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Статті в журналах з теми "PCG signals"
Damani, Devanshi N., Divaakar Siva Baala Sundaram, Shivam Damani, Anoushka Kapoor, Adelaide M. Arruda Olson, and Shivaram P. Arunachalam. "INVESTIGATION OF SYNCHRONIZED ACQUISITION OF ELECTROCARDIOGRAM AND PHONOCARDIOGRAM SIGNALS TOWARDS ELECTROMECHANICAL PROFILING OF THE HEART." Biomedical Sciences Instrumentation 57, no. 2 (April 1, 2021): 305–12. http://dx.doi.org/10.34107/yhpn9422.04305.
Повний текст джерелаDEBBAL, S. M. "Heart Cardiac Sounds analysis using the Wigner distribution (WD) Method." Clinical Cardiology and Cardiovascular Interventions 04, no. 15 (September 20, 2021): 01–04. http://dx.doi.org/10.31579/2641-0419/216.
Повний текст джерелаDEBBAL, S. M., and F. BEREKSI-REGUIG. "DISCRIMINATION OF PATHOLOGICAL CASES OF THE CARDIACS SOUNDS SIGNALS BY THE WAVELET TRANSFORM." Journal of Mechanics in Medicine and Biology 05, no. 04 (December 2005): 517–30. http://dx.doi.org/10.1142/s0219519405001679.
Повний текст джерелаPauline, S. Hannah, Samiappan Dhanalakshmi, R. Kumar, R. Narayanamoorthi, and Khin Wee Lai. "A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal." Journal of Healthcare Engineering 2022 (April 30, 2022): 1–24. http://dx.doi.org/10.1155/2022/3039624.
Повний текст джерелаDEBBAL, S. M., and F. BEREKSI-REGUIG. "HEARTBEAT SOUND ANALYSIS WITH THE WAVELET TRANSFORM." Journal of Mechanics in Medicine and Biology 04, no. 02 (June 2004): 133–41. http://dx.doi.org/10.1142/s0219519404000916.
Повний текст джерелаDEBBAL, S. M., F. BEREKSI-REGUIG, and A. MEZIANE TANI. "THE FAST FOURIER TRANSFORM AND THE CONTINUOUS WAVELET TRANSFORM ANALYSIS OF THE PHONOCARDIOGRAM SIGNAL." Journal of Mechanics in Medicine and Biology 04, no. 03 (September 2004): 257–72. http://dx.doi.org/10.1142/s0219519404001028.
Повний текст джерелаYANG, LIJUN, SHUANG LI, ZHI ZHANG, and XIAOHUI YANG. "CLASSIFICATION OF PHONOCARDIOGRAM SIGNALS BASED ON ENVELOPE OPTIMIZATION MODEL AND SUPPORT VECTOR MACHINE." Journal of Mechanics in Medicine and Biology 20, no. 01 (February 2020): 1950062. http://dx.doi.org/10.1142/s0219519419500623.
Повний текст джерелаChien, Ying-Ren, Kai-Chieh Hsu, and Hen-Wai Tsao. "Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications." Applied Sciences 10, no. 17 (August 24, 2020): 5842. http://dx.doi.org/10.3390/app10175842.
Повний текст джерелаAziz, Sumair, Muhammad Umar Khan, Majed Alhaisoni, Tallha Akram, and Muhammad Altaf. "Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features." Sensors 20, no. 13 (July 6, 2020): 3790. http://dx.doi.org/10.3390/s20133790.
Повний текст джерелаBerraih, Sid Ahmed, Yettou Nour Elhouda Baakek, and Sidi Mohammed El Amine Debbal. "Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals." Polish Journal of Medical Physics and Engineering 27, no. 1 (March 1, 2021): 73–85. http://dx.doi.org/10.2478/pjmpe-2021-0010.
Повний текст джерелаДисертації з теми "PCG signals"
Жемчужкіна, Т. В., and Т. В. Носова. "Construction of bispectra for PCG signals." Thesis, НТУ «ХПІ», 2021. https://openarchive.nure.ua/handle/document/17555.
Повний текст джерелаЖемчужкіна, Т. В., та Т. В. Носова. "Сonstruction of phase portraits of PCG signals". Thesis, НТУ «ХПІ», 2021. https://openarchive.nure.ua/handle/document/17554.
Повний текст джерелаDaura, Ashiru Sani. "A wavelet-based method for the classification of PCG signals." Thesis, University of Newcastle Upon Tyne, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244474.
