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Hehir, Colin, e Alan F. Smeaton. "Calculating the matrix profile from noisy data". PLOS ONE 18, n.º 6 (15 de junho de 2023): e0286763. http://dx.doi.org/10.1371/journal.pone.0286763.
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The matrix profile (MP) is a data structure computed from a time series which encodes the data required to locate motifs and discords, corresponding to recurring patterns and outliers respectively. When the time series contains noisy data then the conventional approach is to pre-filter it in order to remove noise but this cannot apply in unsupervised settings where patterns and outliers are not annotated. The resilience of the algorithm used to generate the MP when faced with noisy data remains unknown. We measure the similarities between the MP from original time series data with MPs generated from the same data with noisy data added under a range of parameter settings including adding duplicates and adding irrelevant data. We use three real world data sets drawn from diverse domains for these experiments Based on dissimilarities between the MPs, our results suggest that MP generation is resilient to a small amount of noise being introduced into the data but as the amount of noise increases this reslience disappears.
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Cartwright, Eoin, Martin Crane e Heather J. Ruskin. "Financial Time Series: Market Analysis Techniques Based on Matrix Profiles †". Engineering Proceedings 5, n.º 1 (16 de julho de 2021): 45. http://dx.doi.org/10.3390/engproc2021005045.
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The Matrix Profile (MP) algorithm has the potential to revolutionise many areas of data analysis. In this article, several applications to financial time series are examined. Several approaches for the identification of similar behaviour patterns (or motifs) are proposed, illustrated, and the results discussed. While the MP is primarily designed for single series analysis, it can also be applied to multi-variate financial series. It still permits the initial identification of time periods with indicatively similar behaviour across individual market sectors and indexes, together with the assessment of wider applications, such as general market behaviour in times of financial crisis. In short, the MP algorithm offers considerable potential for detailed analysis, not only in terms of motif identification in financial time series, but also in terms of exploring the nature of underlying events.
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Ghazvini, Mina Bagherzade, Miquel Sànchez-Marrè, Davood Naderi e Cecilio Angulo. "Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile". Energies 17, n.º 3 (30 de janeiro de 2024): 653. http://dx.doi.org/10.3390/en17030653.
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Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures.
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Lakshmi, G. V. Vidya, e S. Gopikrishnan. "IMD-MP: Imputation of Missing Data in IoT Based on Matrix Profile and Spatio-temporal Correlations". JUCS - Journal of Universal Computer Science 30, n.º 6 (28 de junho de 2024): 814–46. http://dx.doi.org/10.3897/jucs.105363.
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Data in the Internet of Things (IoT) domain may be missing due to connectivity errors, environmental extremes, sensor malfunctions, and human errors. Despite the many approaches for imputing missing values, the most significant difficulty in terms of imputation precision or compute complexity for larger missing sub-sequences in uni-variate series is still being explored. This work introduced IMD-MP (Imputation of Missing Data using Matrix Profile), a new technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations using a novel distance metric Matrix Profile Distance (MPD). Our method preserves spatial correlation by grouping the sensors present in the network (using grouping algorithm-GA) to impute the missing data of the failed sensor node. After grouping, similar sensor nodes to the failed sensor node are identified using the Node Similarity Algorithm (NSF). From its similar sensor data, a certain number of sub-sequences that are most similar to the one preceding the failed node’s missing values are gathered. These sub-sequences heights are optimized to ensure temporal correlation in the imputed data. To find the optimal imputation sequence, the current research uses MPD and similarity scores. Numerical findings using sensor data from real-time environmental mon-itoring and Intel data sets demonstrate the algorithm’s effectiveness compared to other benchmarks.
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Birsan, Magdalena, Nela Bibire, Alina Diana Panainte, Oana Silasi, Paula Antonoaea, Adriana Ciurba, Ana Caterina Cristofor, Magdalena Wroblewska e Katarzyna Sosnowska. "The Influence of the Preparation Method on the Characteristics of a New Cosmetic Gel Based on Hyaluronic Acid and Matrix-Forming Polymers". Materiale Plastice 57, n.º 2 (1 de julho de 2019): 123–30. http://dx.doi.org/10.37358/mp.20.2.5358.
