024
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20. The clinical features, treatment and prognosis of neutropenic fever and Coronavirus disease 2019 results of the multicentre teos study

Author

Dilşah Başkol Elik, Şafak Kaya, Sevil Alkan, Tuna Demirdal, Alper Sener, Selçuk Kaya, Özlem Güzel Tunçcan, Bircan Kayaaslan, Rahmet Güner, Fatma Eser, Hasip Kahraman, Serhat Birengel, Elif Mukime Sarıcaoğlu, Esma Eroğlu, Fatma Çölkesen, Erman Öztürk, Hande Berk Cam, Çiğdem Mermutluoğlu, Şafak Özer Balin, Gülden Sincan, Nilgün Altın, Uluhan Sili, Bedia Mutay Suntur, Tuğba Arslan Gülen, Burak Deveci, Rabin Saba, Şaban İncecik, Gülden Eser Karlıdağ, Elif Hakko, Damla Akdağ, Hüseyin Aytaç Erdem, Hilal Sipahi, Candan Çicek, Mehmet Sezai Taşbakan, Meltem Taşbakan, Hüsnü Pullukçu, Tansu Yamazhan, Bilgin Arda, Sercan Ulusoy, and Oguz Resat Sipahi

Subjects
Medicine, Science
Abstract

Abstract This multicentre (22 centres in Turkey) retrospective cohort study aimed to assess the clinical outcomes of patients with neutropenic fever and SARS-CoV-2 positivity. Study period was 15 March 2020–15 August 2021. A total of 170 cases (58 female, aged 59 ± 15.5 years) that fulfilled the inclusion criteria were included in the study. One-month mortality rate (OMM) was 44.8%. The logistic regression analysis showed the following significant variables for the mentioned dependent variables: (i) achieving PCR negativity: receiving a maximum of 5 days of favipiravir (p = 0.005, OR 5.166, 95% CI 1.639–16.280); (ii) need for ICU: receiving glycopeptide therapy at any time during the COVID-19/FEN episode (p = 0.001, OR 6.566, 95% CI 2.137–20.172), the need for mechanical ventilation (p

Published
2024
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21. Effect of Short Heat Treatment of Amorphous Metal Alloy on Decolorization Dye Mendola Blue

Author

Khrystyna Khrushchyk, Katarzyna Balin, Vasyl Kordan, Sylwia Golba, Malgorzata Karolus, and Lidiya Boichyshyn

Subjects
amorphous metal alloy (ama), decolorozation, uv-visible spectrophotometry, basic blue 6/ mendola blue, wastewater treatment, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350
Abstract

It was established that the change in the structure of the amorphous metal alloy (AMA) as a result of heat treatment of the amorphous metal alloy Al87Gd5Ni4Fe4 at T=645 ±1 K significantly affects the decolorization of the dye Basic Blue 6/Mendola Blue (BB6/MB) at pH=1.7±0.3. The MB solution with a concentration of 4.39 μM was decolorized by 99 % within 27 hours, in the case of annealed AMAs for 30, 45, 60 min., which is 3.8 times more effective than in the case of as-cast AMAs. A change in the AMA structure due to annealing was established by X-ray analisys; kinetic analysis was performed based on experimental data and the kinetic parameters (kobs, t1/2, ksa, etc.) of the MB decolorization reaction were calculated. The surface of AMAs after reaction with aniline dye MB and the proposed scheme of decolorization of aniline dye MB using AMAs Al87Gd5Ni4Fe4 were suggested.

Published
2024
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23. Identifying risk factors for blood culture negative infective endocarditis: An international ID-IRI study

Author

Mine Filiz, Hakan Erdem, Handan Ankarali, Edmond Puca, Yvon Ruch, Lurdes Santos, Teresa Fasciana, Anna M. Giammanco, Nesrin Ghanem-Zoubi, Xavier Argemi, Yves Hansmann, Rahmet Guner, Gilda Tonziello, Jean-Philippe Mazzucotelli, Najada Como, Sukran Kose, Ayse Batirel, Asuman Inan, Necla Tulek, Abdullah Umut Pekok, Ejaz Ahmed Khan, Atilla Iyisoy, Meliha Meric-Koc, Ayse Kaya-Kalem, Pedro Palma Martins, Imran Hasanoglu, André Silva-Pinto, Nefise Oztoprak, Raquel Duro, Fahad Almajid, Mustafa Dogan, Nicolas Dauby, Jesper Damsgaard Gunst, Recep Tekin, Deborah Konopnicki, Nicola Petrosillo, Ilkay Bozkurt, Jamal Wadi Al Ramahi, Corneliu Popescu, Ilker Inanc Balkan, Safak Ozer-Balin, Tatjana Lejko Zupanc, Antonio Cascio, Irina Magdalena Dumitru, Aysegul Erdem, Gulden Ersoz, Meltem Tasbakan, Oday Abu Ajamieh, Fatma Sirmatel, Simin Florescu, Serda Gulsun, Hacer Deniz Ozkaya, Sema Sari, Selma Tosun, Meltem Avci, Yasemin Cag, Guven Celebi, Ayse Sagmak-Tartar, Sumeyra Karakus, Alper Sener, Arjeta Dedej, Serkan Oncu, Rosa Fontana Del Vecchio, Derya Ozturk-Engin, and Canan Agalar

