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This music score was submitted for Resonate 2024: An Open Access Call for Scores by the UCLA Music Library with Bent Frequency and Jan Berry Baker.

Solar energy is an important alternative energy source, and it is essential to forecast solar power generation for efficient power management. Due to the seasonal characteristics of weather features, seasonal data partition strategies help develop prediction models that perform better in extreme weather-related situations. Most existing studies rely on fixed season partitions, such as meteorological and astronomical, where the start and end dates are specific. However, even if the countries are in the same Northern or Southern Hemisphere, seasonal changes can occur due to abnormal climates such as global warming. Therefore, we propose a novel unfixed seasonal data partition based on Symbolic Aggregate Approximation (SAX) to forecast solar power generation. Here, symbolic representations generated by SAX are used to select seasonal features and obtain seasonal criteria. We then employ two-layer stacked LSTM and combine predictions from various seasonal features and partitions through ensemble methods. The datasets used in the experiments are from real-world solar panel plants such as in Gyeongju, South Korea; and in California, USA. The results of the experiments show that the proposed methods perform better than non-partitioned or fixed-partitioned solar power generation forecasts. They outperform them by 2.2% to 3.5%; and 1.6% to 6.5% in the Gyeongju and California datasets, respectively. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) plays a critical role in the early detection of heart diseases. However, ECG signals are often contaminated with various types of noises, including baseline wander, muscle interference, powerline noise, and additional noise when transmitted over e-health care networks. These noises can hinder the accurate detection of important cardiac waveforms making traditional methods like the Pan-Tompkins complex and ineffective in the presence of external disturbances. In this study, for ECG peak detection we introduce the use of Symbolic Aggregate Approximation (SAX) which, to the best of our knowledge, has not been attempted before in ECG analysis. Our findings demonstrate that the use of SAX for ECG analysis offers a new perspective and potential for advancing cardiac waveform detection. By employing SAX in the signal processing pipeline, we can enhance the accuracy of ECG analysis and enable early detection of heart abnormalities, ultimately improving patient outcomes in e-healthcare settings. [ABSTRACT FROM AUTHOR]

Upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), patients with critical coronavirus disease 2019 (COVID-19) present with life-threatening respiratory distress, pulmonary damage, and cytokine storm. One unexplored component in COVID-19 is the neuropeptide calcitonin gene-related peptide (CGRP), which is highly abundant in the airways and could converge in multiple aspects of COVID-19-related pulmonary pathophysiology. Whether CGRP affects SARS-CoV-2 infection directly remains elusive. We show that in critical COVID-19 patients, CGRP is increased in both plasma and lungs. Importantly, CGRP pulmonary levels are elevated in early SARS-CoV-2-positive patients and restored to baseline upon subsequent viral clearance in SARS-CoV-2-negative patients. We further show that CGRP and its stable analog SAX directly inhibit infection of bronchial Calu-3 epithelial cells with SARS-CoV-2 Omicron and Alpha variants in a dose-dependent manner. Both pre- and post-infection treatments with CGRP and/or SAX are enough to block SARS-CoV-2 productive infection of Calu-3 cells. CGRP-mediated inhibition occurs via activation of the CGRP receptor and involves down-regulation of both SARS-CoV-2 entry receptors at the surface of Calu-3 cells. Together, we propose that increased pulmonary CGRP mediates beneficial viral clearance in critical COVID-19 patients by directly inhibiting SARS-CoV-2 propagation. Hence, CGRP-based interventions could be harnessed for management of COVID-19. IMPORTANCE The neuropeptide CGRP is highly abundant in the airways. Due to its immunomodulatory, vasodilatory, and anti-viral functions, CGRP could affect multiple aspects of COVID-19-related pulmonary pathophysiology. Yet, the interplay between CGRP and SARS-CoV-2 during COVID-19 remains elusive. Herein, we show that pulmonary levels of CGRP are increased in critical COVID-19 patients, at an early stage of their disease when patients are SARS-CoV-2-positive. Upon subsequent viral clearance, CGRP levels are restored to baseline in SARS-CoV-2-negative patients. We further show that pre- and post-infection treatments with CGRP directly inhibit infection of Calu-3 bronchial epithelial cells with SARS -CoV-2, via activation of the CGRP receptor leading to decreased expression of both SARS-CoV-2 entry receptors. Together, we propose that increased pulmonary CGRP is beneficial in COVID-19, as CGRP-mediated inhibition of SARS-CoV-2 infection could contribute to viral clearance in critical COVID-19 patients. Accordingly, CGRP-based formulations could be useful for COVID-19 management. [ABSTRACT FROM AUTHOR]

