TY - JOUR AU - Eszlári, Nóra AU - Hullám, Gábor István AU - Gál, Zsófia AU - Török, Dóra AU - Nagy, Tamás AU - Millinghoffer, András Dániel AU - Baksa, Dániel AU - Gonda, Xénia AU - Antal, Péter AU - Bagdy, György AU - Juhász, Gabriella TI - Olfactory genes affect major depression in highly educated, emotionally stable, lean women: a bridge between animal models and precision medicine JF - TRANSLATIONAL PSYCHIATRY J2 - TRANSL PSYCHIAT VL - 14 PY - 2024 IS - 1 PG - 10 SN - 2158-3188 DO - 10.1038/s41398-024-02867-2 UR - https://m2.mtmt.hu/api/publication/34779723 ID - 34779723 N1 - Funding Agency and Grant Number: Hungarian National Research, Development, and Innovation Office [K 139330, K 143391, PD 146014, 2019-2.1.7-ERA-NET-2020-00005, ERAPERMED2019-108]; Hungarian Brain Research Program [2017-1.2.1-NKP-2017-00002]; Hungarian Brain Research Program 3.0 [NAP2022-I-4/2022, TKP2021-EGA-25]; Ministry of Innovation and Technology of Hungary National Research, Development and Innovation Fund [TKP2021-EGA-25]; National Research, Development, and Innovation Fund of Hungary [TKP2021-EGA-02]; European Union [RRF-2.3.1-21-2022-00004]; New National Excellence Program of the Ministry for Culture and Innovation National Research, Development and Innovation Fund [UNKP-23-4-II-SE-2]; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences; Semmelweis University; [UNKP-22-4-II-SE-1] Funding text: This study was supported by the Hungarian National Research, Development, and Innovation Office, with grants K 139330, K 143391, and PD 146014, as well as 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); by the Hungarian Brain Research Program (grant: 2017-1.2.1-NKP-2017-00002) and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and by TKP2021-EGA-25, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme. It was also supported by the National Research, Development, and Innovation Fund of Hungary under Grant TKP2021-EGA-02 and the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory. NE was supported by the UNKP-22-4-II-SE-1, and DB by the UNKP-23-4-II-SE-2 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. NE is supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences. This work uses data provided by patients and collected by the NHS as part of their care and support. Copyright (c) (2019), NHS England. Re-used with the permission of the UK Biobank (Application Number 1602). All rights reserved.Open access funding provided by Semmelweis University. AB - Most current approaches to establish subgroups of depressed patients for precision medicine aim to rely on biomarkers that require highly specialized assessment. Our present aim was to stratify participants of the UK Biobank cohort based on three readily measurable common independent risk factors, and to investigate depression genomics in each group to discover common and separate biological etiology. Two-step cluster analysis was run separately in males ( n = 149,879) and females ( n = 174,572), with neuroticism (a tendency to experience negative emotions), body fat percentage, and years spent in education as input variables. Genome-wide association analyses were implemented within each of the resulting clusters, for the lifetime occurrence of either a depressive episode or recurrent depressive disorder as the outcome. Variant-based, gene-based, gene set-based, and tissue-specific gene expression test were applied. Phenotypically distinct clusters with high genetic intercorrelations in depression genomics were found. A two-cluster solution was the best model in each sex with some differences including the less important role of neuroticism in males. In females, in case of a protective pattern of low neuroticism, low body fat percentage, and high level of education, depression was associated with pathways related to olfactory function. While also in females but in a risk pattern of high neuroticism, high body fat percentage, and less years spent in education, depression showed association with complement system genes. Our results, on one hand, indicate that alteration of olfactory pathways, that can be paralleled to the well-known rodent depression models of olfactory bulbectomy, might be a novel target towards precision psychiatry in females with less other risk factors for depression. On the other hand, our results in multi-risk females may provide a special case of immunometabolic depression. LA - English DB - MTMT ER - TY - JOUR AU - Bajczi, Levente AU - Ádám, Zsófia AU - Micskei, Zoltán Imre TI - ConcurrentWitness2Test: Test-Harnessing the Power of Concurrency (Competition Contribution) JF - LECTURE NOTES IN COMPUTER SCIENCE J2 - LNCS VL - 14572 PY - 2024 SP - 330 EP - 334 PG - 5 SN - 0302-9743 DO - 10.1007/978-3-031-57256-2_16 UR - https://m2.mtmt.hu/api/publication/34768972 ID - 34768972 AB - ConcurrentWitness2Test is a violation witness