Subjects
Infective endocarditis, Blood culture negative endocarditis, Rheumatic heart disease, Prosthetic valves, Cardiac disorders, Infectious and parasitic diseases, RC109-216
Abstract

Background: Blood culture-negative endocarditis (BCNE) is a diagnostic challenge, therefore our objective was to pinpoint high-risk cohorts for BCNE. Methods: The study included adult patients with definite endocarditis. Data were collected via the Infectious Diseases International Research Initiative (ID-IRI). The study analysing one of the largest case series ever reported was conducted across 41 centers in 13 countries. We analysed the database to determine the predictors of BCNE using univariate and logistic regression analyses. Results: Blood cultures were negative in 101 (11.65 %) of 867 patients. We disclosed that as patients age, the likelihood of a negative blood culture significantly decreases (OR 0.975, 95 % CI 0.963–0.987, p

Published
2024
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24. Failure of human rhombic lip differentiation underlies medulloblastoma formation

Author

Hendrikse, Liam D, Haldipur, Parthiv, Saulnier, Olivier, Millman, Jake, Sjoboen, Alexandria H, Erickson, Anders W, Ong, Winnie, Gordon, Victor, Coudière-Morrison, Ludivine, Mercier, Audrey L, Shokouhian, Mohammad, Suárez, Raúl A, Ly, Michelle, Borlase, Stephanie, Scott, David S, Vladoiu, Maria C, Farooq, Hamza, Sirbu, Olga, Nakashima, Takuma, Nambu, Shohei, Funakoshi, Yusuke, Bahcheli, Alec, Diaz-Mejia, J Javier, Golser, Joseph, Bach, Kathleen, Phuong-Bao, Tram, Skowron, Patryk, Wang, Evan Y, Kumar, Sachin A, Balin, Polina, Visvanathan, Abhirami, Lee, John JY, Ayoub, Ramy, Chen, Xin, Chen, Xiaodi, Mungall, Karen L, Luu, Betty, Bérubé, Pierre, Wang, Yu C, Pfister, Stefan M, Kim, Seung-Ki, Delattre, Olivier, Bourdeaut, Franck, Doz, François, Masliah-Planchon, Julien, Grajkowska, Wieslawa A, Loukides, James, Dirks, Peter, Fèvre-Montange, Michelle, Jouvet, Anne, French, Pim J, Kros, Johan M, Zitterbart, Karel, Bailey, Swneke D, Eberhart, Charles G, Rao, Amulya AN, Giannini, Caterina, Olson, James M, Garami, Miklós, Hauser, Peter, Phillips, Joanna J, Ra, Young S, de Torres, Carmen, Mora, Jaume, Li, Kay KW, Ng, Ho-Keung, Poon, Wai S, Pollack, Ian F, López-Aguilar, Enrique, Gillespie, G Yancey, Van Meter, Timothy E, Shofuda, Tomoko, Vibhakar, Rajeev, Thompson, Reid C, Cooper, Michael K, Rubin, Joshua B, Kumabe, Toshihiro, Jung, Shin, Lach, Boleslaw, Iolascon, Achille, Ferrucci, Veronica, de Antonellis, Pasqualino, Zollo, Massimo, Cinalli, Giuseppe, Robinson, Shenandoah, Stearns, Duncan S, Van Meir, Erwin G, Porrati, Paola, Finocchiaro, Gaetano, Massimino, Maura, Carlotti, Carlos G, Faria, Claudia C, Roussel, Martine F, Boop, Frederick, Chan, Jennifer A, Aldinger, Kimberly A, Razavi, Ferechte, Silvestri, Evelina, McLendon, Roger E, and Thompson, Eric M

Subjects
Stem Cell Research, Brain Disorders, Neurosciences, Rare Diseases, Stem Cell Research - Nonembryonic - Non-Human, Aetiology, 1.1 Normal biological development and functioning, Underpinning research, 2.1 Biological and endogenous factors, Cell Differentiation, Cell Lineage, Cerebellar Neoplasms, Cerebellum, Core Binding Factor alpha Subunits, Hedgehog Proteins, Histone Demethylases, Humans, Ki-67 Antigen, Medulloblastoma, Metencephalon, Muscle Proteins, Mutation, Otx Transcription Factors, Repressor Proteins, T-Box Domain Proteins, Transcription Factors, General Science & Technology
Abstract

Medulloblastoma (MB) comprises a group of heterogeneous paediatric embryonal neoplasms of the hindbrain with strong links to early development of the hindbrain1-4. Mutations that activate Sonic hedgehog signalling lead to Sonic hedgehog MB in the upper rhombic lip (RL) granule cell lineage5-8. By contrast, mutations that activate WNT signalling lead to WNT MB in the lower RL9,10. However, little is known about the more commonly occurring group 4 (G4) MB, which is thought to arise in the unipolar brush cell lineage3,4. Here we demonstrate that somatic mutations that cause G4 MB converge on the core binding factor alpha (CBFA) complex and mutually exclusive alterations that affect CBFA2T2, CBFA2T3, PRDM6, UTX and OTX2. CBFA2T2 is expressed early in the progenitor cells of the cerebellar RL subventricular zone in Homo sapiens, and G4 MB transcriptionally resembles these progenitors but are stalled in developmental time. Knockdown of OTX2 in model systems relieves this differentiation blockade, which allows MB cells to spontaneously proceed along normal developmental differentiation trajectories. The specific nature of the split human RL, which is destined to generate most of the neurons in the human brain, and its high level of susceptible EOMES+KI67+ unipolar brush cell progenitor cells probably predisposes our species to the development of G4 MB.

