Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series

The most useful data mining primitives are distance measures. With an effective distance measure, it is possible to perform classification, clustering, anomaly detection, segmentation, etc. For single-event time series Euclidean Distance and Dynamic Time Warping distance are known to be extremely effective. However, for time series containing cyclical behaviors, the semantic meaningfulness of such comparisons is less clear. For example, on two separate days the telemetry from an athlete workout routine might be very similar. The second day may change the order in of performing push-ups and squats, adding repetitions of pull-ups, or completely omitting dumbbell curls. Any of these minor changes would defeat existing time series distance measures. Some bag-of-features methods have been proposed to address this problem, but we argue that in many cases, similarity is intimately tied to the shapes of subsequences within these longer time series. In such cases, summative features will lack discrimination ability. In this work we introduce PRCIS, which stands for Pattern Representation Comparison in Series. PRCIS is a distance measure for long time series, which exploits recent progress in our ability to summarize time series with dictionaries. We will demonstrate the utility of our ideas on diverse tasks and datasets.Comment: Accepted at IEEE ICKG 2022. (Previously entitled IEEE ICBK.) Abridged abstract as per arxiv's requirement

Multitask Learning for Time Series Data with 2D Convolution

Multitask learning (MTL) aims to develop a unified model that can handle a set of closely related tasks simultaneously. By optimizing the model across multiple tasks, MTL generally surpasses its non-MTL counterparts in terms of generalizability. Although MTL has been extensively researched in various domains such as computer vision, natural language processing, and recommendation systems, its application to time series data has received limited attention. In this paper, we investigate the application of MTL to the time series classification (TSC) problem. However, when we integrate the state-of-the-art 1D convolution-based TSC model with MTL, the performance of the TSC model actually deteriorates. By comparing the 1D convolution-based models with the Dynamic Time Warping (DTW) distance function, it appears that the underwhelming results stem from the limited expressive power of the 1D convolutional layers. To overcome this challenge, we propose a novel design for a 2D convolution-based model that enhances the model's expressiveness. Leveraging this advantage, our proposed method outperforms competing approaches on both the UCR Archive and an industrial transaction TSC dataset

Plasma and urine metabolite profiling reveals the protective effect of Clinacanthus nutans in an ovalbumin-induced anaphylaxis model: ¹H-NMR metabolomics approach

The present study sought to identify the key biomarkers and pathways involved in the induction of allergic sensitization to ovalbumin and to elucidate the potential anti-anaphylaxis property of Clinacanthus nutans (Burm. f.) Lindau water leaf extract, a Southeast Asia herb in an in vivo ovalbumin-induced active systemic anaphylaxis model evaluated by 1H-NMR metabolomics. The results revealed that carbohydrate metabolism (glucose, myo-inositol, galactarate) and lipid metabolism (glycerol, choline, sn-glycero-3-phosphocholine) are the key requisites for the induction of anaphylaxis reaction. Sensitized rats treated with 2000 mg/kg bw C. nutans extract before ovalbumin challenge showed a positive correlation with the normal group and was negatively related to the induced group. Further 1H-NMR analysis in complement with Kyoto Encyclopedia of Genes and Genomes (KEGG) reveals the protective effect of C. nutans extract against ovalbumin-induced anaphylaxis through the down-regulation of lipid metabolism (choline, sn-glycero-3-phosphocholine), carbohydrate and signal transduction system (glucose, myo-inositol, galactarate) and up-regulation of citrate cycle intermediates (citrate, 2-oxoglutarate, succinate), propanoate metabolism (1,2-propanediol), amino acid metabolism (betaine, N,N-dimethylglycine, methylguanidine, valine) and nucleotide metabolism (malonate, allantoin). In summary, this study reports for the first time, C. nutans water extract is a potential anti-anaphylactic agent and 1H-NMR metabolomics is a great alternative analytical tool to explicate the mechanism of action of anaphylaxis

Impact of Trade Liberalization on Economic Growth in Japan: Autoregressive Distributed Lag Model (ARDL)

