Literatura científica selecionada sobre o tema "AgRP neuron"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "AgRP neuron".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "AgRP neuron"
Oh, Youjin, Eun-Seon Yoo, Sang Hyeon Ju, Eunha Kim, Seulgi Lee, Seyun Kim, Kevin Wickman e Jong-Woo Sohn. "GIRK2 potassium channels expressed by the AgRP neurons decrease adiposity and body weight in mice". PLOS Biology 21, n.º 8 (18 de agosto de 2023): e3002252. http://dx.doi.org/10.1371/journal.pbio.3002252.
Texto completo da fonteKlima, Michelle, Amber Alhadeff, Kayla Kruger, Santiago Pulido, Aaron McKnight e J. Nicholas Betley. "A Neural Circuit for the Suppression of Peripheral Inflammation by Hunger". Journal of Immunology 204, n.º 1_Supplement (1 de maio de 2020): 228.23. http://dx.doi.org/10.4049/jimmunol.204.supp.228.23.
Texto completo da fonteLin, Chiu-Ya, Kun-Yun Yeh, Hsin-Hung Lai e Guor Mour Her. "AgRP Neuron-Specific Ablation Represses Appetite, Energy Intake, and Somatic Growth in Larval Zebrafish". Biomedicines 11, n.º 2 (9 de fevereiro de 2023): 499. http://dx.doi.org/10.3390/biomedicines11020499.
Texto completo da fontevan de Wall, Esther, Rebecca Leshan, Allison W. Xu, Nina Balthasar, Roberto Coppari, Shun Mei Liu, Young Hwan Jo et al. "Collective and Individual Functions of Leptin Receptor Modulated Neurons Controlling Metabolism and Ingestion". Endocrinology 149, n.º 4 (27 de dezembro de 2007): 1773–85. http://dx.doi.org/10.1210/en.2007-1132.
Texto completo da fontePadilla, Stephanie L., Jian Qiu, Casey C. Nestor, Chunguang Zhang, Arik W. Smith, Benjamin B. Whiddon, Oline K. Rønnekleiv, Martin J. Kelly e Richard D. Palmiter. "AgRP to Kiss1 neuron signaling links nutritional state and fertility". Proceedings of the National Academy of Sciences 114, n.º 9 (14 de fevereiro de 2017): 2413–18. http://dx.doi.org/10.1073/pnas.1621065114.
Texto completo da fonteNa, Junewoo, Byong Seo Park, Doohyeong Jang, Donggue Kim, Thai Hien Tu, Youngjae Ryu, Chang Man Ha et al. "Distinct Firing Activities of the Hypothalamic Arcuate Nucleus Neurons to Appetite Hormones". International Journal of Molecular Sciences 23, n.º 5 (26 de fevereiro de 2022): 2609. http://dx.doi.org/10.3390/ijms23052609.
Texto completo da fonteFang, Xing, Shujun Jiang, Jiangong Wang, Yu Bai, Chung Sub Kim, David Blake, Neal L. Weintraub, Yun Lei e Xin-Yun Lu. "Chronic unpredictable stress induces depression-related behaviors by suppressing AgRP neuron activity". Molecular Psychiatry 26, n.º 6 (11 de janeiro de 2021): 2299–315. http://dx.doi.org/10.1038/s41380-020-01004-x.
Texto completo da fonteHuang, Hu, Seung Hwan Lee, Chianping Ye, Ines S. Lima, Byung-Chul Oh, Bradford B. Lowell, Janice M. Zabolotny e Young-Bum Kim. "ROCK1 in AgRP Neurons Regulates Energy Expenditure and Locomotor Activity in Male Mice". Endocrinology 154, n.º 10 (1 de outubro de 2013): 3660–70. http://dx.doi.org/10.1210/en.2013-1343.