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Because hyaluronic acid (HA) cosmetic gels are the most commonly used gels in the cosmetic industry, the purpose of this study was to develop a new gel formulation of HA in carboxymethyl cellulose sodium (Carmellose, CMC Na), prepared in three different ways and to characterize the gel obtained in terms of texture, pH, thixotropy and decide which preparation method is optimal for obtaining a cosmetic gel. The gel formulations were prepared by dispersing CMC Na in water and glycerol and mixing it in three different ways with HA (at the same time, after gelling and 24 h after gel preparation). The pH, rheological properties and texture of hydrogels were evaluated. The study demonstrated that the formulation prepared with CMC Na has higher viscosity and stability at a pH = 6-9. The viscosity depends on the preparation method of hydrogels, the highest values of the mechanical parameters were recorded in the formulation in which CMC Na and HA were added at the same time. The present study showed that the difference on the texture was realised by the used preparation method. In conclusion the preparation method of a hydrogel with CMC Na 4% and 1% HA has a significant influence on the texture profile and the viscosity characteristics.
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Pavaloiu, Ramona-Daniela, Fawzia Sha At, Mihaela Eremia, Cristina Hlevca, Maria Petrescu, Mousa Sha�at e Claudia Sevcenco. "Formulation and Characterisation of Pullulan Acetate Nanoparticles Loaded with 5-Fluorouracil". Materiale Plastice 59, n.º 2 (1 de julho de 2022): 138–44. http://dx.doi.org/10.37358/mp.22.2.5593.
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This study was geared to analyze the preparation methods of pullulan acetate-based nanoparticles loaded with 5-fluorouracil, as well as the potential of pullulan as a biopolymer matrix for obtaining nanoparticles applied in the delivery of anticancer drugs (5-FU). Various methods were used to produce pullulan acetate-based nanoparticles loaded with 5-FU, including nanoprecipitation, modified nanoprecipitation, and double emulsion. Pullulan was previously chemically modified with acetic anhydride, dimethylformamide and pyridine, and yielded pullulan acetate. Pullulan was made using the Aureobasidium pullulans strain through a fermentation procedure. UV-Vis Spectro-photometric and dynamic light scattering (DLS) methods were used to assess entrapment effectiveness, size, and polydispersity index (PDI) of pullulan acetate-based nanoparticles loaded with 5-FU. Based on the properties of the nanoparticles obtained, the optimum preparation method was chosen. The maximum entrapment efficiency was found in pullulan acetate nanoparticles loaded with 5-FU generated by a double emulsion method. The mean hydrodynamic size and PDI of all nanoparticles were adequate. The best formulation showed faster 5-FU release profile in acid phosphate-buffered saline (pH 5) than in phosphate-buffered saline pH 7.4. According to the findings, pullulan derivatives have a great potential for producing nanoparticles that might be used to deliver anticancer medicines.
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Kasthuri, Raj S., Jessica L. Hysjulien, Laura E. Norton, Tyson Rogers, Bernd Jilma, Nigel S. Key e Gary L. Nelsestuen. "Plasma Proteome Profiling: Widespread Oxidative Changes in Endotoxemia Correlate with Intravascular Tissue Factor Response." Blood 104, n.º 11 (16 de novembro de 2004): 2388. http://dx.doi.org/10.1182/blood.v104.11.2388.2388.