In the mid-1960s, bone morphogenetic proteins (BMPs) were first identified in the extracts of bone to have the remarkable ability to induce heterotopic bone. When the Drosophila gene decapentaplegic (dpp) was first identified to share sequence similarity with mammalian BMP2/BMP4 in the late-1980s, it became clear that secreted BMP ligands can mediate processes other than bone formation. Following this discovery, collaborative efforts between Drosophila geneticists and mammalian biochemists made use of the strengths of their respective model systems to identify BMP signaling components and delineate the pathway. The ability to conduct genetic modifier screens in Drosophila with relative ease was critical in identifying the intracellular signal transducers for BMP signaling and the related transforming growth factor-beta/activin signaling pathway. Such screens also revealed a host of genes that encode other core signaling components and regulators of the pathway. In this review, we provide a historical account of this exciting time of gene discovery and discuss how the field has advanced over the past 30 years. We have learned that while the core BMP pathway is quite simple, composed of 3 components (ligand, receptor, and signal transducer), behind the versatility of this pathway lies multiple layers of regulation that ensures precise tissue-specific signaling output. We provide a sampling of these discoveries and highlight many questions that remain to be answered to fully understand the complexity of BMP signaling. [ABSTRACT FROM AUTHOR]

Transferrin (Tf) is a glycoprotein that transports iron from the serum to the various organs. Several studies have highlighted that Tf can interact with metals other than Fe(III), including actinides that are chemical and radiological toxics. We propose here to report on the behavior of Th(IV) and Pu(IV) in comparison with Fe(III) upon Tf complexation. We considered UV‐Vis and IR data of the M2Tf complex (M=Fe, Th, Pu) and combined experimental EXAFS data with MD models. EXAFS data of the first M−O coordination sphere are consistent with the MD model considering 1 synergistic carbonate. Further EXAFS data analysis strongly suggests that contamination by Th/Pu colloids seems to occur upon Tf complexation, but it seems limited. SAXS data have also been recorded for all complexes and also after the addition of Deferoxamine‐B (DFOB) in the medium. The Rg values are very close for apoTf, ThTf and PuTf, but slightly larger than for holoTf. Data suggest that the structure of the protein is more ellipsoidal than spherical, with a flattened oblate form. From this data, the following order of conformation size might be considered:holoTf

Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data. The currently employed compression methods suffer from varying degrees of feature loss, leading to potential distortions in similarity measurement results. Considering the aforementioned challenges and concerns, this paper proposes a double mean representation method, SAX-DM (Symbolic Aggregate Approximation Based on Double Mean Representation), for time series data, along with a similarity measurement approach based on SAX-DM. Addressing the trade-off between compression ratio and accuracy in the improved SAX representation, SAX-DM utilizes the segment mean and the segment trend distance to represent corresponding segments of time series data. This method reduces the dimensionality of the original sequences while preserving the original features and trend information of the time series data, resulting in a unified representation of time series segments. Experimental results demonstrate that, under the same compression ratio, SAX-DM combined with its similarity measurement method achieves higher expression accuracy, balanced compression rate, and accuracy, compared to SAX-TD and SAX-BD, in over 80% of the UCR Time Series dataset. This approach improves the efficiency and precision of similarity calculation. [ABSTRACT FROM AUTHOR]

Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data. The currently employed compression methods suffer from varying degrees of feature loss, leading to potential distortions in similarity measurement results. Considering the aforementioned challenges and concerns, this paper proposes a double mean representation method, SAX-DM (Symbolic Aggregate Approximation Based on Double Mean Representation), for time series data, along with a similarity measurement approach based on SAX-DM. Addressing the trade-off between compression ratio and accuracy in the improved SAX representation, SAX-DM utilizes the segment mean and the segment trend distance to represent corresponding segments of time series data. This method reduces the dimensionality of the original sequences while preserving the original features and trend information of the time series data, resulting in a unified representation of time series segments. Experimental results demonstrate that, under the same compression ratio, SAX-DM combined with its similarity measurement method achieves higher expression accuracy, balanced compression rate, and accuracy, compared to SAX-TD and SAX-BD, in over 80% of the UCR Time Series dataset. This approach improves the efficiency and precision of similarity calculation.

