Protein phosphorylation is one of the most widespread post-translational modifications in biology
. With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine ...phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes
. For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible
. Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.
The objective is to develop a cuffless modelling approach to accurately estimate the blood pressure (BP) waveform and extract important BP features, such as the systolic BP (SBP), diastolic BP (DBP), ...and mean BP (MAP). Access to the full waveform has significant advantages over previous cuffless BP estimation tools in terms of accuracy and access to additional cardiovascular health markers (e.g., cardiac output), as well as potentially providing arterial stiffness and identifying different cardiovascular diseases. Nonlinear autoregressive models with exogenous input (NARX) are implemented using an artificial neural network to predict the BP waveforms using electrocardiography (ECG), and/or photoplethysmography (PPG) signals as inputs. The efficacy of the model is compared with a pulse arrival time (PAT) model using 15 subjects from the MIMIC II database. Two training modes are considered: training on the first eight minutes of data for each subject (Predictive training) and testing on the rest (up to 5.2 hours); and training on the first and the last eight minutes (Interval training) and testing the model in between. Predictive training and Interval training exhibited similar results initially, while Interval training resulted in higher accuracy over longer periods. The proposed method models the BP as a dynamical system leading to better accuracy in the estimation of SBP, DBP and MAP when compared to the PAT model. Moreover, the NARX model, with its ability to provide the BP waveform, yields more insight into patient health.
A substantial barrier to the clinical adoption of cuffless blood pressure (BP) monitoring techniques is the lack of unified error standards and methods of estimating measurement uncertainty. This ...study proposes a fusion approach to improve accuracy and estimate prediction interval (PI) as a proxy for uncertainty for cuffless blood BP monitoring. BP was estimated during activities of daily living using three model architectures: nonlinear autoregressive models with exogenous inputs, feedforward neural network models, and pulse arrival time models. Multiple one-class support vector machine (OCSVM) models were trained to cluster data in terms of the percentage of outliers. New BP estimates were then assigned to a cluster using the OCSVMs hyperplanes, and the PIs were estimated using the BP error standard deviation associated with different clusters. The OCSVM was used to estimate the PI for the three BP models. The three BP estimations from the models were fused using the covariance intersection fusion algorithm, which improved BP and PI estimates in comparison with individual model precision by up to 24%. The employed model fusion shows promise in estimating BP and PI for potential clinical uses. The PI indicates that about 71%, 64%, and 29% of the data collected from sitting, standing, and walking can result in high-quality BP estimates. Our PI estimator offers an effective uncertainty metric to quantify the quality of BP estimates and can minimize the risk of false diagnosis.
The objective is to develop a cuffless method that accurately estimates blood pressure (BP) during activities of daily living. User-specific nonlinear autoregressive models with exogenous inputs ...(NARX) are implemented using artificial neural networks to estimate the BP waveforms from electrocardiography and photoplethysmography signals. To broaden the range of BP in the training data, subjects followed a short procedure consisting of sitting, standing, walking, Valsalva maneuvers, and static handgrip exercises. The procedure was performed before and after a six-hour testing phase wherein five participants went about their normal daily living activities. Data were further collected at a four-month time point for two participants and again at six months for one of the two. The performance of three different NARX models was compared with three pulse arrival time (PAT) models. The NARX models demonstrate superior accuracy and correlation with "ground truth" systolic and diastolic BP measures compared to the PAT models and a clear advantage in estimating the large range of BP. Preliminary results show that the NARX models can accurately estimate BP even months apart from the training. Preliminary testing suggests that it is robust against variabilities due to sensor placement. This establishes a method for cuffless BP estimation during activities of daily living that can be used for continuous monitoring and acute hypotension and hypertension detection.
Objective: To develop and evaluate an accurate method for cuffless blood pressure (BP) estimation during moderate- and heavy-intensity exercise. Methods: Twelve participants performed three cycling ...exercises: a ramp-incremental exercise to exhaustion, and moderate and heavy pseudorandom binary sequence exercises on an electronically braked cycle ergometer over the course of 21 minutes. Subject-specific and population-based nonlinear autoregressive models with exogenous inputs (NARX) were compared with feedforward artificial neural network (ANN) models and pulse arrival time (PAT) models. Results: Population-based NARX models, (applying leave-one-subject-out cross-validation), performed better than the other models and showed good capability for estimating large changes in mean arterial pressure (MAP). The models were unable to track consistent decreases in BP during prolonged exercise caused by reduction in peripheral vascular resistance, since this information is apparently not encoded in the employed proxy physiological signals (electrocardiography and forehead PPG) used for BP estimation. Nevertheless, the population-based NARX model had an error standard deviation of 11.0 mmHg during the entire exercise window, which improved to 9.0 mmHg when the model was periodically calibrated every 7 minutes. Conclusion: Population-based NARX models can estimate BP during moderate- and heavy-intensity exercise but need periodic calibration to account for the change in vascular resistance during exertion. Significance: MAP can be continuously tracked during exercise using only wearable sensors, making monitoring exercise physiology more convenient and accessible.
