3586 research outputs found
Sort by
Artificial Intelligence in Clinical Neuroscience
No full textArtificial Intelligence (AI) is a branch of computer science that focuses on replicating human intelligence in machines (Malik & Solanki, 2021), allowing them to possess problem-solving (Zeigler, Muzy, & Yilmaz, 2009), and decision-making abilities akin to the human brain (Malik & Solanki, 2021). AI methods undergo training using extensive datasets, enabling them to perform specific tasks. Subsequently, they use this acquired knowledge to evaluate unfamiliar data and generate targeted outcomes. One of the remarkable aspects of AI is its capacity to swiftly process massive datasets without human intervention. Advancements in hardware technologies have facilitated a progression from conventional machine learning to deep learning within the field of AI, resulting in the emergence of widely used applications such as natural language processing, speech recognition, computer vision, and image classification parameters (Rana, Rawat, Bijalwan, & Bahuguna, 2018). Moreover, ongoing advancements in hardware aim to move towards neuromorphic hardware, which would lower the energy consumption of AI systems, emulating the energy efficiency of the human brain (Berggren et al., 2020). In essence, AI empowers machines to intelligently and intuitively tackle complex problems and make informed decisions. © 2025 Elsevier B.V., All rights reserved
Search for long-lived heavy neutral leptons in proton-proton collision events with a lepton-jet pair associated with a secondary vertex at ?s=13 TeV
No full textA search for long-lived heavy neutral leptons (HNLs) using proton-proton collision data corresponding to an integrated luminosity of 138 fb(-1) collected at root s = 13TeV with the CMS detector at the CERN LHC is presented. Events are selected with a charged lepton originating from the primary vertex associated with the proton-proton interaction, as well as a second charged lepton and a hadronic jet associated with a secondary vertex that corresponds to the semileptonic decay of a long-lived HNL. No excess of events above the standard model expectation is observed. Exclusion limits at 95% confidence level are evaluated for HNLs that mix with electron and/or muon neutrinos. Limits are presented in the mass range of 1-16.5 GeV, with excluded square mixing parameter values reaching as low as 2 x 10(-7). For masses above 11 GeV, the presented limits exceed all previous results in the semileptonic decay channel, and for some of the considered scenarios are the strongest to date.FWF; FNRS; FWO (Belgium); CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); BNSF (Bulgaria); MoST; NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG [MoER TK202]; Academy of Finland; MEC; CEA; CNRS/IN2P3 (France); SRNSF; BMBF; DFG; HGF (Germany); NKFIH (Hungary); DAE; DST; IPM; SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE; UM (Malaysia); BUAP; CONACYT; UASLP-FAI (Mexico); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA; TUBITAK; DOE; NSF; Marie-Curie program; European Research Council; Horizon 2020 Grant [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee [22rl-037]; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); FWO (Belgium) under the Excellence of Science - EOS [30820817]; Beijing Municipal Science AMP; Technology Commission [Z191100007219010]; Fundamental Research Funds for the Central Universities (China); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Shota Rustaveli National Science Foundation [FR-22-985]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 390833306, 400140256 - GRK2497]; Hellenic Foundation for Research and Innovation (HFRI) [2288]; Hungarian Academy of Sciences [K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Council of Science and Industrial Research, India; ICSC -National Research Center for High Performance Computing, Big Data and Quantum Computing - NextGenerationEU program (Italy); Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; Fundacao para a Ciencia e a Tecnologia [CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund; ERDF a way of making Europe [MDM-2017-0765]; Programa Severo Ochoa del Principado de Asturias (Spain); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources AMP; Institutional Development, Research and Innovation [B39G670016]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.)We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).r Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science - EOS -be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), among others, under Germany's Excellence Strategy - EXC 2121 Quantum Universe - 390833306, and under project number 400140256 - GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program - UNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC -National Research Center for High Performance Computing, Big Data and Quantum Computing and FAIR -Future Artificial Intelligence Research, funded by the NextGenerationEU program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF a way of making Europe, and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B39G670016 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.)