Published
2022

27. MG-GCN: Scalable Multi-GPU GCN Training Framework

Author

Balın, Muhammed Fatih, Sancak, Kaan, and Çatalyürek, Ümit V.

Subjects
Computer Science - Machine Learning
Abstract

Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single GPU for large graphs containing tens of millions of vertices or more. Recent work has shown that, for the graphs used in the machine learning community, communication becomes a bottleneck and scaling is blocked outside of the single machine regime. Thus, we propose MG-GCN, a multi-GPU GCN training framework taking advantage of the high-speed communication links between the GPUs present in multi-GPU systems. MG-GCN employs multiple High-Performance Computing optimizations, including efficient re-use of memory buffers to reduce the memory footprint of training GNN models, as well as communication and computation overlap. These optimizations enable execution on larger datasets, that generally do not fit into memory of a single GPU in state-of-the-art implementations. Furthermore, they contribute to achieve superior speedup compared to the state-of-the-art. For example, MG-GCN achieves super-linear speedup with respect to DGL, on the Reddit graph on both DGX-1 (V100) and DGX-A100., Comment: 12 pages, 13 figures, Under Review

Published
2021

28. Autoimmunity in thymic epithelial tumors: a not yet clarified pathologic paradigm associated with several unmet clinical needs

Author

Matteo Perrino, Emanuele Voulaz, Simone Balin, Gerardo Cazzato, Elena Fontana, Sara Franzese, Martina Defendi, Fabio De Vincenzo, Nadia Cordua, Roberto Tamma, Federica Borea, Marta Aliprandi, Marco Airoldi, Luigi Giovanni Cecchi, Roberta Fazio, Marco Alloisio, Giuseppe Marulli, Armando Santoro, Luca Di Tommaso, Giuseppe Ingravallo, Laura Russo, Giorgio Da Rin, Anna Villa, Silvia Della Bella, Paolo Andrea Zucali, and Domenico Mavilio

Subjects
thymic epithelial tumors, autoimmunity, myasthenia gravis, immunotherapy, surgery, thymopoiesis, Immunologic diseases. Allergy, RC581-607
Abstract

Thymic epithelial tumors (TETs) are rare mediastinal cancers originating from the thymus, classified in two main histotypes: thymoma and thymic carcinoma (TC). TETs affect a primary lymphoid organ playing a critical role in keeping T-cell homeostasis and ensuring an adequate immunological tolerance against “self”. In particular, thymomas and not TC are frequently associated with autoimmune diseases (ADs), with Myasthenia Gravis being the most common AD present in 30% of patients with thymoma. This comorbidity, in addition to negatively affecting the quality and duration of patients’ life, reduces the spectrum of the available therapeutic options. Indeed, the presence of autoimmunity represents an exclusion criteria for the administration of the newest immunotherapeutic treatments with checkpoint inhibitors. The pathophysiological correlation between TETs and autoimmunity remains a mystery. Several studies have demonstrated the presence of a residual and active thymopoiesis in adult patients affected by thymomas, especially in mixed and lymphocytic-rich thymomas, currently known as type AB and B thymomas. The aim of this review is to provide the state of art in regard to the histological features of the different TET histotype, to the role of the different immune cells infiltrating tumor microenvironments and their impact in the break of central immunologic thymic tolerance in thymomas. We discuss here both cellular and molecular immunologic mechanisms inducing the onset of autoimmunity in TETs, limiting the portfolio of therapeutic strategies against TETs and greatly impacting the prognosis of associated autoimmune diseases.

Published
2024
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29. Integration of Immersive Approaches for Collaborative Processes with Building Information Modeling (BIM) Methodology for the AEC Industry: An Analysis of the Current State and Future Challenges

Author

Simone Balin, Cecilia M. Bolognesi, and Paolo Borin

Subjects
AEC industry, augmented reality, Building Information Modeling (BIM), collaborative process, virtual world, Immersive Technology (ImT), Electronic computers. Computer science, QA75.5-76.95
Abstract

This study aims to identify and analyze existing gaps in the integration of immersive approaches for collaborative processes with Building Information Modeling (BIM) in the Architecture, Engineering, and Construction (AEC) sector. Using a systematic approach that includes metadata analysis and review procedures, we have formulated specific research questions aimed at guiding future investigations into these gaps. Additionally, the analysis generates insights that could guide future research directions and improvements in the field. The methodology involves a comprehensive review of the literature, focusing on the interaction between immersiveness, BIM methodology, and collaborative processes. Data from 2010 to 2023 have been analyzed to ensure relevance and completeness. Our findings reveal current limitations in the field, such as the need for fully integrated prototypes and the execution of empirical studies to clarify operational processes. These limitations serve as the basis for our research questions. The study offers actionable insights that could guide future research and improvements in the AEC sector, particularly in the adoption of immersive technologies. The research underscores the urgency of addressing these challenges to facilitate ongoing development and greater adoption of immersive technologies in the AEC sector.