Post-translational modification (PTM) serves as a regulatory mechanism for protein function, influencing their stability, interactions, activity and localization, and is critical in many signaling ...pathways. The best characterized PTM is phosphorylation, whereby a phosphate is added to an acceptor residue, most commonly serine, threonine and tyrosine in metazoans. As proteins are often phosphorylated at multiple sites, identifying those sites that are important for function is a challenging problem. Considering that any given phosphorylation site might be non-functional, prioritizing evolutionarily conserved phosphosites provides a general strategy to identify the putative functional sites. To facilitate the identification of conserved phosphosites, we generated a large-scale phosphoproteomics dataset from
embryos collected from six closely-related species. We built iProteinDB (https://www.flyrnai.org/tools/iproteindb/), a resource integrating these data with other high-throughput PTM datasets, including vertebrates, and manually curated information for
At iProteinDB, scientists can view the PTM landscape for any
protein and identify predicted functional phosphosites based on a comparative analysis of data from closely-related
species. Further, iProteinDB enables comparison of PTM data from
to that of orthologous proteins from other model organisms, including human, mouse, rat,
,
, and
.
Phosphorylation of proteins on tyrosine (Tyr) residues evolved in metazoan organisms as a mechanism of coordinating tissue growth
. Multicellular eukaryotes typically have more than 50 distinct ...protein Tyr kinases that catalyse the phosphorylation of thousands of Tyr residues throughout the proteome
. How a given Tyr kinase can phosphorylate a specific subset of proteins at unique Tyr sites is only partially understood
. Here we used combinatorial peptide arrays to profile the substrate sequence specificity of all human Tyr kinases. Globally, the Tyr kinases demonstrate considerable diversity in optimal patterns of residues surrounding the site of phosphorylation, revealing the functional organization of the human Tyr kinome by substrate motif preference. Using this information, Tyr kinases that are most compatible with phosphorylating any Tyr site can be identified. Analysis of mass spectrometry phosphoproteomic datasets using this compendium of kinase specificities accurately identifies specific Tyr kinases that are dysregulated in cells after stimulation with growth factors, treatment with anti-cancer drugs or expression of oncogenic variants. Furthermore, the topology of known Tyr signalling networks naturally emerged from a comparison of the sequence specificities of the Tyr kinases and the SH2 phosphotyrosine (pTyr)-binding domains. Finally we show that the intrinsic substrate specificity of Tyr kinases has remained fundamentally unchanged from worms to humans, suggesting that the fidelity between Tyr kinases and their protein substrate sequences has been maintained across hundreds of millions of years of evolution.
Estimation of the Blood Pressure Waveform using Electrocardiography Landry, Cederick; Peterson, Sean D.; Arami, Arash
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
07/2019, Letnik:
2019
Conference Proceeding, Journal Article
This work presents a modelling approach to accurately predict the blood pressure (BP) waveform time series from a single input signal. A nonlinear autoregressive model with exogenous input (NARX) is ...implemented using artificial neural networks and trained on Electrocardiography (ECG) signals to predict the BP waveform. The efficacy of the model is demonstrated using the MIMIC II database. The proposed method can accurately estimate systolic and diastolic BP. The NARX model together with ECG measurement allows continuous monitoring of BP, enables the estimation of other physiological measurements, such as the cardiac output, and provides more insight on the patient health condition.
As spine surgeries for decompression of the spinal cord and nerve roots trend towards minimally invasive approaches, conventional visualization methods for surgical guidance, such as ultrasound, are ...often ineffective. This is because conventional imaging probes are too large to fit down the limited access routes used in these approaches. We have developed a miniature high-resolution ultrasound phased array specifically for this application. The probe has a cross section of 4 mm by 4 mm, length of 16 cm from the handle, and an angled tip that enables imaging at a 40-degree angle from forward looking. The image window was steered between +/-32 degrees with an imaging depth of 15 mm. The array was micromachined using a picosecond laser, had 64 sub-diced elements, and an operating frequency of 30 MHz. The axial and lateral resolution were measured to be 33 μm and 116 μm, respectively, and the secondary lobes were suppressed to -60 dB. During preliminary clinical studies, patients were imaged during minimally invasive spine surgery, where the spinal cord anatomy and compression points could clearly be visualized.
This work presents a modelling approach to predict the blood pressure (BP) waveform time series during activities of daily living without the use of a traditional pressure cuff. A nonlinear ...autoregressive model with exogenous inputs (NARX) is implemented using artificial neural networks and trained to predict the BP waveform time series from electrocardiography (ECG) and forehead photoplethysmography (PPG) input signals. To broaden the range of blood pressures present in the training set, a protocol was implemented that included sitting, standing, walking, Valsalva manoeuvers, and static handgrip exercise. A five-minute interval of data in the sitting position at the end of the day was also used for training. The efficacy of the cuffless BP method for continuous BP estimation over 4.67 hours was evaluated on 3 participants for varying training data segments. A mean absolute error of 6.3 and 5.2 mmHg were achieved for systolic BP and diastolic BP estimates, respectively. Including static handgrips and Valsalva manoeuvers in the training dataset leads to better estimation of the higher ranges of BP observed throughout the day. The proposed method shows potential for estimating the range of BP experienced during activities of daily living.Clinical Relevance- Establishes a method for cuffless continuous blood pressure estimation during activities of daily living that can be used for continuous monitoring and acute hypertension detection.
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