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Abstract The initial response to systemic infection in sepsis syndromes is mediated by the innate immune system and results in widespread activation of inflammation and coagulation. Some of these changes are recapitulated in the human Endotoxemia model. Proteomics represents a novel approach to study the pathophysiology of disease processes, and offers the potential for identification of biomarkers that may be used for diagnostic and/or prognostic purposes. Therefore, plasma proteome profiling was performed to study the response to intravenously administered low-dose lipopolysaccharide (LPS; 2ng/kg) in 28 healthy adult male volunteers. Interleukin 6 (IL 6), which mediates the host inflammatory responses in endotoxemia and sepsis, and circulating tissue factor (TF), which initiates coagulation, were also measured as markers of these respective processes. Plasma was obtained at baseline and at 1, 2, 3, 4, 8, and 24 hours following LPS administration. Protein profiling was performed by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry. All individuals showed qualitative profile changes that peaked at 8 hours after LPS and reverted towards baseline at 24 hours. The spectra obtained consisted of 15 non-redundant peaks that were seen consistently in all samples, and ratios of these peaks were used in data analysis. These peaks consisted of Apolipoproteins CI, CII and CIII, and Transthyretin (TTr) and their various isoforms. Serum Amyloid A appeared at later time points following endotoxin. Six of the 28 subjects demonstrated severe oxidation of plasma proteins detected by the loss of free sulfhydryls of TTr and addition of oxygen to many proteins. Increased proteolysis of plasma proteins was also noted in these subjects, along with the appearance of new protein peaks. These 6 individuals were designated ‘high responders’. Microparticle (MP)-associated TF procoagulant activity (PCA) was measured using a recently described assay [Blood 2004 103: 4545]. The TF PCA peaked between 3-4 hours following endotoxin challenge with a 10 fold increase, on average, compared to baseline (p < 0.001). The mean increase in MP-associated TF PCA at 4 hours after LPS was significantly higher among those subjects classified as high responders by protein profiling (p < 0.05). IL 6 levels, measured in 24 of the 28 subjects, peaked at 2–3 hours following LPS with a mean increase > 175 fold compared to baseline (p < 0.01). This study demonstrates significant increases in the MP-associated TF PCA and IL 6 levels in all subjects. The increase in TF PCA was significantly higher among those with extreme changes in their proteome profile despite the small number of individuals in this category. The magnitude of changes observed among the high responder subset supports the hypothesis that some individuals have a predisposition to increased responsiveness to endotoxin. This response may reflect either a protective effect or an increased susceptibility. Further, based on the findings of this study, this phenomenon can be rapidly determined by a novel technique using a proteomics approach. This approach offers the potential to assess early events that may precede sepsis and warrants further exploration in the clinical setting.
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Froeling, Fieke E. M., Juan Valle, Rosie Upstill-Goddard, Paul Grimwood, Paul Westwood, Stephan B. Dreyer, Caroline Kelly et al. "Abstract PR04: Therapeutic development platform for pancreatic cancer in the UK national health service: Lessons learned and initial results from Precision-Panc". Cancer Research 84, n.º 2_Supplement (16 de janeiro de 2024): PR04. http://dx.doi.org/10.1158/1538-7445.panca2023-pr04.
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Abstract Introduction Pancreatic Cancer (PC) remains almost universally lethal. Precision-Panc UK therapeutic development platform was established to overcome challenges of delivering precision medicine and accelerate drug development for PC. Methods A Master Protocol (MP) was established to screen, biopsy, and molecularly profile patients for subsequent enrolment into multiple PRIMUS clinical trials. Research patient and tissue pathways were incorporated into routine clinical practice for molecular profiling using a bespoke Clinical Cancer Genome assay utilising targeted capture next generation sequencing technology. Whole transcriptome is performed using BioClavis Temp-O-Seq technology. In addition, tumor NGS of selected cases are discussed in the virtual Precision-Panc Molecular Tumor Board for collective interpretation of results, and to support of clinical decision making, trial enrolment and precision medicine. Results (updated data to be presented if selected for presentation) To date, 670 patients have been screened for Precision-Panc, 567 patients were registered on the MP, and 336 enrolled in PRIMUS trials. Main reasons for attrition were decline in performance status, death, alternative diagnosis, or patient declined active treatment or PRIMUS trials. Tissue biopsies were obtained either by endoscopic ultrasound or interventional radiology as part of diagnostic pathway. Sixty five of the 483 samples submitted to the sequencing laboratory contained insufficient material (13% upstream sample fail). Of the 418 samples sequenced, a clinical grade report could be issued for 393 (94%) samples (6% failed sequencing/analysis QC matrix). Most patients carried KRAS G12D mutations (48%), followed by G12V (30%), G12R (12%), Q61H (6%) and G12C (1%), leading to different downstream signalling on transcriptomic pathway analysis. KRAS wildtype was seen in 35 cases (8.9%). Homologous recombination deficiency genomic scar was seen in ~10% (biomarker for the PRIMUS-001 and -002 trials, evaluating FOLFOX-A versus AG as first-line treatment for metastatic PC or in the neoadjuvant setting). Somatic driver variants in RNF43 were detected in 25 patients (7%) (PRIMUS-007, 2nd line trial of porcupine inhibitor RXC004 in RNF43 mutated advanced PC). Four patients (1.2%) were found to be MSI high and 32 (9.3%) had a TMB≥4mutations/Mb (eligible for PRIMUS-008; Pembrolizumab/Olaparib in patients with high TMB). Transcriptomic profiling confirmed previously described molecular subtypes with a relatively higher percentage of squamous/basal-like cancers in patients who rapidly deteriorated and dropped off the treatment pathway. Conclusion We present the initial experience and real-world challenges in longitudinal fashion in delivering precision medicine for PC, with attrition in both patient and tissue pathways. The majority of samples were successfully sequenced, and research activities were embedded in routine clinical practice, enabling easier trial enrolment, therapeutic and biomarker development for PC. Citation Format: Fieke E.M. Froeling, Juan Valle, Rosie Upstill-Goddard, Paul Grimwood, Paul Westwood, Stephan B. Dreyer, Caroline Kelly, Fraser Duthie, Judith Dixon, Sarah Bradley, T.R. Jeffry Evans, Owen J. Sansom, Andrew V. Biankin, David K. Chang. Therapeutic development platform for pancreatic cancer in the UK national health service: Lessons learned and initial results from Precision-Panc [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr PR04.