Published
2023
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30. Implementations of Artificial Intelligence in Various Domains of IT Governance: A Systematic Literature Review

Author

Eva Hariyanti, Made Balin Janeswari, Malvin Mikhael Moningka, Fikri Maulana Aziz, Annisa Rahma Putri, Oxy Setyo Hapsari, Nyoman Agus Arya Dwija Sutha, Yohannes Alexander Agusti Sinaga, and Manik Prasanthi Bendesa

Subjects
Management information systems, T58.6-58.62
Abstract

Background: Artificial intelligence (AI) has become increasingly prevalent in various industries, including IT governance. By integrating AI into the governance environment, organizations can benefit from the consolidation of frameworks and best practices. However, the adoption of AI across different stages of the governance process is unevenly distributed. Objective: The primary objective of this study is to perform a systematic literature review on applying artificial intelligence (AI) in IT governance processes, explicitly focusing on the Deming cycle. This study overlooks the specific details of the AI methods used in the various stages of IT governance processes. Methods: The search approach acquires relevant papers from Elsevier, Emerald, Google Scholar, Springer, and IEEE Xplore. The obtained results were then filtered using predefined inclusion and exclusion criteria to ensure the selection of relevant studies. Results: The search yielded 359 papers. Following our inclusion and exclusion criteria, we pinpointed 42 primary studies that discuss how AI is implemented in every domain of IT Governance related to the Deming cycle. Conclusion: We found that AI implementation is more dominant in the plan, do, and check stages of the Deming cycle, with a particular emphasis on domains such as risk management, strategy alignment, and performance measurement since most AI applications are not able to perform well in different contexts as well as the other usage driven by its unique capabilities. Keywords: Artificial Intelligence, Deming cycle, Governance, IT Governance domain, Systematic literature review

Published
2023
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31. Fetuin A and fetuin B as an indicator of liver fibrosis in hepatitis B

Author

Şenol Arzu, Özer Balin Şafak, and Aşçı Toraman Zülal

Subjects
hepatitis b, fibrosis, fetuin a, fetuin b, elisa, Biochemistry, QD415-436
Abstract

The aim of this study is to investigate the diagnostic and prognostic characteristics of fetuin A and fetuin B in chronic hepatitis B (CHB) and HBe Ag-negative chronic infection (HCI) and the relationship between the levels of these proteins and fibrosis in CHB.

Published
2023
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32. Clinicopathological profile of peritoneal tuberculosis and a new scoring model for predicting mortality: an international ID-IRI study

Author

Tanoglu, Alpaslan, Erdem, Hakan, Friedland, Jon S., Ankaralı, Handan, Garcia-Goez, Jose Fernando, Albayrak, Ayse, El-Kholy, Amani, Ceviker, Sevil Alkan, Amer, Fatma, Erol, Serpil, Darazam, Ilad Alavi, Rabiei, Mohammad Mahdi, Sarwar, Muhammad Zeeshan, Zeb, Misbah, Nawaz, Hassan, Ceylan, Mehmet Resat, Cernat, Roxana, Tasbakan, Meltem, Ayoade, Folusakin, Ruch, Yvon, Tigen, Elif Tükenmez, Angioni, Goffredo, Rajani, Dhanji P., Akhtar, Nasim, Surme, Serkan, Sengoz, Gonul, Karlıdag, Gulden Eser, Marino, Andrea, Ripon, Rezaul Karim, Çağ, Yasemin, Aydın, Özlem, Akkoyunlu, Yasemin, Seyman, Derya, Angamuthu, Kumar, Cascio, Antonio, Popescu, Corneliu Petru, Sirmatel, Fatma, Eren, Esma, Dar, Razi Even, Munu, Foday Usman, Tanoglu, Esra Guzel, Echeverry, Esteban, Velez, Juan Diego, Artuk, Cumhur, Balin, Safak Ozer, Pandya, Nirav, Erdem, Aysegul, Demiray, Emine Kübra Dindar, and Aypak, Adalet

Published
2023
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33. Electrochemical Behavior of Plasma-Nitrided Austenitic Stainless Steel in Chloride Solutions

Author

Viera Zatkalíková, Petra Drímalová, Katarzyna Balin, Martin Slezák, and Lenka Markovičová

Subjects
austenitic stainless steel, plasma nitriding, corrosion resistance, potentiodynamic polarization, electrochemical impedance spectroscopy, Technology, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Engineering (General). Civil engineering (General), TA1-2040, Microscopy, QH201-278.5, Descriptive and experimental mechanics, QC120-168.85
Abstract

The application possibilities of austenitic stainless steels in high friction, abrasion, and sliding wear conditions are limited by their inadequate hardness and tribological characteristics. In order to improve these properties, the thermochemical treatment of their surface by plasma nitriding is suitable. This article is focused on the corrosion resistance of conventionally plasma-nitrided AISI 304 stainless steel (530 °C, 24 h) in 0.05 M and 0.5 M sodium chloride solutions at room temperature (20 ± 3 °C), tested by potentiodynamic polarization and electrochemical impedance spectroscopy. Optical microscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy are used for nitrided layer characterization. The experiment results confirmed the plasma-nitrided layer formation of increased micro-hardness related to the presence of Cr2N chromium nitrides and higher surface roughness compared to the as-received state. Both of the performed independent electrochemical corrosion tests point to a significant reduction in corrosion resistance after the performed plasma nitriding, even in a solution with a very low chloride concentration (0.05 mol/L).