Detection of Critical Links for Improving Network Resilience
No full textIdentifying and eliminating critical links in multi-hop networks is essential for enhancing overall network resilience. In this study, we propose a novel algorithm to detect links that significantly impact the pairwise connectivity of multi-hop networks. We formulate the critical link detection problem as minimizing pairwise connectivity subject to a total edge weight constraint c. The proposed method first computes the maximum flow between neighboring nodes to evaluate strong connections, and then progressively contracts these nodes to expose weaker connections. Throughout this iterative process, the algorithm records previously identified flows to minimize redundant flow computations. At each step, it also keeps track of the cut sets that reduce the network's pairwise connectivity. Ultimately, it selects the subset of these cut sets whose removal minimizes pairwise connectivity while satisfying the total weight constraint c. This approach consistently identifies fewer yet more impactful critical edges than traditional Min-Cut or Greedy strategies. We evaluate the performance of our method against existing algorithms across various network sizes and node degrees. Experimental results show that the proposed method consistently discovers more influential edges and achieves a 34-38% reduction in pairwise connectivity, outperforming Greedy (22-24%), Min-Cut (24-32%), and Degree-based (12-19%) methods.Technological Research Council of Turkey (TUBITAK); [121E500]This research was funded by the Technological Research Council of Turkey (TUBITAK) project number 121E500
Cross-cultural data on romantic love and mate preferences from 117,293 participants across 175 countries
No full textPsychological studies on close relationships have often overlooked cultural diversity, dynamic processes, and potentially universal principles that shape intimate partnerships. To address the limited generalizability of previous research and advance our understanding of romantic love experiences, mate preferences, and physical attractiveness, we conducted a large-scale cross-cultural survey study on these topics. A total of 404 researchers collected data in 45 languages from April to August 2021, involving 117,293 participants from 175 countries. Aside from standard demographic questions, the survey included valuable information on variables relevant to romantic relationships: intimate, passionate, and committed love within romantic relationships, physical-attractiveness enhancing behaviors, gender equality endorsement, collectivistic attitudes, personal history of pathogenic diseases, relationship quality, jealousy, personal involvement in sexual and/or emotional infidelity, relational mobility, mate preferences, and acceptance of sugar relationships. The resulting dataset provides a rich resource for investigating patterns within, and associations across, a broad range of variables relevant to romantic relationships, with extensive opportunities to analyze individual experiences worldwide.National Science Center, Poland [2019/33/N/HS6/00054]; Foundation for Polish Science (FNP) START scholarship; Estonian Research Council [PRG2190]This work is the result of the research project funded by the National Science Center, Poland (2019/33/N/HS6/00054). Marta Kowal was supported by the Foundation for Polish Science (FNP) START scholarship. Toivo Aavik was supported by the Estonian Research Council grant (PRG2190). The authors would like to thank the following scholars for their help with the translation: Christin-Melanie Vauclair Melanie, Catia Carvalho, Diogo Lamela, Elena Piccinelli, Anabela Caetano Santos, Patricia Arriaga, and Isabel Pinto (Portuguese), Stanislava Stoyanova (Bulgarian), Vira Hrabchuk and Anne MacFarlane (Ukrainian). The authors would also like to thank the following organizations and individuals for their help with organizing data collection in El Salvador: the Escuela de Comunicacion Monica Herrera, Directora Nicole Paetz, asistente Maria Erlinda Avalos, Diego Infante, and Gabriela Quintanilla. Finally, the authors would like to thank all participants who devoted their time to answer the survey and to share the survey link with others
Development of undoped or doped zinc and zirconium based metal oxides for voltammetric sensing of DNA
No full textIn this study, metal oxide nanoparticles (MONPs), specifically ZnO, Al-ZnO, Gd-ZnO, Gd-ZrO2, and Cu-ZrO2, were synthesized using the sol-gel combustion method and characterized by X-ray diffraction (XRD) technique. These MONPs were used to modify Pencil Graphite Electrodes (PGEs), and their effects on ferricyanide and guanine signal enhancement were evaluated using Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV) for DNA detection for the first time. Gd-ZnO modification resulted in enhanced ferricyanide signal sensitivity (approximately 24 mu A), while Cu-ZrO2-modified electrodes showed an almost threefold increase in guanine signal intensity. The detection limit for Cu-ZrO2-modified PGEs was calculated as 20.8 ng/mL. The developed MONP-based single-use analysis platforms offer rapid analysis, with 3 min for ferricyanide measurements and 22 min for DNA monitoring. While the metal oxide nanomaterials used in the system offer easy synthesis and low cost, the developed systems provide low detection limits and rapid analysis for DNA sensing