Повний текст джерелаGiacotto, Luca. "Signal integrity at PCB level." Université Joseph Fourier (Grenoble), 2003. http://www.theses.fr/2003GRE10075.
Повний текст джерелаBondurant, Philip D., and Andrew Driesman. "Smart PCM Encoder." International Foundation for Telemetering, 1995. http://hdl.handle.net/10150/611601.
Повний текст джерелаIn this paper, a new concept in PCM telemetry encoding equipment is described. Existing "programmable" PCM encoders allow only simple changes in the functionality of the hardware, such as input gain, offset, and word formatting. More importantly, these encoders do not provide capability for "in-flight" processing of signals and in general have not taken advantage of existing hardware and software digital signal processing technology. In-flight processing of signals can provide a significant reduction in the required transmission bandwidth, allowing additional data that may not have otherwise been transmitted to be sent on the telemetry channel. A modular digital signal processor (DSP) based PCM encoder architecture is described that has a set of on-board processing algorithms configurable via a simple-to-use graphical user interface. Algorithms included are compression (lossy and lossless), Fourier transforms of various resolutions (typically followed by peak detection to provide a data rate reduction), extreme values (max, min, rms), time filtering, regression, trajectory prediction, and serial data stream processing. Custom algorithms can be developed and included as part of the suite of processing algorithms. The preprocessing algorithms exist as firmware on the DSPs and can accommodate as many different signals as the processing bandwidth of the DSP can handle. Typically one DSP can handle many input signals and different algorithms. The encoder is programmable via a standard RS-232 serial interface allowing the signal input configuration, telemetry frame layout, and on-board processing algorithms to be changed quickly.
Noorzadeh, Saman. "Extraction de l'ECG du foetus et de ses caractéristiques grâce à la multi-modalité." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT135/document.
Повний текст джерелаFetal health must be carefully monitored during pregnancy to detect early fetal cardiac diseases, and provide appropriate treatment. Technological development allows a monitoring during pregnancy using the non-invasive fetal electrocardiogram (ECG). Noninvasive fetal ECG is a method not only to detect fetal heart rate, but also to analyze the morphology of fetal ECG, which is now limited to analysis of the invasive ECG during delivery. However, the noninvasive fetal ECG recorded from the mother's abdomen is contaminated with several noise sources among which the maternal ECG is the most prominent.In the present study, the problem of noninvasive fetal ECG extraction is tackled using multi-modality. Beside ECG signal, this approach benefits from the Phonocardiogram (PCG) signal as another signal modality, which can provide complementary information about the fetal ECG.A general method for quasi-periodic signal analysis and modeling is first described and its application to ECG denoising and fetal ECG extraction is explained. Considering the difficulties caused by the synchronization of the two modalities, the event detection in the quasi-periodic signals is also studied which can be specified to the detection of the R-peaks in the ECG signal.The method considers both clinical and signal processing aspects of the application on ECG and PCG signals. These signals are introduced and their characteristics are explained. Then, using PCG signal as the reference, the Gaussian process modeling is employed to provide the possibility of flexible models as nonlinear estimations. The method also tries to facilitate the practical implementation of the device by using the less possible number of channels and also by using only 1-bit reference signal.The method is tested on synthetic data and also on real data that is recorded to provide a synchronous multi-modal data set.Since a standard agreement for the acquisition of these modalities is not yet taken into much consideration, the factors which influence the signals in recording procedure are introduced and their difficulties and effects are investigated.The results show that the multi-modal approach is efficient in the detection of R-peaks and so in the extraction of fetal heart rate, and it also provides the results about the morphology of fetal ECG
Beya, Ouadi. "Analyse et reconnaissance de signaux vibratoires : contribution au traitement et à l'analyse de signaux cardiaques pour la télémédecine." Thesis, Dijon, 2014. http://www.theses.fr/2014DIJOS015/document.