Anomaly detection for sensor systems is one of the most researched topics for the Internet of Thing systems. Researchers have been attracted to machine learning classification problems that are considered the most effective techniques. The novel model is proposed by combining anomaly pattern Symbolic Aggregate Approximation (SAX), processing imbalance data and machine learning techniques for sensor anomaly detection. The advantage of anomaly patterns and machine learning leads to the the proposed model to have better performance. The proposed model consists of three phases: finding anomaly pattern features, processing imbalanced data, exploring data by machine learning model. In this paper, the main contributions with respect to previous works can be listed as follows: (i) Successful modeling the new method of SAX for time series data for finding complex and dynamic anomaly patterns. (ii) Archiving applied anomaly pattern feature into machine learning model Random Forest and hyperparameters optimisation of these model. (iii) Fitfully proposed a model combining SAX, imbalance technique, and random forest to anomaly detection. (iv) Achieving applied proposal model in automatic meter intelligence system in Vietnam. The experiential results of the proposed model have described the robustness and better performance for detecting anomalies of power meter sensors. [ABSTRACT FROM AUTHOR]

Efficient and interpretable classification of time series is an essential data mining task with many real-world applications. Recently several dictionary- and shapelet-based time series classification methods have been proposed that employ contiguous subsequences of fixed length. We extend pattern mining to efficiently enumerate long variable-length sequential patterns with gaps. Additionally, we discover patterns at multiple resolutions thereby combining cohesive sequential patterns that vary in length, duration and resolution. For time series classification we construct an embedding based on sequential pattern occurrences and learn a linear model. The discovered patterns form the basis for interpretable insight into each class of time series. The pattern-based embedding for time series classification (PETSC) supports both univariate and multivariate time series datasets of varying length subject to noise or missing data. We experimentally validate that MR-PETSC performs significantly better than baseline interpretable methods such as DTW, BOP and SAX-VSM on univariate and multivariate time series. On univariate time series, our method performs comparably to many recent methods, including BOSS, cBOSS, S-BOSS, ProximityForest and ResNET, and is only narrowly outperformed by state-of-the-art methods such as HIVE-COTE, ROCKET, TS-CHIEF and InceptionTime. Moreover, on multivariate datasets PETSC performs comparably to the current state-of-the-art such as HIVE-COTE, ROCKET, CIF and ResNET, none of which are interpretable. PETSC scales to large datasets and the total time for training and making predictions on all 85 'bake off' datasets in the UCR archive is under 3 h making it one of the fastest methods available. PETSC is particularly useful as it learns a linear model where each feature represents a sequential pattern in the time domain, which supports human oversight to ensure predictions are trustworthy and fair which is essential in financial, medical or bioinformatics applications. [ABSTRACT FROM AUTHOR]

In complex task domains, such as games, students may ex-ceed their teachers. Such tasks afford diverse means to trade-off one type of performance for another, combining task ele-ments in novel ways to yield method variations and strategydiscoveries that, if mastered, might produce large or smallleaps in performance. For the researcher interested in the de-velopment of extreme expertise in the wild, the problem posedby such tasks is “where to look” to capture the explorations,trials, errors, and successes that eventually lead to the inven-tion of superior performance. In this paper, we present severalsuccessful discoveries of methods for superior performance.For these discoveries we used Symbolic Aggregate Approx-imation as our method of identifying changepoints withinscore progressions in the venerable game of Space Fortress.By decomposing performance at these changepoints, we findpreviously unknown strategies that even the designers of thetask had not anticipated.

Background: The vasodilator neuropeptide calcitonin gene-related peptide (CGRP) plays both detrimental and protective roles in different pathologies. CGRP is also an essential component of the neuro-immune dialogue between nociceptors and mucosal immune cells. We previously discovered that CGRP is endowed with anti-viral activity and strongly inhibits human immunodeficiency virus type 1 (HIV-1) infection, by suppressing Langerhans cells (LCs)-mediated HIV-1 trans-infection in-vitro and mucosal HIV-1 transmission ex-vivo. This inhibition is mediated via activation of the CGRP receptor non-canonical NFκB/STAT4 signaling pathway that induces a variety of cooperative mechanisms. These include CGRP-mediated increase in the expression of the LC-specific pathogen recognition C-type lectin langerin and decrease in LC-T-cell conjugates formation. The clinical utility of CGRP and modalities of CGRP receptor activation, for inhibition of mucosal HIV-1 transmission, remain elusive. Methods: We tested the capacity of CGRP to inhibit HIV-1 infection in-vivo in humanized mice. We further compared the anti-HIV-1 activities of full-length native CGRP, its metabolically stable analogue SAX, and several CGRP peptide fragments containing its binding C-terminal and activating N-terminal regions. These agonists were evaluated for their capacity to inhibit LCs-mediated HIV-1 trans-infection in-vitro and mucosal HIV-1 transmission in human mucosal tissues ex-vivo. Results: A single CGRP intravaginal topical treatment of humanized mice, followed by HIV-1 vaginal challenge, transiently restricts the increase in HIV-1 plasma viral loads but maintains long-lasting higher CD4+ T-cell counts. Similarly to CGRP, SAX inhibits LCs-mediated HIV-1 trans-infection in-vitro , but with lower potency. This inhibition is mediated via CGRP receptor activation, leading to increased expression of both langerin and STAT4 in LCs. In contrast, several N-terminal and N+C-terminal bivalent CGRP peptide fragments fail to increase langerin and STAT4, and accordingly lack anti-HIV-1 activities. Finally, like CGRP, treatment of human inner foreskin tissue explants with SAX, followed by polarized inoculation with cell-associated HIV-1, completely blocks formation of LC-T-cell conjugates and HIV-1 infection of T-cells. Conclusion: Our results show that CGRP receptor activation by full-length CGRP or SAX is required for efficient inhibition of LCs-mediated mucosal HIV-1 transmission. These findings suggest that formulations containing CGRP, SAX and/or their optimized agonists/analogues could be harnessed for HIV-1 prevention. [ABSTRACT FROM AUTHOR]