Published
2024
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34. Direct Numerical Simulation of a Turbulent Boundary Layer with Strong Pressure Gradients

Author

Balin, Riccardo and Jansen, Kenneth E.

Subjects
Physics - Fluid Dynamics
Abstract

The turbulent boundary layer over a Gaussian shaped bump is computed by direct numerical simulation (DNS) of the incompressible Navier-Stokes equations. The two-dimensional bump causes a series of strong pressure gradients alternating in rapid succession. At the inflow, the momentum thickness Reynolds number is approximately 1,000 and the boundary layer thickness is 1/8 of the bump height. DNS results show that the strong favorable pressure gradient (FPG) causes the boundary layer to enter a relaminarization process. The near-wall turbulence is significantly weakened and becomes intermittent, however relaminarization does not complete. The streamwise velocity profiles deviate above the standard logarithmic law and the Reynolds shear stress is reduced. The strong acceleration also suppresses the wall-shear normalized turbulent kinetic energy production rate. At the bump peak, where the FPG switches to an adverse gradient (APG), the near-wall turbulence is suddenly enhanced through a partial retransition process. This results in a new highly energized internal layer which is more resilient to the strong APG and only produces incipient flow separation on the downstream side. In the strong FPG and APG regions, the inner and outer layers become largely independent of each other. The near-wall region responds to the pressure gradients and determines the skin friction. The outer layer behaves similarly to a free-shear layer subject to pressure gradients and mean streamline curvature effects. Results from a RANS simulation of the bump are also discussed and clearly show the lack of predictive capacity of the near-wall pressure gradient effects on the mean flow.

Published
2020
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35. Direct Numerical Simulation of a Turbulent Boundary Layer on a Flat Plate Using Synthetic Turbulence Generation

Author

Wright, James R., Balin, Riccardo, Patterson, John W., Evans, John A., and Jansen, Kenneth E.

Subjects
Physics - Fluid Dynamics
Abstract

The turbulent boundary layer over a flat plate is computed by direct numerical simulation (DNS) of the incompressible Navier-Stokes equations as a test bed for a synthetic turbulence generator (STG) inflow boundary condition. The inlet momentum thickness Reynolds number is approximately 1,000. The study provides validation of the ability of the STG to develop accurate turbulence in 5 to 7 boundary layer thicknesses downstream of the boundary condition. Also tested was the effect of changes in the stabilization scheme on the development of the boundary layer. Moreover, the grid resolution required for both the development region and the downstream flow is investigated when using a stabilized finite element method.

Published
2020

36. On Symmetric Rectilinear Matrix Partitioning

Author

Yaşar, Abdurrahman, Balin, Muhammed Fatih, An, Xiaojing, Sancak, Kaan, and Çatalyürek, Ümit V.

Subjects
Computer Science - Data Structures and Algorithms
Abstract

Even distribution of irregular workload to processing units is crucial for efficient parallelization in many applications. In this work, we are concerned with a spatial partitioning called rectilinear partitioning (also known as generalized block distribution) of sparse matrices. More specifically, in this work, we address the problem of symmetric rectilinear partitioning of a square matrix. By symmetric, we mean the rows and columns of the matrix are identically partitioned yielding a tiling where the diagonal tiles (blocks) will be squares. We first show that the optimal solution to this problem is NP-hard, and we propose four heuristics to solve two different variants of this problem. We present a thorough analysis of the computational complexities of those proposed heuristics. To make the proposed techniques more applicable in real life application scenarios, we further reduce their computational complexities by utilizing effective sparsification strategies together with an efficient sparse prefix-sum data structure. We experimentally show the proposed algorithms are efficient and effective on more than six hundred test matrices. With sparsification, our methods take less than 3 seconds in the Twitter graph on a modern 24 core system and output a solution whose load imbalance is no worse than 1%.

Published
2020

37. S-Frame Discrepancy Correction Models for Data-Informed Reynolds Stress Closure

Author

Peters, Eric L., Balin, Riccardo, Jansen, Kenneth E., Doostan, Alireza, and Evans, John A.

Subjects
Physics - Fluid Dynamics, Physics - Computational Physics
Abstract

Despite their well-known limitations, RANS models remain the most commonly employed tool for modeling turbulent flows in engineering practice. RANS models are predicated on the solution of the RANS equations, but these equations involve an unclosed term, the Reynolds stress tensor, which must be modeled. The Reynolds stress tensor is often modeled as an algebraic function of mean flow field variables and turbulence variables. This introduces a discrepancy between the Reynolds stress tensor predicted by the model and the exact Reynolds stress tensor. This discrepancy can result in inaccurate mean flow field predictions. In this paper, we introduce a data-informed approach for arriving at Reynolds stress models with improved predictive performance. Our approach relies on learning the components of the Reynolds stress discrepancy tensor associated with a given Reynolds stress model in the mean strain-rate tensor eigenframe. These components are typically smooth and hence simple to learn using state-of-the-art machine learning strategies and regression techniques. Our approach automatically yields Reynolds stress models that are symmetric, and it yields Reynolds stress models that are both Galilean and frame invariant provided the inputs are themselves Galilean and frame invariant. To arrive at computable models of the discrepancy tensor, we employ feed-forward neural networks and an input space spanning the integrity basis of the mean strain-rate tensor, the mean rotation-rate tensor, the mean pressure gradient, and the turbulent kinetic energy gradient, and we introduce a framework for dimensional reduction of the input space to further reduce computational cost. Numerical results illustrate the effectiveness of the proposed approach for data-informed Reynolds stress closure for a suite of turbulent flow problems of increasing complexity.

