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Vector-Borne Disinformation During Disasters and Emergencies
No full textDuring disasters and emergencies (earthquakes, pandemics, economic crises etc.), we also face a second challenge, pollution of information. The transmitted information may be false, potentially harmful and speculative. Today, the main source of information seems to be the social media, which behaves as a vector via sharing news. In this manuscript, the concept of the transmission dynamics of vector-borne diseases is adapted to the transmission dynamics of vector-borne disinformation. The dynamical behavior of the model is analyzed, the disinformation-free and disinformation endemic equilibria of the model are found and both their local and global stabilities are presented. Finally, numerical simulations are carried out to support the analytical results of the dynamical transmission of disinformation.(C) 2022 Elsevier B.V. All rights reserved
Evaluation of the Etiological Factors of Thyroid Gland Neoplasms: Our Clinical Experience
No full textObjective: Thyroid cancer (TC), the most common endocrine malignancy worldwide, has a 10-year survival rate of more than 90% and a better prognosis than othe
Variability of Pharmacogenomics Information in Drug Labels Approved by Different Agencies and Its Ethical Implications
No full textAims: The aim of this study was to determine if there are discrepancies among various agency-approved labels for the same active ingredient and where the labels approved by the Turkish Medicines and Medical Devices Agency (TMMDA) stand regarding the inclusion of PGx and discuss these ethical implications. Background: The efficacy and safety of drugs can be improved by rational prescription and personalization of medicine for each patient. Pharmacogenomics information (PGx) in Drug Labels (DL) is one of the important tools for the personalization of medications because genetic differences may affect both drug efficacy and safety. Providing adequate PGx to patients has ethical implications. Objective: The study aims to evaluate PGx in the DLs approved by TMMDA and other national agencies provided by the Pharmacogenomics Knowledgebase. Methods: DL annotations from the Pharmacogenomics Knowledgebase and DLs approved by the TMMDA were analyzed according to information and action levels, which are testing required, testing reconunended, actionable, and informative. Results: There are 381 drugs listed in PharmGKB drug label annotations with pharmacogenomics information, and 278 of these have biomarkers. A total of 242 (63.5%) drugs are approved and available in Turkey. Of these, 207 (85.5%) contain the same information as in or similar to that in the labels approved by the other agencies. The presence and level of information varied among the DLs approved by different agencies. The inconsistencies may have an important effect on the efficacy and the safety of drugs. Conclusion: These findings suggest a need for the standardization of PGx information globally because it may not only affect the efficacy and safety of medications but also essential ethical rules regarding patient rights by violating not sufficiently sharing all available information
Search for Associated Production of a Z Boson With an Invisibly Decaying Higgs Boson or Dark Matter Candidates at S=13 Tev With the Atlas Detector
No full textA search for invisible decays of the Higgs boson as well as searches for dark matter candidates, produced together with a leptonically decaying Z boson, are presented. The analysis is performed using proton?proton collisions at a centre-of-mass energy of 13 TeV, delivered by the LHC, corresponding to an integrated luminosity of 139 fb?1 and recorded by the ATLAS experiment. Assuming Standard Model cross-sections for ZH production, the observed (expected) upper limit on the branching ratio of the Higgs boson to invisible particles is found to be 19% (19%) at the 95% confidence level. Exclusion limits are also set for simplified dark matter models and two-Higgs-doublet models with an additional pseudoscalar mediator. © 2022 The Author(s)We acknowledge the support of ANPCyT , Argentina; YerPhI , Armenia; ARC , Australia; BMWFW and FWF , Austria; ANAS , Azerbaijan; SSTC , Belarus; CNPq and FAPESP , Brazil; NSERC , NRC and CFI , Canada; CERN ; ANID , Chile; CAS , MOST and NSFC , China; Minciencias , Colombia; MEYS CR , Czech Republic; DNRF and DNSRC , Denmark; IN2P3 - CNRS and CEA - DRF / IRFU , France; SRNSFG , Georgia; BMBF , HGF and MPG , Germany; GSRI , Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center , Israel; INFN , Italy; MEXT and JSPS , Japan; CNRST , Morocco; NWO , Netherlands; RCN , Norway; MEiN , Poland; FCT , Portugal; MNE/IFA , Romania; JINR ; MES of Russia and NRC KI , Russian Federation; MESTD , Serbia; MSSR , Slovakia; ARRS and MIZŠ , Slovenia; DSI/NRF , South Africa; MICINN , Spain; SRC and Wallenberg Foundation , Sweden; SERI , SNSF and Canton of Bern and Canton of Geneva , Switzerland; MOST , Taiwan; TAEK , Turkey; STFC , United Kingdom; DOE and NSF , United States of America. In addition, individual groups and members have received support from BCKDF , Canarie , Compute Canada and CRC , Canada; COST , ERC , ERDF , Horizon 2020 and Marie Sk?odowska-Curie Actions , European Union ; Investissements d'Avenir Labex , Investissements d'Avenir Idex and ANR , France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF , Greece; BSF-NSF and GIF , Israel; Norwegian Financial Mechanism 2014-2021 , Norway; NCN and NAWA , Poland; La Caixa Banking Foundation , CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana , Spain; Göran Gustafssons Stiftelse , Sweden; The Royal Society and Leverhulme Trust , United Kingdom.We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MES of Russia and NRC KI, Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZ?, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Canton of Bern and Canton of Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, Canarie, Compute Canada and CRC, Canada; COST, ERC, ERDF, Horizon 2020 and Marie Sk?odowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; G?ran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [109].IN2P3-CNRS; CC-IN2P3; 2014-2021; National Science Foundation, NSF; U.S. Department of Energy, USDOE; Alexander von Humboldt-Stiftung, AvH; Arkansas Space Grant Consortium, ASGC; Brookhaven National Laboratory, BNL; Karlsruhe Institute of Technology, KIT; Disability Rights Fund, DRF; H2020 Marie Sk?odowska-Curie Actions, MSCA; Arizona-Nevada Academy of Science, ANAS; CERN; Göran Gustafssons Stiftelser; Natural Sciences and Engineering Research Council of Canada, NSERC; National Research Council Canada, NRC; Canada Foundation for Innovation, CFI; Science and Technology Facilities Council, STFC; Leverhulme Trust; Royal Society; European Research Council, ERC; European Cooperation in Science and Technology, COST; Australian Research Council, ARC; Neurosurgical Research Foundation, NRF; Singapore Eye Research Institute, SERI; Helmholtz-Gemeinschaft, HGF; Deutsche Forschungsgemeinschaft, DFG; Agence Nationale de la Recherche, ANR; Japan Society for the Promotion of Science, KAKEN; Ministry of Education, Culture, Sports, Science and Technology, MEXT; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Danmarks Grundforskningsfond, DNRF; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; National Natural Science Foundation of China, NSFC; Ministerstvo Školství, Mláde?e a T?lov?chovy, MŠMT; Fundação para a Ciência e a Tecnologia, FCT; Bundesministerium für Bildung und Forschung, BMBF; Chinese Academy of Sciences, CAS; Austrian Science Fund, FWF; Generalitat de Catalunya; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Joint Institute for Nuclear Research, JINR; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; Israel Science Foundation, ISF; Instituto Nazionale di Fisica Nucleare, INFN; Narodowe Centrum Nauki, NCN; Javna Agencija za Raziskovalno Dejavnost RS, ARRS; Ministry of Science and Technology, Taiwan, MOST; Centre National de la Recherche Scientifique, CNRS; Ministerio de Ciencia e Innovación, MICINN; Centre National pour la Recherche Scientifique et Technique, CNRST; Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA; Horizon 2020; British Columbia Knowledge Development Fund, BCKDF; European Regional Development Fund, ERDF; Defence Science Institute, DSI; Council on grants of the President of the Russian Federation; Institut National de Physique Nucléaire et de Physique des Particules, IN2P3; National Research Center "Kurchatov Institute", NRC KI; Narodowa Agencja Wymiany Akademickiej, NAW