Most of the literature on utility pattern mining (UPM) assumes that the particular patterns' utility in known in advance. Concurrently, in frequent pattern mining (FPM) it is assumed that all patterns take the same value. In reality, the information about the utility of patterns is not or hardly available in most cases. Moreover, the utility and frequency of the particular pattern are not directly proportional. An algorithm for estimating a generic pattern utility has been recently proposed, but the numeric results might be difficult to interpret. In particular, in datasets with many independent instances or groups. In this paper, we present an approach to generating utility bitmaps that provide visual representation of the numeric data obtained using generic pattern utility algorithm. We demonstrate validity of this approach on two datasets: PAMAP2 Physical Activity Monitoring Data Set, an open dataset from the UCI Machine Learning Repository, and an ECG dataset collected using Biopac Student Lab during Ruffier's test. For PAMAP2 dataset, utility bitmaps allow for immediate separation of various physical activities. Variation between participants are present, but do not overshadow differences between the activity types. For the ECG dataset, utility bitmaps immediately indicate age and fitness differences between the participants, even thought this information was not available to the algorithm. In both cases, partial similarity in bitmaps can be traced back to partial similarity in activities or participants generating the data. Based on these tests, the approach seems to be promising for exploratory analysis of large collections of long time series and possibly other sequential patterns such as distance series common in sports data analysis and depth series common in petroleum engineering.

BackgroundThe vasodilator neuropeptide calcitonin gene-related peptide (CGRP) plays both detrimental and protective roles in different pathologies. CGRP is also an essential component of the neuro-immune dialogue between nociceptors and mucosal immune cells. We previously discovered that CGRP is endowed with anti-viral activity and strongly inhibits human immunodeficiency virus type 1 (HIV-1) infection, by suppressing Langerhans cells (LCs)-mediated HIV-1 trans-infection in-vitro and mucosal HIV-1 transmission ex-vivo. This inhibition is mediated via activation of the CGRP receptor non-canonical NFκB/STAT4 signaling pathway that induces a variety of cooperative mechanisms. These include CGRP-mediated increase in the expression of the LC-specific pathogen recognition C-type lectin langerin and decrease in LC-T-cell conjugates formation. The clinical utility of CGRP and modalities of CGRP receptor activation, for inhibition of mucosal HIV-1 transmission, remain elusive.MethodsWe tested the capacity of CGRP to inhibit HIV-1 infection in-vivo in humanized mice. We further compared the anti-HIV-1 activities of full-length native CGRP, its metabolically stable analogue SAX, and several CGRP peptide fragments containing its binding C-terminal and activating N-terminal regions. These agonists were evaluated for their capacity to inhibit LCs-mediated HIV-1 trans-infection in-vitro and mucosal HIV-1 transmission in human mucosal tissues ex-vivo.ResultsA single CGRP intravaginal topical treatment of humanized mice, followed by HIV-1 vaginal challenge, transiently restricts the increase in HIV-1 plasma viral loads but maintains long-lasting higher CD4+ T-cell counts. Similarly to CGRP, SAX inhibits LCs-mediated HIV-1 trans-infection in-vitro, but with lower potency. This inhibition is mediated via CGRP receptor activation, leading to increased expression of both langerin and STAT4 in LCs. In contrast, several N-terminal and N+C-terminal bivalent CGRP peptide fragments fail to increase langerin and STAT4, and accordingly lack anti-HIV-1 activities. Finally, like CGRP, treatment of human inner foreskin tissue explants with SAX, followed by polarized inoculation with cell-associated HIV-1, completely blocks formation of LC-T-cell conjugates and HIV-1 infection of T-cells.ConclusionOur results show that CGRP receptor activation by full-length CGRP or SAX is required for efficient inhibition of LCs-mediated mucosal HIV-1 transmission. These findings suggest that formulations containing CGRP, SAX and/or their optimized agonists/analogues could be harnessed for HIV-1 prevention.