The objective of this study is to identify the impact of trade liberalization on economic growth in Japan. Annual data are utilized from 1985 to 2016 via on Autoregressive Distributed Lag Model (ARDL) Cointegration test and Vector Error Correction Model (VECM) based Granger causality. The findings from unit root tests revealed that all the variables of mixed results whereby they are integrated at I(0) and I(1) and could proceed to the ARDL Cointegration test. Furthermore, all the variables have long-run relationships between trade openness, investment, education, inflation and economic growth in Japan. However, this study found a significant positive of trade openness and investment on economic growth in the long run. Lastly, VECM based Granger causality showed some of the causality relationships between variables in the short run for Japan

Toward a Foundation Model for Time Series Data

A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks. However, current research on time series pre-training has mostly focused on models pre-trained solely on data from a single domain, resulting in a lack of knowledge about other types of time series. However, current research on time series pre-training has predominantly focused on models trained exclusively on data from a single domain. As a result, these models possess domain-specific knowledge that may not be easily transferable to time series from other domains. In this paper, we aim to develop an effective time series foundation model by leveraging unlabeled samples from multiple domains. To achieve this, we repurposed the publicly available UCR Archive and evaluated four existing self-supervised learning-based pre-training methods, along with a novel method, on the datasets. We tested these methods using four popular neural network architectures for time series to understand how the pre-training methods interact with different network designs. Our experimental results show that pre-training improves downstream classification tasks by enhancing the convergence of the fine-tuning process. Furthermore, we found that the proposed pre-training method, when combined with the Transformer model, outperforms the alternatives

An Efficient Content-based Time Series Retrieval System

A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing. For example, users seeking to learn more about the source of a time series can submit the time series as a query to the CTSR system and retrieve a list of relevant time series with associated metadata. By analyzing the retrieved metadata, users can gather more information about the source of the time series. Because the CTSR system is required to work with time series data from diverse domains, it needs a high-capacity model to effectively measure the similarity between different time series. On top of that, the model within the CTSR system has to compute the similarity scores in an efficient manner as the users interact with the system in real-time. In this paper, we propose an effective and efficient CTSR model that outperforms alternative models, while still providing reasonable inference runtimes. To demonstrate the capability of the proposed method in solving business problems, we compare it against alternative models using our in-house transaction data. Our findings reveal that the proposed model is the most suitable solution compared to others for our transaction data problem

Dexfenfluramine and the oestrogen-metabolizing enzyme CYP1B1 in the development of pulmonary arterial hypertension

<p>Aims: Pulmonary arterial hypertension (PAH) occurs more frequently in women than men. Oestrogen and the oestrogen-metabolising enzyme cytochrome P450 1B1 (CYP1B1) play a role in the development of PAH. Anorectic drugs such as dexfenfluramine (Dfen) have been associated with the development of PAH. Dfen mediates PAH via a serotonergic mechanism and we have shown serotonin to up-regulate expression of CYP1B1 in human pulmonary artery smooth muscle cells (PASMCs). Thus here we assess the role of CYP1B1 in the development of Dfen-induced PAH.</p> <p>Methods and results: Dfen (5 mg kg−1 day−1 PO for 28 days) increased right ventricular pressure and pulmonary vascular remodelling in female mice only. Mice dosed with Dfen showed increased whole lung expression of CYP1B1 and Dfen-induced PAH was ablated in CYP1B1−/− mice. In line with this, Dfen up-regulated expression of CYP1B1 in PASMCs from PAH patients (PAH-PASMCs) and Dfen-mediated proliferation of PAH-PASMCs was ablated by pharmacological inhibition of CYP1B1. Dfen increased expression of tryptophan hydroxylase 1 (Tph1; the rate-limiting enzyme in the synthesis of serotonin) in PAH-PASMCs and both Dfen-induced proliferation and Dfen-induced up-regulation of CYP1B1 were ablated by inhibition of Tph1. 17β-Oestradiol increased expression of both Tph1 and CYP1B1 in PAH-PASMCs, and Dfen and 17β-oestradiol had synergistic effects on proliferation of PAH-PASMCs. Finally, ovariectomy protected against Dfen-induced PAH in female mice.</p> <p>Conclusion: CYP1B1 is critical in the development of Dfen-induced PAH in mice in vivo and proliferation of PAH-PASMCs in vitro. CYP1B1 may provide a novel therapeutic target for PAH.</p&gt