Spectroscopic Evaluation of DNA Binding Activities of Copper (II) Phthalocyanine Complex Consisting of Tetrakis-(4-Tritylphenoxy) Ligand
No full textThe structure and basic properties of Cu(II) phthalocyanine compound possessing tetrakis-(4-tritylphenoxy) group were elucidated in a past study with the help of absorption and infrared spectroscopic equipments. The electronic spectra, emission spectroscopy, gel agarose electrophoresis and thermal melting were employed to reveal the DNA interaction functions of this complex at changing concentrations of CT-DNA. In this experiment, the binding constant for the Cu(II) phthalocyanine compound which contains the tetrakis(4-tritylphenoxy) group was computed to be 1.53 x 106 M-1. The data obtained from absorption and fluorescence spectroscopic studies revealed that the CuPc compound reacted with CT-DNA through an intercalating mechanism. Well as the above methods, melting temperature and electrophoresis were also employed to analyse the interaction feature of CuPc with DNA. The interaction of the CuPc compound with DNA was also confirmed by data from melting temperature and electrophoresis experiments. Within the framework of the results obtained, it is predicted that CuPc compound may be a possible cancer therapeutic agent. © 2025 Elsevier B.V., All rights reserved
Use of indirect methods and machine learning algorithms for the estimation of reference intervals, taking cortisol measurements as an example
No full textObjectives This study aims to determine reliable reference intervals (RIs) for total cortisol (TC) in adults considering the effects of both age and blood collection time, using indirect methods and machine learning approaches. Methods Serum TC results from blood samples collected between 08:00 and 10:00 am at the first outpatient visit were included in the study. Serum TC were measured using a Roche Elecsys Cortisol II kit. Estimated RIs by indirect methods with the support of R packages (refineR and reflimR) for the implementation of machine learning algorithms (mclust and rpart) were compared with the manufacturer's reference interval (RI) (48-195 mu g/L). Results Estimated RIs by refineR and reflimR (57-256 mu g/L and 62-271 mu g/L, respectively) were wider than the manufacturer's RI. When reflimR was applied to Box-Cox-transformed data with the lambda value of 0.284 suggested by refineR, an RI of 57-251 mu g/L was obtained, which was like that obtained with refineR. An even better match with the manufacturer's RI was achieved using Gaussian mixture modelling with the mclust, which suggested one out of four clusters with an RI of 55.8-187 mu g/L. Clustering the data with rpart suggested stratification into two age groups (35 years) and three blood collection periods (08:00-08:45, 08:45-09:35, and 09:35-10:00). The TC levels demonstrated the highest concentrations in the early morning (8:00-08:45) and in young adults (18-35 years). Conclusions This study highlights the necessity of considering both age and blood collection time in clinical interpretation and demonstrates the effectiveness of indirect methods and machine learning approaches in the verification of RIs for hormones with known heterogeneity
Evaluation of potential candidate genes in the differentiation of neoplastic and non-neoplastic conditions of endometrium
No full textBackground: Endometrial cancer is one of the most common malignancies and alternative successful biomarkers are needed in the diagnostic process. Objectives: This study aimed to investigate candidate genes' diagnostic potential in endometrial neoplasia. Material and Methods: The expression levels of USP28, JAG2, AURKA, PGK1, HRPT1, EZH2, YAP, and P53 genes were evaluated by qPCR and/or immunohistochemistry analyses on endometrioid-type adenocarcinomas including all three grades, as well as from non-atypical hyperplasia, and atypical hyperplasia. Results: We determined significant (p < 0.05) increases in USP28, PGK1, EZH2, JAG2, AURKA, and YAP mRNA expressions in endometrial cancer tissues. Significant differences in the expressions of USP28 and P53 genes were determined between tumor grade groups (p = 0.002, and p = 0.005, respectively). Immunohistochemically, significant differences were found between the study groups (p < 0.001) and tumor grades (p = 0.013) by the evaluation of USP28. Statistically significant differences were found between all study groups (p < 0.001) and tumor grades (p = 0.008) in terms of PGK1 immunohistochemical expressions. A positive correlation (p = 0.002, r = 0.356) was found between p53 and PGK1. Conclusions: Considering our qPCR and immunohistochemistry results together, it was concluded that USP28, PGK1, EZH2, JAG2, AURKA, HPRT1and YAP expressions could offer beneficial expression values in precancerous lesions and endometrial cancer. © The Author(s) 2025.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAKKirikkale University Coordination Office of Scientific Research Projects, (2020/065