Sample multiplexing-based proteomic strategies rely on fractionation to improve proteome coverage. Tandem mass tag (TMT) experiments, for example, can currently accommodate up to 18 samples with proteins spanning several orders of magnitude, thus necessitating fractionation to achieve reasonable proteome coverage. Here, we present a simple yet effective peptide fractionation strategy that partitions a pooled TMT sample with a two-step elution using a strong anion-exchange (SAX) spin column prior to gradient-based basic pH reversed-phase (BPRP) fractionation. We highlight our strategy with a TMTpro18-plex experiment using nine diverse human cell lines in biological duplicate. We collected three data sets, one using only BPRP fractionation and two others of each SAX-partition followed by BPRP. The three data sets quantified a similar number of proteins and peptides, and the data highlight noticeable differences in the distribution of peptide charge and isoelectric point between the SAX partitions. The combined SAX partition data set contributed 10% more proteins and 20% more unique peptides that were not quantified by BPRP fractionation alone. In addition to this improved fractionation strategy, we provide an online resource of relative abundance profiles for over 11,000 proteins across the nine human cell lines, as well as two additional experiments using ovarian and pancreatic cancer cell lines.

Processing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX. [ABSTRACT FROM AUTHOR]

There is a need to develop more advanced tools to improve guidance on physical exercise to reduce risk of adverse events and improve benefits of exercise. Vast amounts of data are generated continuously by Personal Monitoring Devices (PMDs) from sports events, biomedical experiments, and fitness self-monitoring that may be used to guide physical exercise. Most of these data are sampled as time- or distance-series. However, the inherent high-dimensionality of exercise data is a challenge during processing. As a result, current data analysis from PMDs seldomly extends beyond aggregates.Common challanges are: • alterations in data density comparing the time- and the distance domain; • large intra and interindividual variations in the relationship between numerical data and physiological properties; • alterations in temporal statistical properties of data derived from exercise of different exercise durations.These challenges are currently unresolved leading to suboptimal analytic models. In this paper, we present algorithms and approaches to address these problems, allowing the analysis of complete PMD datasets, rather than having to rely on cumulative statistics. Our suggested approaches permit effective application of established Symbolic Aggregate Approximation modeling and newer deep learning models, such as LSTM.

Time series data are widely found in finance, health, environmental, social, mobile and other fields. A large amount of time series data has been produced due to the general use of smartphones, various sensors, RFID and other internet devices. How a time series is represented is key to the efficient and effective storage and management of time series data, as well as being very important to time series classification. Two new time series representation methods, Hexadecimal Aggregate approXimation (HAX) and Point Aggregate approXimation (PAX), are proposed in this paper. The two methods represent each segment of a time series as a transformable interval object (TIO). Then, each TIO is mapped to a spatial point located on a two-dimensional plane. Finally, the HAX maps each point to a hexadecimal digit so that a time series is converted into a hex string. The experimental results show that HAX has higher classification accuracy than Symbolic Aggregate approXimation (SAX) but a lower one than some SAX variants (SAX-TD, SAX-BD). The HAX has the same space cost as SAX but is lower than these variants. The PAX has higher classification accuracy than HAX and is extremely close to the Euclidean distance (ED) measurement; however, the space cost of PAX is generally much lower than the space cost of ED. HAX and PAX are general representation methods that can also support geoscience time series clustering, indexing and query except for classification.

The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. Prior research has mostly focused on improving the accuracy and efficiency of classifiers, with interpretability being somewhat neglected. This aspect of classifiers has become critical for many application domains and the introduction of the EU GDPR legislation in 2018 is likely to further emphasize the importance of interpretable learning algorithms. Currently, state-of-the-art classification accuracy is achieved with very complex models based on large ensembles (COTE) or deep neural networks (FCN). These approaches are not efficient with regard to either time or space, are difficult to interpret and cannot be applied to variable-length time series, requiring pre-processing of the original series to a set fixed-length. In this paper we propose new time series classification algorithms to address these gaps. Our approach is based on symbolic representations of time series, efficient sequence mining algorithms and linear classification models. Our linear models are as accurate as deep learning models but are more efficient regarding running time and memory, can work with variable-length time series and can be interpreted by highlighting the discriminative symbolic features on the original time series. We advance the state-of-the-art in time series classification by proposing new algorithms built using the following three key ideas: (1) Multiple resolutions of symbolic representations: we combine symbolic representations obtained using different parameters, rather than one fixed representation (e.g., multiple SAX representations); (2) Multiple domain representations: we combine symbolic representations in time (e.g., SAX) and frequency (e.g., SFA) domains, to be more robust across problem types; (3) Efficient navigation in a huge symbolic-words space: we extend a symbolic sequence classifier (SEQL) to work with multiple symbolic representations and use its greedy feature selection strategy to effectively filter the best features for each representation. We show that our multi-resolution multi-domain linear classifier (mtSS-SEQL+LR) achieves a similar accuracy to the state-of-the-art COTE ensemble, and to recent deep learning methods (FCN, ResNet), but uses a fraction of the time and memory required by either COTE or deep models. To further analyse the interpretability of our classifier, we present a case study on a human motion dataset collected by the authors. We discuss the accuracy, efficiency and interpretability of our proposed algorithms and release all the results, source code and data to encourage reproducibility. [ABSTRACT FROM AUTHOR]