Published
2020

38. Comparison of Immunological and Virological Recovery with Rapid, Early, and Late Start of Antiretroviral Treatment in Naive Plwh: Real-World Data

Author

Sarıgül Yıldırım F, Candevir A, Akhan S, Kaya S, Çabalak M, Ersöz G, İnan D, Ceren N, Karaoğlan İ, Damar Çakırca T, Özer Balin Ş, Alkan S, Kandemir Ö, Üser Ü, Karabay O, and Çelen MK

Subjects
hiv, art, antiretroviral, rapid start, Medicine (General), R5-920
Abstract

Figen Sarıgül Yıldırım,1 Aslıhan Candevir,2 Sıla Akhan,3 Selçuk Kaya,4 Mehmet Çabalak,5 Gülden Ersöz,6 Dilara İnan,7 Nurgül Ceren,8 İlkay Karaoğlan,9 Tuba Damar Çakırca,10 Şafak Özer Balin,11 Sevil Alkan,12 Özlem Kandemir,6 Ülkü Üser,13 Oğuz Karabay,14 Mustafa Kemal Çelen15 1Antalya Life Hospital, Department of Infectious Diseases and Clinical Microbiology, Antalya, Turkey; 2Cukurova University, Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Adana, Turkey; 3Kocaeli Üniversity, Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Kocaeli, Turkey; 4Karadeniz Teknik University, Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Trabzon, Turkey; 5Mustafa Kemal University Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Hatay, Turkey; 6Mersin University, Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Mersin, Turkey; 7Akdeniz University Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Antalya, Turkey; 8Health Science University, Haydarpaşa Numune Education and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, İstanbul, Turkey; 9Gaziantep University, Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Gaziantep, Turkey; 10Health Science University, Şanlıurfa Numune Education and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Şanlıurfa, Turkey; 11Fırat University Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Elazığ, Turkey; 12Çanakkale 18 Mart University, Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Çanakkale, Turkey; 13Health Science University, Antalya Education and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Antalya, Turkey; 14Sakarya University Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Hatay, Turkey; 15Dicle University, Medical Faculty, Department of Infectious Diseases and Clinical Microbiology, Diyarbakır, TurkeyCorrespondence: Figen Sarıgül Yıldırım, Antalya Life Hospital, Department of Infectious Diseases and Clinical Microbiology, Antalya, Turkey, Tel +90 532 473 44 46, Email drfigensarigul@yahoo.com.trBackground: Rapid initiation of antiretroviral therapy (ART) reduces the transmission of HIV infection in the community. This study aimed to determine whether rapid ART initiation is effective compared to standard ART treatment in our country.Methods: Patients were grouped based on time to treatment initiation. HIV RNA levels, CD+4 T cell count, CD4/CD8 ratio, and ART regimens were recorded at baseline and follow-up visits for 12 months.Results: There were 368-ART naive adults (treatment initiated at the time of HIV diagnosis; 143 on the first day, 48 on the second-seventh day, and 177 after the seventh day). Although virological suppression rates at 12th months were higher in all groups, over 90% on average, there were no statistically significant differences in HIV-1 RNA suppression rates, CD+4 T cell count, and CD4/CD8 ratio normalization in the studied months but in multivariate logistic regression analysis; showed a significant correlation between both virological and immunological response and those with CD4+ T < 350 cells/mL at 12th month in total patients.Conclusion: Our findings support the broader application of recommendations for rapid ART initiation in HIV patients.Keywords: HIV, ART, antiretroviral, rapid start

Published
2023

39. Identification and Biological Evaluation of a Water-Soluble Fullerene Nanomaterial as BTK Kinase Inhibitor

Author

Malarz K, Korzuch J, Marforio TD, Balin K, Calvaresi M, Mrozek-Wilczkiewicz A, Musiol R, and Serda M

Subjects
fullerenes, btk inhibitor, anticancer agent, autophagy, apoptosis, Medicine (General), R5-920
Abstract