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Shi, J., S. X. Zhang, J. Qiao, S. Song, R. Zhao, P. Zhao, G. Y. Liu, J. Luo, P. F. He e X. Li. "POS0353 DEEP PHENOTYPING OF PSORIASIS BASED ON LIPID METABOLISM RELATED GENES BY INTEGRATIVE SYSTEMS ANALYSIS". Annals of the Rheumatic Diseases 80, Suppl 1 (19 de maio de 2021): 407.1–407. http://dx.doi.org/10.1136/annrheumdis-2021-eular.2073.
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Background:Psoriasis is an immune-mediated inflammatory, genetic disease with numerous concomitant metabolic disorders1. Patients with psoriasis experience substantial morbidity and increased rates of cardiometabolic diseases2. Among the metabolic disorders, lipid disturbances are recognized as a very important part in the pathogenesis of psoriasis and psoriasis-associated cardiometabolic diseases3 4.Objectives:To characterize the molecular features of psoriasis by the development of a classification system that was based on the gene expression profile of lipid metabolism related genes.Methods:We performed molecular subtyping and association analysis of psoriasis from Gene Expression Omnibus (GEO). The training sets includes GSE13355 cohort (N = 58), GSE14905 (N = 28) cohort and GSE30999 cohort (N = 81), while the GSE54456 (N = 92) was selected as the validation set. Energy metabolism-related genes were collected from MSigDB (version 7.0; http://software.broadinstitute.org/gsea/msigdb/). Based on these psoriasis lipid metabolism-related genes, we conducted consensus molecular subtyping with nonnegative matrix factorization (NMF). Finally, Gene set variation analysis (GSVA) was used to score individual subtypes against the metabolism-related gene sets, and each group got a GSVA index.Results:Total 104 lipid metabolism-related genes were clustered into three subgroups (Figure 1A). GSVA revealed the lipid metabolism with the correlation in subtypes. There was no statistical difference was identified between the subtype 1 and subtype 2(P > 0.05, Figure 1B), so we merged the two types as a new Sub1, another subtype was named as the new Sub2 (Figure 1C). We finally identified 2 distinct subtypes of psoriasis in GEO cohort that were characterized by significantly different lipid metabolic gene score. Sub1 was featured by higher metabolic gene score compared with that of Sub2(P < 0.001; Figure 1D). The lipid metabolic subtype classification was successfully validated in another independent patient cohorts (GSE54456).Conclusion:This study established a new classification based on the gene expression profiles of lipid metabolic genes, thereby furthering the understanding of the pathogenesis and psoriasis-associated diseases.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Armstrong AW, Read C. Pathophysiology, Clinical Presentation, and Treatment of Psoriasis: A Review. JAMA 2020;323(19):1945-60. doi: 10.1001/jama.2020.4006 [published Online First: 2020/05/20].[3]Goolam Mahyoodeen N, Crowther NJ, Pillay L, et al. Relationship of Visceral Fat and Adipokines with Cardiometabolic Diseases in Psoriasis. Acta Derm Venereol 2019;99(13):1218-23. doi: 10.2340/00015555-3327 [published Online First: 2019/10/04].[4]Pietrzak A, Michalak-Stoma A, Chodorowska G, et al. Lipid disturbances in psoriasis: an update. Mediators Inflamm 2010;2010 doi: 10.1155/2010/535612 [published Online First: 2010/08/14].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared
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Ghebaur, Adi, Sorina Alexandra Garea, Sergiu Cecoltan e Horia Iovu. "Development and Characterization of Novel Freeze-thawed Polyvinyl Alcohol/ Halloysite Hydrogels. An approach for drug delivery application". Materiale Plastice 54, n.º 1 (30 de março de 2017): 8–13. http://dx.doi.org/10.37358/mp.17.1.4774.