Sleep apnea is a respiratory disorder that affects a very significant number of patients of different ages. One of the main consequences of suffering apnea is a higher likelihood of stroke onset. Sleep apnea is identified as alterations to the breathing rate of the individual while asleep, due to obstruction of the respiratory tract. These alterations produce abnormal chest movements that can be measured by using accelerometers. In recent studies, the use of simple heuristics to determine the time between inhalation and exhalation has been reported using preset thresholds to determine inhalation/exhalation events. In this study, two different approaches based on SAX Time Series representation are proposed. The first approach identifies the time intervals between respiratory events, while the second approach compiles a dictionary of normal movements while asleep. A dictionary is created for each independent posture. Abnormal movements are detected by means of the SAX distances between the shortened words. Experiments are based on the same realistic data set taken from previous studies in the literature. The results show that the small windows-based detection algorithm could be a suitable approach for simplifying the implementation and the scalability of apnea problems. Further work is needed for accurate automated setting of the thresholds. [ABSTRACT FROM AUTHOR]

The phosphorylation of proteins modulates various functions of proteins and plays an important role in the regulation of cell signaling. In recent years, label-free quantitative (LFQ) phosphoproteomics has become a powerful tool to analyze the phosphorylation of proteins within complex samples. Despite the great progress, the studies of protein phosphorylation are still limited in throughput, robustness, and reproducibility, hampering analyses that involve multiple perturbations, such as those needed to follow the dynamics of phosphoproteomes. To address these challenges, we introduce here the LFQ phosphoproteomics workflow that is based on Fe-IMAC phosphopeptide enrichment followed by strong anion exchange (SAX) and porous graphitic carbon (PGC) fractionation strategies. We applied this workflow to analyze the whole-cell phosphoproteome of the fission yeast Schizosaccharomyces pombe. Using this strategy, we identified 8353 phosphosites from which 1274 were newly identified. This provides a significant addition to the S. pombe phosphoproteome. The results of our study highlight that combining of PGC and SAX fractionation strategies substantially increases the robustness and specificity of LFQ phosphoproteomics. Overall, the presented LFQ phosphoproteomics workflow opens the door for studies that would get better insight into the complexity of the protein kinase functions of the fission yeast S. pombe.

A large amount of time series data is being generated every day in a wide range of sensor application domains. The symbolic aggregate approximation (SAX) is a well-known time series representation method, which has a lower bound to Euclidean distance and may discretize continuous time series. SAX has been widely used for applications in various domains, such as mobile data management, financial investment, and shape discovery. However, the SAX representation has a limitation: Symbols are mapped from the average values of segments, but SAX does not consider the boundary distance in the segments. Different segments with similar average values may be mapped to the same symbols, and the SAX distance between them is 0. In this paper, we propose a novel representation named SAX-BD (boundary distance) by integrating the SAX distance with a weighted boundary distance. The experimental results show that SAX-BD significantly outperforms the SAX representation, ESAX representation, and SAX-TD representation.

The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced mutations of the data (i.e., data drifts). The algorithm splits the data streams into blocks and groups them in square matrices, computes the Discrete Cosine Transform (DCT), and quantizes them. The key information is contained in the upper-left part of the resulting matrix. We extract this sub-matrix, compute the modulus of its eigenvalues, and remove duplicates. The algorithm, called BEATS, is designed to tackle dynamic IoT streams, whose distribution changes over time. We implement experiments with six datasets combining real, synthetic, real-world data, and data with drifts. Compared to other segmentation methods like Symbolic Aggregate approXimation (SAX), BEATS shows significant improvements. Trying it with classification and clustering algorithms it provides efficient results. BEATS is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources. The datasets, code of the algorithm and the analysis results can be accessed publicly at: https://github.com/auroragonzalez/BEATS. [ABSTRACT FROM AUTHOR]