validator for concurrent software. Taking both nondeterminism of data and interleaving-based nondeterminism into account, the tool aims to use the metadata described in the violation witnesses to synthesize an executable test harness. While plagued by some initial challenges yet to overcome, the validation performance of ConcurrentWitness2Test corroborates the usefulness of the proposed approach. LA - English DB - MTMT ER - TY - JOUR AU - Bajczi, Levente AU - Telbisz, Csanád Ferenc AU - Somorjai, Márk AU - Ádám, Zsófia AU - Dobos-Kovács, Mihály AU - Szekeres, Dániel AU - Mondok, Milán AU - Molnár, Vince TI - Theta: Abstraction Based Techniques for Verifying Concurrency (Competition Contribution) JF - LECTURE NOTES IN COMPUTER SCIENCE J2 - LNCS VL - 14572 PY - 2024 SP - 412 EP - 417 PG - 6 SN - 0302-9743 DO - 10.1007/978-3-031-57256-2_30 UR - https://m2.mtmt.hu/api/publication/34768428 ID - 34768428 AB - Theta is a model checking framework, with a strong emphasis on effectively handling concurrency in software using abstraction refinement algorithms. In SV-COMP 2024, we use 1) an abstraction-aware partial order reduction; 2) a dynamic statement reduction technique; and 3) enhanced support for call stacks to handle recursive programs. We integrate these techniques in an improved architecture with inherent support for portfolio-based verification using dynamic algorithm selection, with a diverse selection of supported SMT solvers as well. In this paper we detail the advances of Theta regarding concurrent and recursive software support. LA - English DB - MTMT ER - TY - JOUR AU - Bajczi, Levente AU - Szekeres, Dániel AU - Mondok, Milán AU - Ádám, Zsófia AU - Somorjai, Márk AU - Telbisz, Csanád Ferenc AU - Dobos-Kovács, Mihály AU - Molnár, Vince TI - EmergenTheta: Verification Beyond Abstraction Refinement (Competition Contribution) JF - LECTURE NOTES IN COMPUTER SCIENCE J2 - LNCS VL - 14572 PY - 2024 SP - 371 EP - 375 PG - 5 SN - 0302-9743 DO - 10.1007/978-3-031-57256-2_23 UR - https://m2.mtmt.hu/api/publication/34768422 ID - 34768422 AB - Theta is a model checking framework conventionally based on abstraction refinement techniques. While abstraction is useful for a large number of verification problems, the over-reliance on the technique led to Theta being unable to meaningfully adapt. Identifying this problem in previous years of SV-COMP has led us to create EmergenTheta , a sandbox for the new approaches we want Theta to support. By differentiating between mature and emerging techniques, we can experiment more freely without hurting the reliability of the overall framework. In this paper we detail the development route to EmergenTheta , and its first debut on SV-COMP’24 in the ReachSafety category. LA - English DB - MTMT ER - TY - JOUR AU - Pogány, Domonkos AU - Antal, Péter TI - Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space JF - PLOS ONE J2 - PLOS ONE VL - 19 PY - 2024 IS - 3 PG - 23 SN - 1932-6203 DO - 10.1371/journal.pone.0300906 UR - https://m2.mtmt.hu/api/publication/34751205 ID - 34751205 N1 - Funding Agency and Grant Number: Doctoral Excellence Fellowship Programme (DCEP) - National Research Development and Innovation Fund of the Ministry of Culture and Innovation; Budapest University of Technology and Economics [2020-2.1.1-ED-2023-00239]; J. Heim Student Scholarship [OTKA-K139330]; European Union [SOLID JPND2021-650-233, RRF-2.3.1-21-2022-00004]; National Research, Development, and Innovation Fund of Hungary [TKP2021-EGA-02] Funding text: The project supported by the Doctoral Excellence Fellowship Programme (DCEP) is funded by the National Research Development and Innovation Fund of the Ministry of Culture and Innovation and the Budapest University of Technology and Economics, under a grant agreement with the National Research, Development and Innovation Office (2020-2.1.1-ED-2023-00239) (PD). This research was also funded by the J. Heim Student Scholarship (PD), the OTKA-K139330, the European Union (EU) Joint Program on Neurodegenerative Disease (JPND) Grant: (SOLID JPND2021-650-233), the National Research, Development, and Innovation Fund of Hungary under Grant TKP2021-EGA-02, the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. AB - Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainability of machine learning models remains a challenge. Our work aims to provide more interpretable interaction prediction models using similarity-based prediction in a latent space aligned to biological hierarchies. We investigated integrating drug and protein hierarchies into a joint-embedding drug-target latent space via embedding regularization by conducting a comparative analysis between models employing traditional flat Euclidean vector spaces and those utilizing hyperbolic embeddings. Besides, we provided a latent space analysis as an example to show how we can gain visual