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The influence of aluminosilicates on the structure and drug release profiles of polyvinyl alcohol (PVA) - halloysite (HNT) hydrogels containing acetylsalicylic acid (ASA) as a model drug was monitored. The hydrogels were synthesized using a three cycle freeze - thawing procedure and were characterized by FTIR, XRD and SEM. The swelling degree and cytotoxicity were also determined. All hydrogels properties were influenced by HNT concentration from the polymer matrix. The release of ASA, from PVA - HNT hydrogels was monitored in the gastrointestinal tract conditions.
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Wang, Qian, Yaning Zhang, Bing Yue, Yu Zhang, Changchuan Jiang, Stuthi Perimbeti e Michael J. Mauro. "Cardiovascular Mortality Among CML Patients in the Pre-TKI and TKI Eras: A SEER Analysis 1992-2004". Blood 132, Supplement 1 (29 de novembro de 2018): 1737. http://dx.doi.org/10.1182/blood-2018-99-110408.
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Abstract Background: Since the US Food and Drug Administration (FDA) approved the first BCR-ABL1-targeted tyrosine kinase inhibitor (TKI) imatinib in 2002 as the first line therapy1, chronic phase chronic myeloid leukemia (CML-CP), once a uniformly fatal disease, now has become a disease that is controlled in >90% patients2. Conventional treatment for CML before TKIs included hydroxyurea, interferon alpha with or without cytarabine, busulfan, as well as stem cell transplantation3. Currently, there are five TKIs approved by FDA for chronic-phase CML treatment: imatinib, nilotinib, dasatinib, bosutinib, and ponatinib4.Studies have suggested that CML patients have higher baseline cardiovascular comorbidities than general US population5. Moreover, TKIs, especially later generation, are associated with cardiovascular toxicities including myocardial infarction, ischemic heart disease, cerebrovascular disease, peripheral arterial disease, QT prolongation, hypertension, and venous thrombosis1,4,6-9. As patients may need life-long (or minimum of several years') therapy, the cardiovascular safety profile of TKI treatment warrants utmost attention. Thus, we examined the effect of TKIs on cardiovascular mortality using the SEER database. Methods: Data were extracted from the Surveillance, Epidemiology and End Results (SEER) 18 program 1992-2004. Cardiovascular diseases were defined as diseases of the heart, hypertension, cerebrovascular disease, atherosclerosis, aortic aneurysm and dissection, and other disease of arteries, arterioles, and capillaries. Time of diagnosis between 1992-2002 was defined as pre-TKI era and 2003-2014 as TKI era. Patients had BCR/ABL negative CML or any other cancer before CML were excluded. T-test and chi-square test were used to analyze the baseline characteristics. Multivariable Cox proportional hazards regression was used to calculate the hazard ratio (HR). Sub-distribution hazard ratios (SHRs) for cardiovascular disease (CVD) specific mortality were calculated by using Fine and Gray competing-risks model. All above statistical tests were performed assuming a 2-sided alpha of 0.05 by using SAS 9.4. CVD standard mortality ratio (SMR) and absolute excess risk (AER) were calculated by using SEER*Stat 8.3.5 software MP-SIR Matrix comparing to the total U.S. population (1969-2016 Counties). Results: A total of 10,502 patients were included (Table 1). Table 2 shows that as expected, increased age was associated with higher CVD mortality. Females had a 20% decreased risk of overall CVD mortality compared to males. Notably, those diagnosed in the TKI era had better cardiovascular outcome than the pre-TKI diagnosis, HR=0.78 (95%CI: 0.64-0.94), which was also illustrated in Figure 1. In the adjusted model (Table 3), the significantly decreased mortality in the TKI era was mostly observed at 10-year follow up and among the youngest age group 15-44 years. The 10-year cardiovascular mortality also differed by gender, especially after age 54 (Fig. 2). In the TKI era, males had a 58% increased 10-year CVD mortality (p<0.01) compared to females (Table 4). In comparison to US general population (Fig. 3), CML patients had higher cardiovascular mortality, regardless of year of diagnosis. However, the overall CVD was higher in the pre-TKI era: 1.69 (95% CI: 1.51-1.89) vs. 1.45 (95%CI: 1.24-1.69) in the TKI era. Additionally, the AERs in the TKI era across all age groups were significantly lower compared to the pre-TKI era (Figure 4). Conclusions: Though TKIs appear to be associated with increased cardiovascular toxicity, we herein report an observed decreased cardiovascular mortality among CML patients diagnosed in the TKI era relative to the pre-TKI era. One potential explanation is that TKIs might be less cardiotoxic than conventional chemotherapies such as busulfan, cytarabine, or interferon; additionally these findings could be due to overall decreased CVD mortality since 200010. While CVD mortality among CML patients improved after 2002, it was still higher than the general US population. Besides the cardiovascular toxicities of TKIs, underlying CML associated factors could also be a potential driver for increased CVD mortality11. Our results argue for aggressive management, in general, of CVD risk factors among CML patients, especially elderly and males, and further investigation into specific mechanisms, factors and predictors of risk in TKI-treated CML. Disclosures Mauro: Takeda: Consultancy; Pfizer: Consultancy; Novartis: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy.