Katarzyna Malarz,1,&ast; Julia Korzuch,2,&ast; Tainah Dorina Marforio,3 Katarzyna Balin,1 Matteo Calvaresi,3 Anna Mrozek-Wilczkiewicz,1 Robert Musiol,2 Maciej Serda2 1A. Che&lstrok;kowski Institute of Physics, University of Silesia in Katowice, Chorzów, Poland; 2Institute of Chemistry, University of Silesia in Katowice, Katowice, Poland; 3Department of Chemistry “Giacomo Ciamician”, University of Bologna, Bologna, Italy&ast;These authors contributed equally to this workCorrespondence: Maciej Serda; Katarzyna Malarz, Email maciej.serda@us.edu.pl; katarzyna.malarz@us.edu.plIntroduction: Thanks to recent advances in synthetic methodology, water-soluble fullerene nanomaterials that interfere with biomolecules, especially DNA/RNA and selected proteins, have been found with tremendous potential for applications in nanomedicine. Herein, we describe the synthesis and evaluation of a water-soluble glycine-derived [60]fullerene hexakisadduct (HDGF) with Th symmetry, which is a first-in-class BTK protein inhibitor.Methods: We synthesized and characterized glycine derived [60]fullerene using NMR, ESI-MS, and ATR-FT-IR. DLS and zeta potential were measured and high-resolution transmission electron microscopy (HRTEM) observations were performed. The chemical composition of the water-soluble fullerene nanomaterial was examined by X-ray photoelectron spectrometry. To observe aggregate formation, the cryo-TEM analysis was carried out. The docking studies and molecular dynamic simulations were performed to determine interactions between HDGF and BTK. The in vitro cytotoxicity was evaluated on RAJI and K562 blood cancer cell lines. Subsequently, we examined the induction of cell death by autophagy and apoptosis by determining the expression levels of crucial genes and caspases. We investigated the direct association of HDGF on inhibition of the BTK signalling pathway by examining changes in the calcium levels in RAJI cells after treatment. The inhibitory potential of HDGF against non-receptor tyrosine kinases was evaluated. Finally, we assessed the effects of HDGF and ibrutinib on the expression of the BTK protein and downstream signal transduction in RAJI cells following anti-IgM stimulation.Results: Computational studies revealed that the inhibitory activity of the obtained [60]fullerene derivative is multifaceted: it hampers the BTK active site, interacting directly with the catalytic residues, rendering it inaccessible to phosphorylation, and binds to residues that form the ATP binding pocket. The anticancer activity of produced carbon nanomaterial revealed that it inhibited the BTK protein and its downstream pathways, including PLC and Akt proteins, at the cellular level. The mechanistic studies suggested the formation of autophagosomes (increased gene expression of LC3 and p62) and two caspases (caspase-3 and − 9) were responsible for the activation and progression of apoptosis.Conclusion: These data illustrate the potential of fullerene-based BTK protein inhibitors as nanotherapeutics for blood cancer and provide helpful information to support the future development of fullerene nanomaterials as a novel class of enzyme inhibitors.Graphical Abstract: Keywords: fullerenes, BTK inhibitor, anticancer agent, autophagy, apoptosis

Published
2023

43. Liraglutide Pretreatment Does Not Improve Acute Doxorubicin-Induced Cardiotoxicity in Rats

Author

Carolina R. Tonon, Marina G. Monte, Paola S. Balin, Anderson S. S. Fujimori, Ana Paula D. Ribeiro, Natália F. Ferreira, Nayane M. Vieira, Ronny P. Cabral, Marina P. Okoshi, Katashi Okoshi, Leonardo A. M. Zornoff, Marcos F. Minicucci, Sergio A. R. Paiva, Mariana J. Gomes, and Bertha F. Polegato

Subjects
doxorubicin, heart failure, acute cardiotoxicity, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

Doxorubicin is an effective drug for cancer treatment; however, cardiotoxicity limits its use. Cardiotoxicity pathophysiology is multifactorial. GLP-1 analogues have been shown to reduce oxidative stress and inflammation. In this study, we evaluated the effect of pretreatment with liraglutide on doxorubicin-induced acute cardiotoxicity. A total of 60 male Wistar rats were allocated into four groups: Control (C), Doxorubicin (D), Liraglutide (L), and Doxorubicin + Liraglutide (DL). L and DL received subcutaneous injection of liraglutide 0.6 mg/kg daily, while C and D received saline for 2 weeks. Afterwards, D and DL received a single intraperitoneal injection of doxorubicin 20 mg/kg; C and L received an injection of saline. Forty-eight hours after doxorubicin administration, the rats were subjected to echocardiogram, isolated heart functional study, and euthanasia. Liraglutide-treated rats ingested significantly less food and gained less body weight than animals that did not receive the drug. Rats lost weight after doxorubicin injection. At echocardiogram and isolated heart study, doxorubicin-treated rats had systolic and diastolic function impairment. Myocardial catalase activity was statistically higher in doxorubicin-treated rats. Myocardial protein expression of tumor necrosis factor alpha (TNF-α), phosphorylated nuclear factor-κB (p-NFκB), troponin T, and B-cell lymphoma 2 (Bcl-2) was significantly lower, and the total NFκB/p-NFκB ratio and TLR-4 higher in doxorubicin-treated rats. Myocardial expression of OPA-1, MFN-2, DRP-1, and topoisomerase 2β did not differ between groups (p > 0.05). In conclusion, doxorubicin-induced cardiotoxicity is accompanied by decreased Bcl-2 and phosphorylated NFκB and increased catalase activity and TLR-4 expression. Liraglutide failed to improve acute doxorubicin-induced cardiotoxicity in rats.

Published
2024
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44. Dynamics of individual Brownian rods in a microchannel flow

Author

Zöttl, Andreas, Klop, Kira E., Balin, Andrew K., Gao, Yongxiang, Yeomans, Julia M., and Aarts, Dirk G. A. L.