Texto completo da fonteLiu, Yang, Ying Huang, Tiemin Liu, Hua Wu, Huxing Cui e Laurent Gautron. "Lipopolysacharide Rapidly and Completely Suppresses AgRP Neuron-Mediated Food Intake in Male Mice". Endocrinology 157, n.º 6 (25 de abril de 2016): 2380–92. http://dx.doi.org/10.1210/en.2015-2081.
Texto completo da fonteCoutinho, Eulalia A., Melanie Prescott, Sabine Hessler, Christopher J. Marshall, Allan E. Herbison e Rebecca E. Campbell. "Activation of a Classic Hunger Circuit Slows Luteinizing Hormone Pulsatility". Neuroendocrinology 110, n.º 7-8 (21 de outubro de 2019): 671–87. http://dx.doi.org/10.1159/000504225.
Texto completo da fonteTeses / dissertações sobre o assunto "AgRP neuron"
Qu, Mengdi. "Molecular mechanism underlying CaMK1D-dependent function in AgRP neurons". Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAJ029.
Texto completo da fonteDisruption of stress response mechanisms in organisms can lead to cellular dysfunction and diseases like metabolic syndrome. Energy balance is mainly regulated by the central nervous system (CNS), which integrates hormonal, neuronal, and dietary signals from various tissues. Dysfunction in this system is linked to obesity and metabolic syndrome, both precursors to type 2 diabetes (T2D). Our laboratory discovered that calcium/calmodulin-dependent protein kinase ID (CaMK1D), a gene associated with T2D, promotes ghrelin-mediated food intake in mice. However, CaMK1D signaling in NPY/AgRP neurons still remains questions. In this work, we proformed RNA sequencing using the GT1-7 hypothalamic cell line. To this end, we found that CalHM6 is a downstream target of CaMK1D signaling. CalHM6 mRNA levels were significantly upregulated in CaMK1D-/- cells and downregulated when CaMK1D was re-expressed. This was confirmed in vivo in the hypothalamus of CaMK1D-/- mice. CalHM6, likely a voltage-gated calcium channel, showed increased intracellular Ca2+ levels in response to ghrelin in CaMK1D-/- cells compared to CaMK1D+/+ cells using jGCamps method. Altogether, our work showed CalHM6 is a novel target of CaMK1D. High CaMK1D, leading to low CalHM6 expression, may enhance food intake and obesity by modulating calcium-dependent signaling in NPY/AgRP neuron
Zagmutt, Caroca Sebastián. "Analysis of the in vivo effect of carnitine palmitoyltransferase 1A deletion in AgRP neurons". Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/671758.
Texto completo da fonteHuang, Cao Zhen Fang. "Neuronal circuits and reinforcement mechanisms underlying feeding behaviour". Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/247221.
Texto completo da fonteRamírez, Flores Sara. "Hypothalamic Ceramide Levels regulated by CPT1C mediate the Orexigenic effect of Ghrelin". Doctoral thesis, Universitat Internacional de Catalunya, 2014. http://hdl.handle.net/10803/276184.
Texto completo da fonteHaverty, Maureen. "The microstructure, texture and thermal expansion of nuclear graphite". Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/the-microstructure-texture-and-thermal-expansion-of-nuclear-graphite(5fef7053-d4ca-42b8-a203-d5b2e6d5bedc).html.
Texto completo da fonteBuccarello, L. "EFFECTS OF DIFFERENT DIETS IN A MOUSE MODEL OF NEURODEGENERATIVE DISEASE". Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/339393.
Texto completo da fonteMascanzoni, Elisa. "Epidemiology of herbicide resistance evolution in rice weeds and variability in Echinochloa spp". Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3425887.