Fibrodysplasia ossificans progressiva (FOP) is a rare bone disease characterized by episodic events of heterotopic ossification (HO). All cases of FOP have been attributed to mutations in the ACVR1 gene that render the encoded BMP type I ALK2 receptor hypersensitive, resulting in the activation of BMP signaling, at inappropriate times in inappropriate locations. The episodic or sporadic nature of HO associated with FOP rests with the occurrence of specific ‘triggers’ that push the hypersensitive ALK2-FOP receptor into full signaling mode. Identification of these triggers and their mechanism of action is critical for preventing HO and its devastating consequences in FOP patients. Models of FOP, generated in Drosophila , are shown to activate the highly conserved BMP signaling pathway in both Drosophila cell culture and in developing tissues in vivo . The most common FOP mutation, R206H, in ALK2 and its synonymous mutation, K262H, in the orthologous Drosophila receptor Sax, abolish the ability of wild type receptors to inhibit BMP ligand-induced signaling and lead to ubiquitous pathway activation in both cases but with important differences. When expressed in Drosophila , human ALK2 R206H exhibits constitutive signaling. Sax K262H on the other hand can elicit excessive signaling similar to that observed for ALK2 R206H in mammalian systems in vivo . For example, hyperactive signaling mediated by Sax K262H is triggered by an increase in ligand or in type II receptors. Interestingly, while the constitutive nature of ALK2 R2026H in Drosophila requires activation by the type II receptor, it does not require its ligand binding domain. The differences exhibited by the two Drosophila FOP models enable a valuable comparative analysis poised to reveal critical regulatory mechanisms governing signaling output from these mutated receptors. Modifier screens using these Drosophila FOP models will be extremely valuable in identifying genes or compounds that reduce or prevent the hyperactive BMP signaling that initiates HO associated with FOP. [ABSTRACT FROM AUTHOR]

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Time series classification is the task of predicting the class label of an unclassified time series. In the era of big data, time series classification is one of the best-known grand challenges because of its many fields of application and difficulty. There are three important things that we need to consider in time series classification; representation, similarity measurement, and assignment strategy. Representation for time series is a technique that converts time series to feature vectors representing the characteristics of time series. In the last decade, Symbolic Aggregate approXimation (SAX), which is a state of- the-art feature expression for time series, has attracted the attention of many data mining researchers, because huge number of good sequence data mining algorithms are available once time series are converted to SAX sequences. In this paper, we propose a novel method for time series classification using a hybrid SAX-based symbolic representation, which is called a moving average convergence divergence (MACD)-histogrambased SAX (MHSAX) proposed in our previous work. The proposed time series classification method includes the MHSAX and a nearest neighbor (1-NN) classifier utilizing the local sequence alignment technique. To evaluate the proposed time series classification method, we implemented it and conducted experiments using all 85 data sets in the UCR Time Series Classification Archive. The experimental results show that the proposed time series classification method outperforms not only other distance-based 1-NNs, but also other state-of-theart methods. [ABSTRACT FROM AUTHOR]

• The classification of operating modes could improve the operation accuracy. • Symbolized data can discretize time series data and obtain image labels. • Both image-label data and image-text data are considered. • A novel image joint contrast method is proposed in this paper. • Application results confirm the effectiveness and reliability of the proposed method. In the industry process, the operation of the operator contains rich operating experience, which can be divided into different operating modes according to different working conditions. The traditional division of operating modes often only starts from the current state or process knowledge, lacking comprehensive data extraction and understanding, which may lead to inaccurate modal classification or modal omission. When modal classification is performed based on pictures, the classification will be inaccurate due to the missing or errors of picture labels or text information. To solve this problem, this paper proposes a new operation modality classification method based on joint image contrast (OMC-JIC). First, the time series data is symbolized by the method of symbolic approximate aggregation (SAX). The variable curve corresponding to the current symbol is used as a picture, while the variable value is stored as text information. Then the symbolic data name can be regarded as the label of the above image. Finally, we can use the operation modality classification method proposed in this paper to train and test the images with labels and text information, and final operation mode classification results are obtained. In order to further verify the effectiveness of the proposed OMC-JIC method, an industrial methanol production process is considered in this paper to test the methodology, and the proposed OMC-JIC method could receive better classification results and operational assistance comparing with other methods. [ABSTRACT FROM AUTHOR]

Drought is one of the most powerful natural disasters which are affected on different aspects of the environment. Most of the time this phenomenon is immense in the arid and semi-arid area. Monitoring and prediction the severity of the drought can be useful in the management of the natural disaster caused by drought. Many indices were used in predicting droughts such as SPI, VCI, and TVX. In this paper, based on three data sets (rainfall, NDVI, and land surface temperature) which are acquired from MODIS satellite imagery, time series of SPI, VCI, and TVX in time limited between winters 2000 to summer 2015 for the east region of Isfahan province were created. Using these indices and fusion of symbolic aggregation approximation and hidden Markov chain drought was predicted for fall 2015. For this purpose, at first, each time series was transformed into the set of quality data based on the state of drought (5 group) by using SAX algorithm then the probability matrix for the future state was created by using Markov hidden chain. The fall drought severity was predicted by fusion the probability matrix and state of drought severity in summer 2015. The prediction based on the likelihood for each state of drought includes severe drought, middle drought, normal drought, severe wet and middle wet. The analysis and experimental result from proposed algorithm show that the product of this algorithm is acceptable and the proposed algorithm is appropriate and efficient for predicting drought using remote sensor data. [ABSTRACT FROM AUTHOR]