Photobiomodulation With Polychromatic Light (600–1200 Nm) Improves Fat Graft Survival by Increasing Adipocyte Viability, Neovascularization, and Reducing Inflammation in a Rat Model
No full textObjectives: Unpredictability with the final volume and viability of the graft are the major concerns in fat grafting. An experimental study was conducted to increase graft retention using photobiomodulation (PBM) with polychromatic light in near-infrared region (600–1200 nm) by utilizing its stimulatory effects on angiogenesis, neovascularization, adipocyte viability, and anti-inflammatory properties. Methods: A total of 24 rats were divided into four groups (n = 6) according to the applied polychromatic light protocol to the recipient site (none, before fat transfer, after fat transfer, and combined). In all groups, inguinal fat pad was excised, measured for volume and weight, and transferred to the dorsum of the rat. At the end of the experiment, fat grafts were harvested from the recipient site for volume and weight measurements, histological, and immunohistochemical evaluation. Results: Intergroup comparison revealed that fat graft retention regarding weight and volume, was significantly superior in Group IV (p = 0.049 and p = 0.043, respectively), which polychromatic light was applied both before and after transfer of the graft. Hematoxylin–eosin and Masson's trichrome stained sections showed absence of necrosis, fibrosis, inflammation, cyst formation, and increased vascularization of both inner and outer zones of the grafts in Group IV. Also, immunohistochemical staining scores for perilipin (indicator for adipocyte viability), CD31 and VEGF (indicators for angiogenesis and neovascularization) were significantly higher (p 0.001). Ki67 scores were significantly lower in this group because of anti-inflammatory environment (p 0.001). Conclusions: Application of PBM to the recipient site before and after fat transfer improved outcomes in rats at 56 day after fat grafting by means of volume retention, increased neovascularization and adipocyte viability and reduced necrosis, fibrosis and inflammation. © 2021 Wiley Periodicals LLCTHD‐2019‐1821
De Novo Pustular Psoriasis Associated With Dupilumab Therapy in a Young Male With the Diagnosis of Atopic Dermatitis
No full text[Abstract Not Available
Hira: Hidden Row Activation for Reducing Refresh Latency of Off-The Dram Chips
No full textYaglikci, Abdullah Giray/0000-0002-9333-6077; Ergin, Oguz/0000-0003-2701-3787DRAM is the building block of modern main memory systems. DRAM cells must be periodically refreshed to prevent data loss. Refresh operations degrade system performance by interfering with memory accesses. As DRAM chip density increases with technology node scaling, refresh operations also increase because: 1) the number of DRAM rows in a chip increases; and 2) DRAM cells need additional refresh operations to mitigate bit failures caused by RowHammer, a failure mechanism that becomes worse with technology node scaling. Thus, it is critical to enable refresh operations at low performance overhead. To this end, we propose a new operation, Hidden Row Activation (HiRA), and the HiRA Memory Controller (HiRA-MC) to perform HiRA operations. HiRA hides a refresh operation's latency by refreshing a row concurrently with accessing or refreshing another row within the same bank. Unlike prior works, HiRA achieves this parallelism without any modifications to off-the-shelf DRAM chips. To do so, it leverages the new observation that two rows in the same bank can be activated without data loss if the rows are connected to different charge restoration circuitry. We experimentally demonstrate on 56 real off-the-shelf DRAM chips that HiRA can reliably parallelize a DRAM row's refresh operation with refresh or activation of any of the 32% of the rows within the same bank. By doing so, HiRA reduces the overall latency of two refresh operations by 51.4%. HiRA-MC modifies the memory request scheduler to perform HiRA when a refresh operation can be performed concurrently with a memory