Texto completo da fonteLa resistenza agli erbicidi è un problema che è cresciuto esponenzialmente negli ultimi 10 anni e riguarda tutte le più importanti colture al mondo. L’Italia è il primo produttore europeo di riso con 230.000 ha localizzati nel nord-ovest della Pianura Padana. Ad oggi 6 infestanti hanno evoluto popolazioni resistenti in riso in Italia, fra queste la più importante è Echinochloa spp. La ricerca si è svolta su due binari paralleli: il primo è uno studio epidemiologico a larga scala sulla principale area riso, il secondo è uno studio sulla classificazione di Echinochloa spp. che comprendente esperimenti di dose risposta su varie specie di Echinochloa spp.agli erbicidi. Obiettivo dello studio epidemiologico era analizzare, a livello comunale, il grado di associazione fra presenza di resistenza e tre importanti predittori agronomici: percentuale di semina in acqua, tessitura del suolo e percentuale di rotazione. Le analisi sono state fatte con più approcci statistici: l’analisi discriminante e la regressione logistica hanno permesso di individuare un alto grado di associazione fra la presenza di resistenza ed i predittori. L’approccio Neural Network ha permesso di calcolare il rischio di evolvere popolazioni resistenti sulla base dei predittori: 70% quando tutte le infestanti resistenti sono state considerate, 30% quando solo i casi di Echinochloa spp. sono stati inclusi, perdendo parte del potere predittivo forse a causa della minore quantità di casi di resistenza inclusi nel database. In Qgis sono state create mappe per illustrare la diffusione dei tre predittori nell’area dello studio e il rischio di evoluzione della resistenza. 20 popolazioni di giavone sono state raccolte nei comuni dove non sono mai stati segnalati casi di resistenza al GIRE ed è stato fatto uno screening. 16 popolazioni sono risultate resistenti ACCase+ALS mostrando che in queste aree la resistenza è presente, ma viene tenuta ad un livello accettabile per gli agricoltori. La seconda parte della ricerca riguardava la classificazione delle diverse specie di giavone, abbinando la classificazione fenotipica alla discriminazione fatta attraverso marcatori molecolari usando il DNA barcoding. 40 accessioni di giavone sono state raccolte da piante singole nel 2015, sono state classificate morfologicamente e testate per la sensibilità al penoxsulam. Fra le piante sensibili, 10 sono state riprodotte per ottenere una quantità maggiore di seme. Lo screening con il penoxsulam è stato ripetuto sulle accessioni riprodotte a confermare la sensibilità. La classificazione morfologica e la discriminazione per marcatori molecolari sono state fatte sia sulle piante originali che sulle riprodotte, dando risultati consistenti: nelle popolazioni riprodotte usando Costea & Tardif (2002) sono presenti solo E. crus-galli ed E. oryzicola, usando Tabacchi et al. (2006) sono presenti E. crus-galli, E. oryzicola ed E. phyllopogon. Sono state analizzate 5 regioni di DNA cloroplastico: matK ha discriminato fra le diverse specie di giavone bianco, oltre a E. crus-galli offrendo un buon match, anche se incompleto, con la classificazione di Tabacchi et al. (2006). Le sequenze di rbcL invece hanno differenziato solo E. oryzicola da E. crus-galli, corrispondendo perfettamente a Costea & Tardif (2002) Un protocollo di PCR specie-specifica è stato impostato su matK gene per discriminare diverse specie di giavoni bianchi in una sola reazione di PCR. Gli esperimenti di dose-risposta sono stati fatti sia in serra che all’aperto su 9 accessioni con 3 erbicidi: cyhalofop – butyl, penoxsulam e florpyrauxifen benzyl: anche se i risultati sono stati variabili, soprattutto nell’esperimento all’aperto, è chiaro che i vari erbicidi hanno efficacia simile su tutte le specie di Echinochloa. I risultati ottenuti dimostrano che pianificare le strategie erbicide sulla base delle diverse specie di Echinochla possa essere erroneo.
Mesaros, Andrea [Verfasser]. "Activation of Stat3 signaling in AgRP neurons promotes locomotor activity / vorgelegt von Andrea Mesaros". 2008. http://d-nb.info/989800385/34.