Повний текст джерелаThe heart is a muscle. Its mechanical operation is like a pump charged for distributing and retrieving the blood in the lungs and cardiovascular system. Its electrical operation is regulated by the sinus node, a pacemaker or electric regulator responsible for triggering the natural heart beats that punctuate the functioning of the body.Doctors monitor the electromechanical functioning of the heart by recording an electrical signal called an electrocardiogram (ECG) or an audible signal : the phonocardiogram (PCG). The analysis and processing of these two signals are essential for diagnosis, to help detect anomalies and cardiac pathologies.The objective of this thesis is to develop signal processing tools on ECG and PCG to assist cardiologist in his analysis of these signals. The basic idea is to develop algorithms of low complexity and having inexpensive computing time. The primary interest is to ensure their easy implementation in a mobile heart monitoring system for use by the doctor or the patient. The second advantage lies in the possibility of automatic real-time analysis of signals with the mobile device, allowing control of the transmission of these signals to a removal of doubt.Numerous studies have led to significant advances in the analysis of ECG signals and the automatic recognition of cardiac conditions. Databases of real or synthetic signals annotated also assess the performance of new methods. PCG signals are much less studied, difficult to analyze and to interpret. The main methods (Fourier, wavelet and Wigner Ville) were tested : they do not allow automatic recognition of signatures, and an accurate understanding of their contents.Wavelet Transform (WT) on cardiac signals showed its effectiveness to filter and locate useful information, but it involves an external processing function (mother wavelet) whose the choice depends on the prior knowledge on the signal to be processed. This is not always suitable for cardiac signals. Moreover, the wavelet transform generally induces inaccuracies in the location due to the external function and optionally due to the sub- sampling of the signatures.The non-stationary nature of the ECG and PCG and their sensitivity to noise makes it difficult to separate an informative transition of a transition due to measurement noise. The choice of treatment tool should allow denoising and analysis of these signals without alteration or the processing tool delocalization of the singularities.In response to our objectives and considering these problems, we propose to rely primarily on empirical mode decomposition (EMD) and Hilbert Huang Transform (HHT) to develop solutions. The EMD is a non linear approach decomposing the signal in intrinsic signal (IMF), oscillations of the type FM-AM, giving a time/scale signal representation. Associated with the Hilbert transform (TH), the THH determines the instantaneous amplitude (IA) and instantaneous frequency (IF) of each mode, leading to a time/frequency representation of the ECG and PCG.Without involving an external function, EMD approach can restore (noise reduction), analyze and reconstruct the signal without relocation of its singularities. This approach allows to locate R peaks of the ECG, heart rate and study the cardiac frequency variability (CFV), locate and analyze the sound components B1 and B2 of the PCG.Among the trials and developments that we made, we present in particular a new method (EDA : empirical denoising approach) inspired by the EMD approach for denoising cardiac signals. We also set out the implementation of two approaches for locating ECG signature (QRS complex, T and P waves). The first is based on the detection of local maxima : in using Modulus Maxima and Lipschitz exponent followed by a classifier. The second uses NFLS, wich an nonlinear approach for the detection and location of unique transitions in the discrete domain
Law, E. L. "RF SPECTRAL CHARACTERISTICS OF RANDOM PCM/FM AND PSK SIGNALS." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612122.
Повний текст джерелаThe telemetry radio frequency (RF) spectrum is rapidly becoming more crowded. Therefore, telemetry system engineers and frequency managers must become more knowledgeable about the RF spectral characteristics of telemetry signals. This paper presents methods to calculate the expected RF spectrum of random non-return-to-zero (NRZ) pulse code modulation (PCM)/frequency modulation (FM) and phase shift key (PSK) signals. The discussion includes the effects of bit rate, peak deviation, premodulation filtering, and spectrum analyzer resolution bandwidth. The methods are easily implemented using a personal computer and a spreadsheet program with graphics capability. Calculated spectra agree well with measured spectra. Equations are presented for accurately estimating the peak deviation and unmodulated carrier power of a random NRZ PCM/FM signal from the measured RF spectrum. Adjacent channel interference is also calculated. Key words: radio frequency spectral occupancy, pulse code modulation, frequency modulation, phase shift keying, premodulation filtering, adjacent channel interference.
Ahlström, Christer. "Nonlinear phonocardiographic Signal Processing." Doctoral thesis, Linköpings universitet, Fysiologisk mätteknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11302.
Повний текст джерелаHákonardóttir, Stefanía. "Prosthetic Control using Implanted Electrode Signals." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-147699.
Повний текст джерелаКниги з теми "PCG signals"
Shukla, Vikas. Signal integrity for PCB designers. Attleboro, MA: Reference Designer Inc., 2009.
Знайти повний текст джерелаIntroduction to PCM telemetering systems. 2nd ed. Boca Raton: CRC Press, 2002.
Знайти повний текст джерелаIntroduction to PCM telemetering systems. Boca Raton: CRC Press, 1993.
Знайти повний текст джерелаMaes, Dominiek, Marina Sibila, and Maria Pieters, eds. Mycoplasmas in swine. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249941.0000.
Повний текст джерелаButkov, Nic. Polysomnography. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.
Повний текст джерелаMoss, Joel. Adp-Ribosylating Toxins and G Proteins: Insights into Signal Transduction (Pco-017-9). Amer Society for Microbiology, 1990.