Selection of tropical microalgae species for mass production based on lipid and fatty acid profiles

Numerous recent studies have identified microalgae biofuel as one of the major renewable energy sources for sustainable development due to their high biomass productivity, high lipid content, and availability of locally adapted strains in various geographical locations. There have been minimal studies on the fatty acid composition of lipid production on local microalgae species in Sabah, Malaysia. Thus, screening for local microalgae species capable of producing biodiesel can aid in the selection of suitable species. This study aimed to isolate and identify promising local microalga as biodiesel feedstock for mass cultivation. Eight microalgae species, Acutodesmus obliquus, Chaetoceros muelleri, Isochrysis galbana, Ankistrodesmus falcatus, Chlamydomonas monadina, Chlorella emersonii, Nannochloropsis oculata, and Tetraselmis chuii, were successfully isolated and identified from Kota Kinabalu, Sabah. The isolated microalgae were characterized based on the lipid/biomass productivity, lipid content and fatty acid profiles. These isolates had biomass productivity of 0.11–0.78 g/L/day, lipid content of 11.69–39.00% dry weight, and lipid productivity of 21.11–252.64 mg/L/day. According to GC-MS analyses, four isolates produced more than 80% of C14–C18 fatty acids, which were A. falcatus (95%), C. emersonii (93%), A. obliquus (91%), and C. muelleri (81%). Despite its low biomass productivity, C. muelleri was chosen as the best biodiesel species candidate because of its moderately high lipid productivity (42.90 mg/L/day), highest lipid content (39% dry weight), high level of MUFAs and C14–C18 FAs (81.47%), with the highest oleic acid proportion (28.38%), all of which are desirable characteristics for producing high-quality biodiesel

Myocardial Fibrosis and Cardiac Decompensation in Aortic Stenosis

OBJECTIVES: Cardiac magnetic resonance (CMR) was used to investigate the extracellular compartment and myocardial fibrosis in patients with aortic stenosis, as well as their association with other measures of left ventricular decompensation and mortality. BACKGROUND: Progressive myocardial fibrosis drives the transition from hypertrophy to heart failure in aortic stenosis. Diffuse fibrosis is associated with extracellular volume expansion that is detectable by T1 mapping, whereas late gadolinium enhancement (LGE) detects replacement fibrosis. METHODS: In a prospective observational cohort study, 203 subjects (166 with aortic stenosis [69 years; 69% male]; 37 healthy volunteers [68 years; 65% male]) underwent comprehensive phenotypic characterization with clinical imaging and biomarker evaluation. On CMR, we quantified the total extracellular volume of the myocardium indexed to body surface area (iECV). The iECV upper limit of normal from the control group (22.5 ml/m(2)) was used to define extracellular compartment expansion. Areas of replacement mid-wall LGE were also identified. All-cause mortality was determined during 2.9 ± 0.8 years of follow up. RESULTS: iECV demonstrated a good correlation with diffuse histological fibrosis on myocardial biopsies (r = 0.87; p < 0.001; n = 11) and was increased in patients with aortic stenosis (23.6 ± 7.2 ml/m(2) vs. 16.1 ± 3.2 ml/m(2) in control subjects; p < 0.001). iECV was used together with LGE to categorize patients with normal myocardium (iECV <22.5 ml/m(2); 51% of patients), extracellular expansion (iECV ≥22.5 ml/m(2); 22%), and replacement fibrosis (presence of mid-wall LGE, 27%). There was evidence of increasing hypertrophy, myocardial injury, diastolic dysfunction, and longitudinal systolic dysfunction consistent with progressive left ventricular decompensation (all p < 0.05) across these groups. Moreover, this categorization was of prognostic value with stepwise increases in unadjusted all-cause mortality (8 deaths/1,000 patient-years vs. 36 deaths/1,000 patient-years vs. 71 deaths/1,000 patient-years, respectively; p = 0.009). CONCLUSIONS: CMR detects ventricular decompensation in aortic stenosis through the identification of myocardial extracellular expansion and replacement fibrosis. This holds major promise in tracking myocardial health in valve disease and for optimizing the timing of valve replacement. (The Role of Myocardial Fibrosis in Patients With Aortic Stenosis; NCT01755936)