Texto completo da fonteLivros sobre o assunto "AgRP neuron"
IEEE International Workshop on Cellular Neural Networks and Their Applications. (1990 Budapest, Hungary). 1990 IEEE International Workshop on Cellular Neural Networks and Their applications, CNNA-90: Proceedings : Hotel Agro, Budapest, Hungary, December 16-19, 1990. [New York]: Institute of Electrical and Electronics Engineers, 1990.
Encontre o texto completo da fonteWójcik-Gładysz, Anna. Ghrelin – hormone with many faces. Central regulation and therapy. The Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences, 2020. http://dx.doi.org/10.22358/mono_awg_2020.
Texto completo da fonteCapítulos de livros sobre o assunto "AgRP neuron"
Huang, Cong, Huiping Lin e Yuhan Xiao. "AGRP: A Fused Aspect-Graph Neural Network for Rating Prediction". In Neural Information Processing, 597–608. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92273-3_49.
Texto completo da fonteHan, Yong, Guobin Xia e Qi Wu. "Functional Interrogation of the AgRP Neural Circuits in Control of Appetite, Body Weight, and Behaviors". In Advances in Experimental Medicine and Biology, 1–16. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1286-1_1.
Texto completo da fonteShamiul Islam, Md, Ummya Habiba, Md Abu Baten, Nazrul Amin, Imrus Salehin e Tasmia Tahmida Jidney. "Hybrid Convolution Neural Network with Transfer Learning Approach for Agro-Crop Leaf Disease Identification". In Lecture Notes on Data Engineering and Communications Technologies, 209–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-24475-9_18.
Texto completo da fonteHong, Yiling. "Impact of Silver Nanoparticles on Neurodevelopment and Neurodegeneration". In Neurotoxicity - New Advances. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101723.
Texto completo da fonteThiel, D. "AN INTERACTIVE NEURAL NETWORK FOR ANALYSING THE FOOD CONSUMER BEHAVIOUR STABILITY". In Agri-Food Quality II, 40–44. Elsevier, 1999. http://dx.doi.org/10.1533/9781845698140.2.40.
Texto completo da fonteNaidu, Diwakar, Babita Majhi e Surendra Kumar Chandniha. "Development of Rainfall Prediction Models Using Machine Learning Approaches for Different Agro-Climatic Zones". In Advances in Data Mining and Database Management, 72–94. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6659-6.ch005.
Texto completo da fonteSuganyadevi, S., D. Shamia, R. Arun Sekar e R. Deepa. "Automated diagnosis of disease in grape leaves using deep neural networks". In Agri 4.0 and the Future of Cyber-Physical Agricultural Systems, 257–77. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-443-13185-1.00014-9.
Texto completo da fonteKambo, Rubi, Jyothi Pillai e Sunita Soni. "ENHANCING AGRICULTURE COMMODITY PRICE FORECASTING: INTEGRATION OF DEEP LEARNING AND SOFT COMPUTING TECHNIQUES FOR ECONOMIC, FARMER, AND INDUSTRY PERSPECTIVES". In Futuristic Trends in Computing Technologies and Data Sciences Volume 3 Book 8, 113–20. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bkct8p2ch4.
Texto completo da fonteMadhuri J. e Indiramma M. "Big Data Analytics-Based Agro Advisory System for Crop Recommendation Using Spark Platform". In Advances in Business Information Systems and Analytics, 227–47. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7105-0.ch012.
Texto completo da fonteN.S. Sampaio, Pedro, e Carla Brites. "Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice". In Rice [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99017.
Texto completo da fonteTrabalhos de conferências sobre o assunto "AgRP neuron"
Ma, Yuyang, Chao Yang, Haixiang Guan, Jingyue Xu e Chuli Hu. "Prediction of Soil Organic Matter in Black Soil Region Based on BP Neural Network with Genetic Algorithm Variable Selection". In 2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/agro-geoinformatics262780.2024.10660717.
Texto completo da fonteYalcin, Hulya, e Salar Razavi. "Plant classification using convolutional neural networks". In 2016 5th International Conference on Agro-geoinformatics (Agro-geoinformatics). IEEE, 2016. http://dx.doi.org/10.1109/agro-geoinformatics.2016.7577698.