The adsorption of iodoperfluoroarenes (IPFArs) by solid-liquid interfacial halogen bonding was investigated and preliminarily applied to solid-phase extraction (SPE). Based on 19 F NMR titration experiments, UV spectrophotometric titrations, and chemical computations, silica gel functionalized with trimethylaminopropyl chloride (SAX) groups was chosen as a strong anion-exchanging adsorbent, since the solution-phase association ability of Cl − as halogen bond acceptor is superior to that of Br − or I − . Further explorations indicated that the adsorption of IPFArs is mainly driven by halogen bonding. The adsorption isotherms of 1,4-diiodotetrafluorobenzene (1,4-DITFB) and iodoperfluorobenzene (IPFB) were fitted by the Freundlich equation, while that of 1,2-diiodotetrafluorobenzene (1,2-DITFB) was fitted by the Langmuir equation. Such alternative adsorption models can meet the requirements of different self-assembly systems based on halogen bonding. In SPE applications, the adsorption efficiency of IPFArs on SAX is as follows: 1,4-DITFB ≈ 1,2-DITFB > IPFB, with no significant adsorption of bromoperfluoroarenes. These results indicate promising applications of halogen bonding in the separation and enrichment of IPFArs. [ABSTRACT FROM AUTHOR]

Symbolic Aggregate approximation (SAX) has been the de facto standard representation methods for knowledge discovery in time series on a number of tasks and applications. So far, very little work has been done in empirically investigating the intrinsic properties and statistical mechanics in SAX words. In this paper, we applied several statistical measurements and proposed a new statistical measurement, i.e. information embedding cost (IEC) to analyze the statistical behaviors of the symbolic dynamics. Our experiments on the benchmark datasets and the clinical signals demonstrate that SAX can always reduce the complexity while preserving the core information embedded in the original time series with significant embedding efficiency, as well as robust to missing values and noise. Our proposed IEC score provide a priori to determine if SAX is adequate for specific dataset, which can be generalized to evaluate other symbolic representations. Our work provides an analytical framework with several statistical tools to analyze, evaluate and further improve the symbolic dynamics for knowledge discovery in time series. [ABSTRACT FROM AUTHOR]

Fractionation is essential to achieving deep proteome coverage for sample multiplexing experiments where currently up to 18 samples can be analyzed concurrently. However, peptide fractionation (i.e., upstream of LC-MS/MS analysis) with a liquid chromatography system constrains sample processing as only a single sample can be fractionated at once. Here, we highlight the use of spin column-based methods which permit multiple multiplexed samples to be fractionated simultaneously. These methods require only a centrifuge and eliminate the need for a dedicated liquid chromatography system. We investigate peptide fractionation with strong anion exchange (SAX) and high-pH reversed phase (HPRP) spin columns, as well as a combination of both. In two separate experiments, we acquired deep proteome coverage (>8000 quantified proteins), while starting with <25 μg of protein per channel. Our datasets showcase the proteome alterations in two human cell lines resulting from treatment with inhibitors acting on the ubiquitin-proteasome system. We recommend this spin column-based peptide fractionation strategy for high-throughput screening applications or whenever a liquid chromatograph is not readily available. Fractionation is a means to achieve deep proteome coverage for global proteomics analysis. Typical liquid chromatography systems may be a prohibitive expense for many laboratories. Here, we investigate prefractionation with strong anion exchange (SAX) and high-pH reversed phase (HPRP) spin columns, as well as a combination of both, as peptide fractionation methods. These spin columns have advantages over liquid chromatography systems, which include relative affordability, higher throughput capability, no carry over, and fewer potential instrument-related malfunctions. In two separate experiments, we acquired deep proteome coverage (>8000 quantified proteins), thereby showing the utility of each or a combination of both spin columns for global proteome analysis. [Display omitted] • Spin column-based prefractionation can be used to increase proteome depth. • SAX and HPRP spin column-based prefractionation strategies showed similar depth. • SAX and HPRP can be used in tandem to increase proteome coverage. • Deep proteome coverage (n > 8000) possible with <25 μg of protein per channel. [ABSTRACT FROM AUTHOR]