Subjects
Condensed Matter - Soft Condensed Matter, Physics - Fluid Dynamics
Abstract

We study the orientational dynamics of heavy silica microrods flowing through a microfluidic channel. Comparing experiments and Brownian dynamics simulations we identify different particle orbits, in particular in-plane tumbling behavior, which cannot be explained by classical Jeffery theory, and we relate this behavior to the rotational diffusion of the rods. By constructing the full, three-dimensional, orientation distribution, we describe the rod trajectories and quantify the persistence of Jeffery orbits using temporal correlation functions of the Jeffery constant. We find that our colloidal rods lose memory of their initial configuration in about a second, corresponding to half a Jeffery period., Comment: 5 pages, 4 figures

Published
2019
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45. Concrete Autoencoders for Differentiable Feature Selection and Reconstruction

Author

Abid, Abubakar, Balin, Muhammad Fatih, and Zou, James

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and simultaneously learns a neural network to reconstruct the input data from the selected features. Our method is unsupervised, and is based on using a concrete selector layer as the encoder and using a standard neural network as the decoder. During the training phase, the temperature of the concrete selector layer is gradually decreased, which encourages a user-specified number of discrete features to be learned. During test time, the selected features can be used with the decoder network to reconstruct the remaining input features. We evaluate concrete autoencoders on a variety of datasets, where they significantly outperform state-of-the-art methods for feature selection and data reconstruction. In particular, on a large-scale gene expression dataset, the concrete autoencoder selects a small subset of genes whose expression levels can be use to impute the expression levels of the remaining genes. In doing so, it improves on the current widely-used expert-curated L1000 landmark genes, potentially reducing measurement costs by 20%. The concrete autoencoder can be implemented by adding just a few lines of code to a standard autoencoder., Comment: Submitted to ICML 2019

Published
2019

46. Defining Transcriptional Signatures of Human Hair Follicle Cell States

Author

Takahashi, Rie, Grzenda, Adrienne, Allison, Thomas F, Rawnsley, Jeffrey, Balin, Samuel J, Sabri, Shan, Plath, Kathrin, and Lowry, William E

Subjects
Biomedical and Clinical Sciences, Immunology, Stem Cell Research, Stem Cell Research - Nonembryonic - Non-Human, Stem Cell Research - Nonembryonic - Human, Genetics, Underpinning research, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Aetiology, Skin, Animals, Cell Differentiation, Endothelial Cells, Hair Follicle, Humans, Stem Cells, Clinical Sciences, Oncology and Carcinogenesis, Dermatology & Venereal Diseases, Clinical sciences
Abstract

The epidermis and its appendage, the hair follicle, represent an elegant developmental system in which cells are replenished with regularity because of controlled proliferation, lineage specification, and terminal differentiation. Although transcriptome data exists for human epidermal and dermal cells, the hair follicle remains poorly characterized. Through single-cell resolution profiling of the epidermis and anagen hair follicle, we characterized the anatomical, transcriptional, functional, and pathological profiles of distinct epidermal, hair follicle, and hair follicle-associated cell subpopulations including melanocytes, endothelial cells, and immune cells. We additionally traced the differentiation trajectory of interfollicular and matrix cell progenitors and explored the association of specific cell subpopulations to known molecular signatures of common skin conditions. These data simultaneously corroborate prior murine and human studies while offering new insights into epidermal and hair follicle differentiation and pathogenesis.

Published
2020

47. Theoretical and Applied Aspects of the Categorical and Conceptual Apparatus of Legal Psychology

Author

V.D. Balin and V.A. Ilyina

Subjects
Psychology, BF1-990
Abstract

The article reflects the results of a study of theoretical and applied aspects of the categori-cal and conceptual apparatus of legal psychology from a critical point of view. Conclusions are drawn about the special position of legal psychology as an applied discipline in relation to the legal sciences, in connection with which the priority role in solving its problems is given to legal concepts, which in their essence are self-sufficient and conventional. The conventional nature of legal concepts, built on an agreement between lawyers in the under-standing of certain terms, comes down to the fact that in their content they in some cases do not coincide with the content of general psychological categories and concepts. In terms of scope, they are either narrower or wider than psychological concepts. In this regard, the significant reverse influence of psychological concepts on legal ones, expressed in the re-flection of psychological patterns and mechanisms of psychological and legal phenomena, becomes a laborious and long process, since legal concepts do not aim to reflect the true structure of the world. The features of the categorical-conceptual apparatus of legal psy-chology listed in this study make it possible to more accurately determine the direction of scientific research, correctly understand the needs of legal practice and predict the long-term consequences of the results of one’s scientific activity.

Published
2023
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48. A trend analysis of Sars-Cov-2 anti-nucleocapsid IgG antibodies with a recommendation of the cut-off classification to identify naïve, vaccinated, and infected cases

Author

Serhat Uysal, Kutbeddin Demirdag, Mesut Batur, Safak Ozer Balin, Mehmet Ali Asan, Ayse Sagmak Tartar, and Ayhan Akbulut

Subjects
covid-19, anti-nucleocapsid igg, ınactive vaccine, naïve, sars-cov-2, vaccination, serology, Medicine
Abstract

Determination of the vaccination's effect on immune responses with the emergence of the Sars-Cov-2 pandemic become an important issue. This study aimed to evaluate the response of the anti-nucleocapsid IgG (anti-N IgG) index in naïve, vaccinated, or infected cases, in addition to suggesting ‘How to distinguish between vaccinated versus infected cases via anti-N IgG?'. Anti-N levels of the naïve [0.03 (0.02–0.06)], vaccinated [0.7 (0.2–1.96)], and infected [3.07 (1.44–5.2)] groups were statistically different (p [Med-Science 2022; 11(4.000): 1534-40]

Published
2022
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