Знайти повний текст джерелаRavelo, Blaise, and Zhifei Xu, eds. Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/pbcs072e.
Повний текст джерелаXplore, IEEE. Icics-Pcm 2003: Proceedings of the 2003 Joint Conference of the Fourth International Conference on Information, Communications & Signa. Institute of Electrical & Electronics Enginee, 2003.
Знайти повний текст джерелаTavares, Hermano. Assessment and Treatment of Pathological Gambling. Edited by Jon E. Grant and Marc N. Potenza. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195389715.013.0091.
Повний текст джерелаAmbiguity Function Analysis and Direct-Path Signal Filtering of the Digital Audio Broadcast (DAB) Waveform for Passive Coherent Location (PCL). Storming Media, 2002.
Знайти повний текст джерелаЧастини книг з теми "PCG signals"
Sattar, F., F. Jin, A. Moukadem, C. Brandt, and A. Dieterlen. "Time-Scale-Based Segmentation for Degraded PCG Signals Using NMF." In Signals and Communication Technology, 179–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48331-2_8.
Повний текст джерелаAlmanifi, Omair Rashed Abdulwareth, Mohd Azraai Mohd Razman, Rabiu Muazu Musa, Ahmad Fakhri Ab. Nasir, Muhammad Yusri Ismail, and Anwar P. P. Abdul Majeed. "The Classification of Heartbeat PCG Signals via Transfer Learning." In Lecture Notes in Electrical Engineering, 49–59. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4597-3_5.
Повний текст джерелаAthreya, A. Manoj, K. Paramesha, H. S. Avani, Pooja, and S. Madhu. "Neural Networks for Detecting Cardiac Arrhythmia from PCG Signals." In Studies in Autonomic, Data-driven and Industrial Computing, 103–15. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7771-7_9.
Повний текст джерелаRuffo, M., M. Romano, M. Cesarelli, P. Bifulco, A. Fratini, G. Pasquariello, M. Iaccarino, and S. Iaccarino. "Comparison of software developed for FHR extraction from PCG signals." In IFMBE Proceedings, 946–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_252.
Повний текст джерелаMubarak, Qurat-ul-ain, Muhammad Usman Akram, Arslan Shaukat, and Aneeqa Ramazan. "Quality Assessment and Classification of Heart Sounds Using PCG Signals." In Applications of Intelligent Technologies in Healthcare, 1–11. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96139-2_1.
Повний текст джерелаGelpud, John, Silvia Castillo, Mario Jojoa, Begonya Garcia-Zapirain, Wilson Achicanoy, and David Rodrigo. "Deep Learning for Heart Sounds Classification Using Scalograms and Automatic Segmentation of PCG Signals." In Advances in Computational Intelligence, 583–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85030-2_48.
Повний текст джерелаElgendi, Mohamed. "Photoplethysmogram Signals." In PPG Signal Analysis, 27–52. Boca Raton : Taylor & Francis, [2018]: CRC Press, 2020. http://dx.doi.org/10.1201/9780429449581-2.
Повний текст джерелаGergely, S., M. N. Roman, and R. V. Ciupa. "Portable Complex PCG Signal Analyzer." In IFMBE Proceedings, 140–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22586-4_29.
Повний текст джерелаElgendi, Mohamed. "Visualization of PPG Signals." In PPG Signal Analysis, 53–71. Boca Raton : Taylor & Francis, [2018]: CRC Press, 2020. http://dx.doi.org/10.1201/9780429449581-3.
Повний текст джерелаElgendi, Mohamed. "Pre-processing of PPG Signals." In PPG Signal Analysis, 73–96. Boca Raton : Taylor & Francis, [2018]: CRC Press, 2020. http://dx.doi.org/10.1201/9780429449581-4.
Повний текст джерелаТези доповідей конференцій з теми "PCG signals"
Baala Sundaram, Divaakar Siva, Anoushka Kapoor, Jackie Xie, Natalie C. Xu, Prissha Krishna Moorthy, Rogith Balasubramani, Suganti Shivaram, Anjani Muthyala, and Shivaram Poigai Arunachalam. "Comparison of Multiscale Frequency Characteristics of Normal Phonocardiogram With Diseased Heart States." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9090.
Повний текст джерелаBashar, Md Khayrul, Samarendra Dandapat, and Itsuo Kumazawa. "Heart Abnormality Classification Using Phonocardiogram (PCG) Signals." In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2018. http://dx.doi.org/10.1109/iecbes.2018.8626627.