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Shabikay Senobari, Nader, Peter M. Shearer, Gareth J. Funning, Zachary Zimmerman, Yan Zhu, Philip Brisk e Eamonn Keogh. "The Matrix Profile in Seismology: Template Matching of Everything With Everything". Journal of Geophysical Research: Solid Earth 129, n.º 2 (fevereiro de 2024). http://dx.doi.org/10.1029/2023jb027122.
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AbstractTemplate matching has proven to be an effective method for seismic event detection, but is biased toward identifying events similar to previously known events, and thus is ineffective at discovering events with non‐matching waveforms (e.g., those dissimilar to existing catalog events). In principle, this limitation can be overcome by cross‐correlating every segment (possible template) of a seismogram with every other segment to identify all similar event pairs, but doing so has been previously considered computationally infeasible for long time series. Here we describe a method, called the ‘Matrix Profile’ (MP), a “correlate everything with everything” calculation that can be efficiently and scalably computed. The MP returns the maximum value of the correlation coefficient of every sub‐window of continuous data with every other sub‐window, as well as the best‐correlated sub‐window location. Here we show how MP methods can obtain valuable results when applied to months and years of continuous seismic data in both local and global case studies. We find that the MP can identify many new events in Parkfield, California seismicity that are not contained in existing event catalogs and that it can efficiently find clusters of similar earthquakes in global seismic data. Either used by itself, or as a starting point for subsequent template matching calculations, the MP is likely to provide a useful new tool for seismology research.
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Guidotti, Riccardo, e Matteo D’Onofrio. "Matrix Profile-Based Interpretable Time Series Classifier". Frontiers in Artificial Intelligence 4 (20 de outubro de 2021). http://dx.doi.org/10.3389/frai.2021.699448.
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Time series classification (TSC) is a pervasive and transversal problem in various fields ranging from disease diagnosis to anomaly detection in finance. Unfortunately, the most effective models used by Artificial Intelligence (AI) systems for TSC are not interpretable and hide the logic of the decision process, making them unusable in sensitive domains. Recent research is focusing on explanation methods to pair with the obscure classifier to recover this weakness. However, a TSC approach that is transparent by design and is simultaneously efficient and effective is even more preferable. To this aim, we propose an interpretable TSC method based on the patterns, which is possible to extract from the Matrix Profile (MP) of the time series in the training set. A smart design of the classification procedure allows obtaining an efficient and effective transparent classifier modeled as a decision tree that expresses the reasons for the classification as the presence of discriminative subsequences. Quantitative and qualitative experimentation shows that the proposed method overcomes the state-of-the-art interpretable approaches.
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Nichiforov, Cristina, Antonio Martinez-Molina e Miltiadis Alamaniotis. "An intelligent big data analytics method for two-dimensional non-residential building energy forecasting". Intelligent Decision Technologies, 20 de novembro de 2022, 1–8. http://dx.doi.org/10.3233/idt-220212.