Texto completo da fonteChang, Gray S., Blaine Grover, John T. Maki e Misti A. Lillo. "The Feasibility Study of AGR 7-Position Fuel Testing Assembly in NEFT Position". In Fourth International Topical Meeting on High Temperature Reactor Technology. ASMEDC, 2008. http://dx.doi.org/10.1115/htr2008-58098.
Texto completo da fonteLeng, Chuang, Shanzhen Yi e Wenhao Xie. "Estimation of rainfall based on MODIS using neural networks". In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2019. http://dx.doi.org/10.1109/agro-geoinformatics.2019.8820239.
Texto completo da fonteKulaglic, Ajla, e B. Berk Ustundag. "Neural network with wavelet preprocessing for wheat growth stage estimation". In 2016 5th International Conference on Agro-geoinformatics (Agro-geoinformatics). IEEE, 2016. http://dx.doi.org/10.1109/agro-geoinformatics.2016.7577618.
Texto completo da fonteAskanian, Haroutioun, Ottavio Novello, Christian Coelho, Sophie Commereuc e Vincent Verney. "Application of agro-wastes for bio-composite materials". In THE SECOND ICRANET CÉSAR LATTES MEETING: Supernovae, Neutron Stars and Black Holes. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4937321.
Texto completo da fonteHawkes, Grant L., James W. Sterbentz e John T. Maki. "Thermal Predictions of the AGR-3/4 Experiment With Time Varying Gas Gaps". In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-36943.
Texto completo da fonteKouhalvandi, Lida, Ece Olcay Gunes e Serdar Ozoguz. "Algorithms for Speeding-Up the Deep Neural Networks For Detecting Plant Disease". In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2019. http://dx.doi.org/10.1109/agro-geoinformatics.2019.8820541.
Texto completo da fonteKang, Lingjun, Liping Di, Meixia Deng, Eugene Yu e Yang Xu. "Forecasting vegetation index based on vegetation-meteorological factor interactions with artificial neural network". In 2016 5th International Conference on Agro-geoinformatics (Agro-geoinformatics). IEEE, 2016. http://dx.doi.org/10.1109/agro-geoinformatics.2016.7577673.
Texto completo da fonteSawant, Suryakant, Rishabh Agarwal, Jayantrao Mohite, Ankur Pandit e Srinivasu Pappula. "Field Boundary Identification using Convolutional Neural Network and GIS on High Resolution Satellite Observations". In 2021 9th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2021. http://dx.doi.org/10.1109/agro-geoinformatics50104.2021.9530340.
Texto completo da fonteRelatórios de organizações sobre o assunto "AgRP neuron"
Gothilf, Yoav, Roger Cone, Berta Levavi-Sivan e Sheenan Harpaz. Genetic manipulations of MC4R for increased growth and feed efficiency in fish. United States Department of Agriculture, janeiro de 2016. http://dx.doi.org/10.32747/2016.7600043.bard.
Texto completo da fonteWalker, Billy, e Michael Reichenberger. MEASURED THERMAL AND FAST NEUTRON FLUENCE RATES ATR CYCLES 167A AGR. Office of Scientific and Technical Information (OSTI), julho de 2020. http://dx.doi.org/10.2172/1688702.
Texto completo da fonteWalker, Billy, e Michael Reichenberger. MEASURED THERMAL AND FAST NEUTRON FLUENCE RATES ATR CYCLES 168A AGR. Office of Scientific and Technical Information (OSTI), dezembro de 2020. http://dx.doi.org/10.2172/2376855.
Texto completo da fonteSmith, Larry, e Michael Reichenberger. MEASURED THERMAL AND FAST NEUTRON FLUENCE RATES FOR AGR HOLDERS DURING CYCLE 166B. Office of Scientific and Technical Information (OSTI), março de 2020. http://dx.doi.org/10.2172/2370096.
Texto completo da fonte