Auswahl der wissenschaftlichen Literatur zum Thema „Matrix Profile (MP)“

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

Na era digital, a crescente sofisticação e variedade de ameaças cibernéticas destacam a importância de fortalecer a cibersegurança para proteger as redes atuais. Este estudo propõe uma abordagem para a detecção antecipada de ataques, utilizando a técnica Matrix Profile (MP) para analisar de forma online fluxos de dados de rede como séries temporais. Este método concentra-se na identificação de anomalias na rede como indicadores de ataques de rede, abordando as limitações dos sistemas de Aprendizado de Máquina existentes que dependem predominantemente de treinamento offline e têm dificuldades em reconhecer padrões de ataques novos ou não treinados. Nossa proposta foi avaliada em diversos cenários de ataque, demonstrando métricas de desempenho superiores quando comparado com métodos tradicionais como CUSUM, EWMA e ARIMA.