insights into the trained model with the help of dimensionality reduction. Our results demonstrate that hierarchy regularization improves interpretability without compromising predictive performance. Furthermore, integrating hyperbolic embeddings, coupled with regularization, enhances the quality of the embedded hierarchy trees. Our approach enables a more informed and insightful application of interaction prediction models in drug discovery by constructing an interpretable hyperbolic latent space, simultaneously incorporating drug and target hierarchies and pairing them with available interaction information. Moreover, compatible with pairwise methods, the approach allows for additional transparency through existing explainable AI solutions. LA - English DB - MTMT ER - TY - JOUR AU - Serban, Andrada Alexia AU - Micskei, Zoltán Imre TI - Application of Mutation testing in Safety-Critical Embedded Systems: A Case Study JF - ACTA POLYTECHNICA HUNGARICA J2 - ACTA POLYTECH HUNG VL - 21 PY - 2024 IS - 8 SP - 87 EP - 106 PG - 20 SN - 1785-8860 DO - 10.12700/APH.21.8.2024.8.5 UR - https://m2.mtmt.hu/api/publication/34727894 ID - 34727894 N1 - Export Date: 5 April 2024 LA - English DB - MTMT ER - TY - BOOK AU - Ákos, Ferenc Hegedűs AU - Dabóczi, Tamás TI - Design Optimization of a Current Sensing Trace with respect to Skin Effect by FEM Simulations PY - 2024 SP - 4 UR - https://m2.mtmt.hu/api/publication/34726765 ID - 34726765 LA - English DB - MTMT ER - TY - JOUR AU - Alrwashdeh, Monther AU - Czifra, Balazs AU - Kollár, Zsolt TI - Analysis of Quantization Noise in Fixed-Point HDFT Algorithms JF - IEEE SIGNAL PROCESSING LETTERS J2 - IEEE SIGNAL PROC LET VL - 31 PY - 2024 SP - 756 EP - 760 PG - 5 SN - 1070-9908 DO - 10.1109/LSP.2024.3372782 UR - https://m2.mtmt.hu/api/publication/34720081 ID - 34720081 N1 - Export Date: 22 March 2024 CODEN: ISPLE AB - The Discrete Fourier Transform (DFT) algorithm is widely used in signal processing and communication systems to transform the signal to the frequency-domain. As real-time signal analysis is required for fast processing, several recursive algorithms were proposed to perform the calculation with overlapping sequences in a sliding manner. One Sliding DFT (SDFT) method is the Hopping DFT (HDFT), where the DFT calculations are not evaluated sample-by-sample but with longer steps, thus further reducing the computational complexity compared to the other SDFT algorithms. This letter analyses the effect of fixed-point roundoff error in the HDFT algorithm, including the Updating Vector Transform (UVT) block. A closed-form expression for the resulting quantization noise power at the output of the HDFT algorithm is provided, which is validated through simulations. The results show that the roundoff error can be determined based on the number and size of the hops, the window size, and the number of fractional bits used in the quantization process. LA - English DB - MTMT ER - TY - JOUR AU - Al-Rikabi, Hussein AU - Renczes, Balázs TI - Floating-Point Quantization Analysis of Multi-Layer Perceptron Artificial Neural Networks JF - JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY J2 - J SIGNAL PROCESS SYS PY - 2024 PG - 12 SN - 1939-8018 DO - 10.1007/s11265-024-01911-0 UR - https://m2.mtmt.hu/api/publication/34714521 ID - 34714521 AB - The impact of quantization in Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANNs) is presented in this paper. In this architecture, the constant increase in size and the demand to decrease bit precision are two factors that contribute to the significant enlargement of quantization errors. We introduce an analytical tool that models the propagation of Quantization Noise Power (QNP) in floating-point MLP ANNs. Contrary to the state-of-the-art approach, which compares the exact and quantized data experimentally, the proposed algorithm can predict the QNP theoretically when the effect of operation quantization and Coefficient Quantization Error (CQE) are considered. This supports decisions in determining the required precision during the hardware design. The algorithm is flexible in handling MLP ANNs of user-defined parameters, such as size and type of activation function. Additionally, a simulation environment is built that can perform each operation on an adjustable bit precision. The accuracy of the QNP calculation is verified with two publicly available benchmarked datasets, using the default precision simulation environment as a reference. © The Author(s) 2024. LA - English DB - MTMT ER - TY - CONF AU - Paolo, Carbone AU - Renczes, Balázs AU - Alessio, De Angelis AU - Antonio, Moschitta TI - Calibrated Sinefit Based on Quantized Data T2 - Proceedings of the IEEE I2MTC conference PY - 2024 UR - https://m2.mtmt.hu/api/publication/34714453 ID - 34714453 N1 - Accepted for publication/ Közlésre elfogadva LA - English DB - MTMT ER -