Повний текст джерелаSingh, Jang Bahadur, and Parveen Kumar Lehana. "Separation of PCG signal from Mixture of Speech and PCG Signals with Genetic Algorithm-Based Filter Banks." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474161.
Повний текст джерелаSiva Baala Sundaram, Divaakar, Anjani Muthyala, Rogith Balasubramani, Suganti Shivaram, Susan Karki, and Shivaram Poigai Arunachalam. "Profiling Multiscale Frequency State of Normal Phonocardiogram: Feasibility Study." In 2019 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dmd2019-3301.
Повний текст джерелаBhoi, Akash Kumar, Karma Sonam Sherpa, Jitendra Singh Tamang, Devakishore Phurailatpam, and Akhilesh Kumar Gupta. "Real time acquisition and analysis of PCG and PPG signals." In 2015 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2015. http://dx.doi.org/10.1109/iccsp.2015.7322558.
Повний текст джерелаH. Oliveira, J., V. Ferreira, and M. Coimbra. "Can We Find Deterministic Signatures in ECG and PCG Signals?" In International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005205201840189.
Повний текст джерелаFidan, Ugur, Naim Karasekreter, and Gulin Utebay. "Real time wireless monitoring of Ecg and Pcg signals at computer." In 2010 15th National Biomedical Engineering Meeting (BIYOMUT 2010). IEEE, 2010. http://dx.doi.org/10.1109/biyomut.2010.5479827.
Повний текст джерелаVarshney, Shivam, and Satyendra Singh. "Murmur Detection in PCG signals using DWT Entropy and Feature Clustering." In 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2020. http://dx.doi.org/10.1109/conecct50063.2020.9198548.
Повний текст джерелаManikandan, M. Sabarimalai, and S. Dandapat. "Wavelet-Based ECG and PCG Signals Compression Technique for Mobile Telemedicine." In 15th International Conference on Advanced Computing and Communications (ADCOM 2007). IEEE, 2007. http://dx.doi.org/10.1109/adcom.2007.29.
Повний текст джерелаPrasad, G. Venkata Hari, and P. Rajesh Kumar. "Performance analysis of feature selection methods for feature extracted PCG signals." In 2015 13th International Conference on Electromagnetic Interference and Compatibility (INCEMIC). IEEE, 2015. http://dx.doi.org/10.1109/incemic.2015.8055885.
Повний текст джерелаЗвіти організацій з теми "PCG signals"
Coplin, David L., Shulamit Manulis, and Isaac Barash. roles Hrp-dependent effector proteins and hrp gene regulation as determinants of virulence and host-specificity in Erwinia stewartii and E. herbicola pvs. gypsophilae and betae. United States Department of Agriculture, June 2005. http://dx.doi.org/10.32747/2005.7587216.bard.
Повний текст джерелаVaidyanathan, P. P., and Jamal Tuqan. Oversampling PCM Techniques and Optimum Noise Shapers for Quantizing a Class of Nonbandlimited Signals,. Fort Belvoir, VA: Defense Technical Information Center, December 1996. http://dx.doi.org/10.21236/ada323685.
Повний текст джерелаBarron, Elizabeth A. Training HBCU Faculty and Students in Prostate Cancer (PC) Research: Signal Transduction and Receptor-Inhibitor in the Progress of PC. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada446889.
Повний текст джерелаWiese, Thomas E., and R. B. Klassen. Training HBCU Faculty and Students in Prostate Cancer (PC) Research: Signal Transduction and Receptor-Inhibitor Interactions in the Progress of PC. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada486576.
Повний текст джерелаWiese, Thomas E., and R. B. Klassen. Training HBCU Faculty and Students in Prostate Cancer (PC) Research: Signal Transduction and Receptor-Inhibitor Interactions in the Progress of PC. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada486710.
Повний текст джерелаWiese, Thomas E., and R. B. Klassen. Training HBCU Faculty and Students in Prostate Cancer (PC) Research: Signal Transduction and Receptor-Inhibitor Interactions in the Progress of PC. Addendum. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada512650.
Повний текст джерелаFluhr, Robert, and Maor Bar-Peled. Novel Lectin Controls Wound-responses in Arabidopsis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7697123.bard.
Повний текст джерелаFunkenstein, Bruria, and Shaojun (Jim) Du. Interactions Between the GH-IGF axis and Myostatin in Regulating Muscle Growth in Sparus aurata. United States Department of Agriculture, March 2009. http://dx.doi.org/10.32747/2009.7696530.bard.
Повний текст джерелаOr, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.
Повний текст джерела