access or another refresh. Our system-level evaluations show that HiRA-MC increases system performance by 12.6% and 3.73x as it reduces the performance degradation due to periodic refreshes and refreshes for RowHammer protection (preventive refreshes), respectively, for future DRAM chips with increased density and RowHammer vulnerability.We thank our shepherd and the reviewers of ASPLOS'22, ISCA'22, and MICRO'22 for valuable feedback. We thank the SAFARI Research Group members for useful feedback and the stimulating intellectual environment they provide. We acknowledge the generous gifts provided by our industrial partners, including Google, Huawei, Intel, Microsoft, and VMware, and support from the Microsoft Swiss Joint Research Center.Microsoft Swiss Joint Research Cente
Jest ve Mimiklerden Yapay Sinir Ağları ile Duygu Sınıflandırma
No full textClassifying the right emotion from different data sources such as text, images, video, and speech has been an inspiring field for researchers from various disciplines. Automatic emotion detection from videos and photos is one of the challenging topics being studied using supervised and unsupervised machine learning methods. In this thesis, several preprocessing steps and a new deep learning architecture and emotion classification method from videos are presented. The face and body position information obtained from the videos using the OpenPose tool was converted into pose descriptors for use in the models, then the LSTM and Transformer models were trained with this data and their performances were compared. Then, the output of the CNN block fed with pose descriptors was used as input for the LSTM and Transformer models. Video Generation, Keyframe Selection, and Gaussian Mixture Center approaches were added as preprocessing steps to improve model accuracy and the experiments were repeated for combinations of these different approaches. After extensive experiments, the results were compared and the effects of the proposed two-layer classifier structure and preprocessing steps were observed. Results were also compared with other recent, high-accuracy methods using the same dataset. Experiments using two common datasets, FABO and CK+, showed that the CNN-Transformer structure with video generation approach for the FABO dataset outperforms the other models, with an accuracy of 99%. For both datasets, the proposed method in many versions achieved remarkable success, reaching an accuracy of over 90%.Metin, resim, video ve konuşma gibi farklı veri kaynaklarından doğru duyguyu sınıflandırmak, çeşitli disiplinlerden araştırmacılar için ilham verici bir alanı olmuştur. Videolardan ve fotoğraflardan otomatik duygu algılama, denetimli ve denetimsiz makine öğrenimi yöntemleri kullanılarak üzerinde çalışılan zorlu konulardan biridir. Bu tez çalışmasında bir takım ön işleme adımları ve yeni bir derin öğrenme mimarisi ile videolardan duygu analizi yöntemi sunulmaktadır. Videolardan OpenPose aracı kullanılarak elde edilen yüz ve vücut pozisyon bilgileri modellerde kullanılmak üzere poz tanımlayıcılara dönüştürüldü, ardından LSTM ve Dönüştürücü modelleri bu veri ile eğitilerek performansları karşılaştırıldı. Ardından LSTM ve Dönüştürücü modellerine bir CNN bloğu ön katman olarak eklenmiş poz tanımlayıcılarla beslenen CNN bloğunun çıktısı LSTM ve Dönüştürücü modelleri için girdi olarak kullanıldı. Model doğruluklarını iyileştirmek amacı ile Video Çoklama, Anahtar Kare Seçimi ve Gauss Karışım Merkezi yaklaşımları ön işleme adımları olarak eklenmiş ve deneyler bu farklı yaklaşımların kombinaysonları için tekrarlandı. Yapılan kapsamlı deneylerin ardından sonuçlar karşılaştırıldı ve önerilen iki katmanlı sınıflandırıcı yapısı ve ön işleme adımlarının etkileri gözlemlendi. Sonuçlar ayrıca aynı veri kümesini kullanan güncel, yüksek doığruluk oranlarına sahip diğer yöntemlerle de karşılaştırıldı. FABO ve CK+ olmak üzere iki yaygın veri kümesi kullanılarak gerçekleştirilen deneyler, FABO veri seti için video çoklama uygulanmış CNN-Dönüştürücü yapısının %99 doğruluk oranı ile, diğer modellerden daha iyi bir performansa sahip olduğunu gösterdi. Her iki veri kümesi için de bir çok versiyonda önerilen model %90 üzerinde doğruluğa ulaşarak kayda değer başarımlar elde etti