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The wide use of sensor and information technologies in buildings resulted in the massive generation of data related to its operation. Thus, there is a need for identifying patterns in data that they may use for the optimal operation of the buildings. The case of non-residential building characteristically provides a big volume of data whose analysis requires computational efficient methods. In this paper, we introduce a new big data analytic method that is applicable to forecasting energy demand in non-residential buildings. The goal is to make energy forecasts in a two-dimensional (2D) space defined by i) the electricity load and ii) gas demand. The proposed method combines the matrix profile (MP) method with a Long-Short Term Memory (LSTM) neural network. The combination of the above tools provides an efficient method in 2D hour ahead forecasting with the big data environment of smart buildings. In specific, with respect to mean average percentage error (MAPE) the combined MP-LSTM method provides a concurrent forecast of electricity and gas around 3% and 4%, respectively.
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Shaaban, Gamal, Maryam Khan, Lilia Ragad, Carol Norman, Abdul Kasem e Michal Uhercik. "EP.TH.177Safety profile of SurgiMend Acellular Dermal Matrix and Mentor implant assisted immediate breast reconstruction – Our experience at the Princess Royal University Hospital". British Journal of Surgery 108, Supplement_7 (1 de outubro de 2021). http://dx.doi.org/10.1093/bjs/znab309.026.
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Abstract Background ADM (Acellular Dermal Matrix) assisted implant-based immediate breast reconstruction (IBR) is a common technique worldwide. Various ADMs are available manufactured from different sources. The Surgimend ADM (fetal bovine dermis) is fenestrated and rich in type I/III collagen; it is claimed to reduce fluid accumulation and promote healing. Methods Retrospective observational analysis exploring safety profile using combination of SurgiMend MacroPorous (MP)/SurgiMend PRS and Mentor CPG/Mentor Siltex implants between January 2018 to December 2019. Patients with unilateral/bilateral IBR following Nipple sparing (NSM) or Skin sparing mastectomy (SSM) were included. The primary outcome was minor complications (seroma (>100ml on USS), hematoma, wound infection and infected seromas). Secondary outcome was major complications (flap necrosis, capsular contracture, implant explantation and Red Breast Syndrome). Results Six (16.2%) bilateral and 31 (83.8%) unilateral IBR were performed in 37 patients. Mean age was 53.78 years. No major complications were recorded. Conclusions The Surgimend ADM and Mentor implant combination demonstrate a good safety profile. Higher rates of seroma in this study could be explained by overdiagnosis and treatment at our unit, which in turn might imply as a result that no major complications were detected and no implant loss.
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Oderda, Marco, Alessandro Marquis, Luca Bertero, Giorgio Calleris, Riccardo Faletti, Marco Gatti, Luca Mangherini et al. "Histopathologic Features and Transcriptomic Signatures Do Not Solve the Issue of Magnetic Resonance Imaging‐Invisible Prostate Cancers: A Matched‐Pair Analysis". Prostate, 12 de dezembro de 2024. https://doi.org/10.1002/pros.24838.
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ABSTRACTBackgroundMultiparametric magnetic resonance imaging (mpMRI) is pivotal in prostate cancer (PCa) diagnosis, but some clinically significant (cs) PCa remain undetected. This study aims to understand the pathological and molecular basis for csPCa visibility at mpMRI.MethodsWe performed a retrospective matched‐pair cohort study, including patients undergoing radical prostatectomy (RP) for csPCa (i.e., ISUP grade group ≥ 2) from 2015 to 2020, in our tertiary‐referral center. We screened for inclusion in the “mpMRI‐invisible” cohort all consecutive men (N = 45) having a negative preoperative mpMRI. The “mpMRI‐visible” cohort was matched based on age, PSA, prostate volume, ISUP grade group. Included patients underwent radiological and pathological open‐label revisions and characterization of the tumor mRNA expression profile (analyzing 780 gene transcripts, signaling pathways, and cell‐type profiling). We compared the clinical‐pathological variables and the gene expression profile between matched pairs. The analysis was stratified according to histological characteristics and lesion diameter.ResultsWe included 34 patients (17 per cohort); mean age at RP and PSA were 70.5 years (standard deviation [SD] = 7.7), 7.1 ng/mL (SD = 3.3), respectively; 65% of men were ISUP 2. Overall, no significant differences in histopathological features, tumor diameter and location, mRNA profile, pathways, and cell‐type scores emerged between cohorts. In the stratified analysis, an upregulation of cell adhesion and motility, of extracellular matrix remodeling and of metastatic process pathways was present in specific subgroups of mpMRI‐invisible cancers.ConclusionsNo PCa pathological or gene‐expression hallmarks explaining mp‐MRI invisibility were identified. Aggressive features can be present both in mpMRI‐invisible and ‐visible tumors.