Activity-aware electrocardiogram biometric verification utilising deep learning on wearable devices
No full textWith the advancement of technology and the increasing use of wearable devices, information security have become a necessity. Although many biometrics authentication methods have been studied on these devices to ensure information security, an activity-aware deep learning (DL) model that is compatible with different device types and uses only electrocardiogram signals has not been studied. Our objective is to investigate DL models that exclusively use ECG signals during several physical activities, facilitating their implementation on various devices. Through this research, we aim to contribute to the advancement of wearable devices for the purpose of biometric verification. In this context, this study investigates the application of adaptive techniques that rely on prior activity classification to potentially improve biometric performance using DL models. In this study, we compare three time-frequency representations to generate images for activity classification using GoogleNet, ResNet50 and DenseNet201, and for biometric verification using ResNet50 and DenseNet201. Despite employing various convolutional neural network (CNN) models, we could not achieve high accuracy in activity classification. Consequently, manually classified samples were used for activity-aware biometric verification. We also provide a detailed comparison of various DL parameters. We use a public dataset simultaneously collected from both medical and wearable devices to offer a cross-device comparison. The results demonstrate that our method can be applied to both wearable and medical devices for activity classification and biometric verification. Besides, although it is known that DL requires a large amount of training data, our model, which was created using a small amount of training data and a real-life biometric verification scenario, achieved comparable results to studies using a large amount of data. The model was achieved 0.16% to 30.48% better results when classified according to their physical activities
The Cognitive Behavioral Case Formulation of Panic Disorder
No full textPanik bozukluğun fobilerden ayırdına gidilebildiğinde toplumdaki yaygınlığının sanılandan da fazla olduğu görülmektedir. Bu bozukluk pek çok psikoterapi yaklaşımı ile ele alınsa da en etkili yaklaşımlardan birinin bilişsel davranışçı terapi olduğu bilinmektedir. Bilişsel davranışçı yaklaşımda panik bozukluğun ele alınmasında en temel noktalardan biri, diğer bozukluklarda da olduğu gibi vaka formülasyonundan geçmektedir. Vaka formülasyonu, bilişsel davranışçı teoriyi merkeze alarak bireyin halihazırda var olan problemlerini tanımlamayı içerdiği gibi; müdahaleye rehberlik sağlayabilecek nedenler, problem listesi, işlevsiz inançlar, sürdürücüler ve koruyucu faktörler hakkında çıkarımlar yapmayı da sağlamaktadır. Ayrıca vaka formülasyonu ile bireyin sahip olduğu problemi neden şu anda yaşıyor olduğunu açıklamak, teoriye dayalı tutarlı ve açıklayıcı çıkarımlar yapmak, müdahaleye karar vermek için yol haritası oluşturulmaktadır. Bu nedenle panik bozuklukta vaka formülasyonu çeşitli araştırmacılar tarafından ilgi çekici bulunmuştur ve bu hususta çeşitli modeller geliştirilmiştir. Bu derleme çalışmasında panik bozukluğun formülasyonu için kullanılan panik modelden yola çıkılarak geliştirilmiş olan yenilenmiş bilişsel panik bozukluk modeline ve bu modelin vaka formülasyonunu oluşturmada üstlendiği rehber role odaklanılmıştır.When panic disorder is distinguished from phobias, it becomes evident that its prevalence in society is higher than previously thought. Although this disorder can be addressed with many psychotherapy approaches. However, cognitive behavioral therapy (CBT) is known to be one of the most effective. In the cognitive-behavioral approach, one of the most fundamental aspects of addressing panic disorder, as with other disorders, is case formulation. Case formulation, which centers on cognitive behavioral theory, involves identifying the individual's current problems and making inferences about causes, problem lists, dysfunctional beliefs, maintaining factors, and protective factors that can guide the intervention. Additionally, case formulation helps explain why the individual is experiencing the problem at this moment, make consistent and explanatory inferences based on theory, and create a roadmap for deciding on the intervention. Therefore, case formulation in panic disorder has been found intriguing by various researchers, leading to the development of several models. This review focuses on the revised cognitive model of panic disorder and its guiding role in case formulation, developed based on the panic model used for formulating panic disorder