Time-dependent Data Sets Research Articles

Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice.

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of "Nano-Tumor Database", which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded "Nano-Tumor Database" and several statistical models that may help nanomedicine design in the future.

Deep learning-assisted calculation of apparent activation energy for cement-based systems incorporating microencapsulated phase change materials

Sustained efforts have been directed towards understanding the effects of microencapsulated phase change materials (MPCMs) on the microstructural and mechanical strength development of portland cement-based systems. Yet, the sensitivity of the hydration reaction kinetics of MPCM-integrated cementitious systems to temperature remains largely unexplored. This study proposes a deep learning framework to simulate the hydration process and calculate the apparent activation energy of such cementitious composites. Extensive isothermal calorimetry tests were carried out to compile a robust time-dependent dataset for developing various pertinent machine learning models. Initially, it was demonstrated that the deep neural network outperformed the gradient boosting ensemble in predicting the hydration rate and cumulative heat. Furthermore, the hydration parameters and apparent activation energy of diverse cement-based systems incorporating MPCMs were calculated based on the predictions of the deep learning model. It was found that the addition of MPCMs at 10 and 20 wt% of cement reduced the apparent activation energy by nearly 10% and 28%, respectively, indicating less sensitivity of the hydration reactions to temperature. Ultimately, the results of this study can help better understand the effects of high-temperature curing on mechanical strength development and calculate the temperature-related stresses and potential thermal cracking in hydrating concretes made with MPCM inclusions.

Widespread slowdown in thinning rates of West Antarctic ice shelves

Abstract. Antarctica's floating ice shelves modulate discharge of grounded ice into the ocean by providing a backstress. Ice shelf thinning and grounding line retreat have reduced this backstress, driving rapid drawdown of key unstable areas of the Antarctic Ice Sheet, leading to sea-level rise. If ice shelf loss continues, it may initiate irreversible glacier retreat through the marine ice sheet instability. Identification of areas undergoing significant change requires knowledge of spatial and temporal patterns in recent ice shelf loss. We used 26 years (1992–2017) of satellite-derived Antarctic ice shelf thickness, flow, and basal melt rates to construct a time-dependent dataset of ice shelf thickness and basal melt on a 3 km grid every 3 months. We used a novel data fusion approach, state-of-the-art satellite-derived velocities, and a new surface mass balance model. Our data revealed an overall pattern of thinning all around Antarctica, with a thinning slowdown starting around 2008 widespread across the Amundsen, Bellingshausen, and Wilkes sectors. We attribute this slowdown partly to modulation in external ocean forcing, altered in West Antarctica by negative feedbacks between ice shelf thinning rates and grounded ice flow, and sub-ice-shelf cavity geometry and basal melting. In agreement with earlier studies, the highest rates of ice shelf thinning are found for those ice shelves located in the Amundsen and Bellingshausen sectors. Our study reveals that over the 1992–2017 observational period the Amundsen and Bellingshausen ice shelves experienced a slight reduction in rates of basal melting, suggesting that high rates of thinning are largely a response to changes in ocean conditions that predate our satellite altimetry record, with shorter-term variability only resulting in small deviations from the long-term trend. Our work demonstrates that causal inference drawn from ice shelf thinning and basal melt rates must take into account complex feedbacks between thinning and ice advection and between ice shelf draft and basal melt rates.

A Study on OLED Cell Simulation and Detection Phases Based on the A2G Algorithm for Artificial Intelligence Application

In this study, we demonstrate the viability of applying artificial intelligence (AI) techniques to conduct inspections at the OLED cell level using simulated data. The implementation of AI technologies necessitates training data, which we addressed by generating an OLED dataset via our proprietary A2G algorithm, integrating the finite element method among concerns over data security. Our A2G algorithm is designed to produce time-dependent datasets and establish threshold conditions for the expansion of dark spots based on OLED parameters and predicted lifespan. We explored the potential integration of AI in the inspection phase, performing cell-based evaluations using three distinct convolutional neural network models. The test results yielded a promising 95% recognition rate when classifying OLED data into pass and fail categories, demonstrating the practical effectiveness of this approach. Through this research, we not only confirmed the feasibility of using simulated OLED data in place of actual data but also highlighted the potential for the automation of manual inspection processes. Furthermore, by introducing OLED defect detection models at the cell level, as opposed to the traditional panel level during inspections, we anticipate higher classification rates and improved yield. This forward-thinking approach underscores significant advancement in OLED inspection processes, indicating a potential shift in industry standards.

Development of Online-learning based Adaptive Anomaly Detection Algorithm for Monitoring Data Analysis on Caisson Type Breakwater

Most port structures are massive and data measured on them sensitively changes to the surrounding environment including sea waves, tides, wind, and other operational conditions so it might be difficult to extract and long-term monitor their own features such as natural frequencies and mode shapes. To solve this problem, an anomaly detection algorithm with online learning was developed for the analysis of monitoring data on the port structures. For this, data were first measured on a 1/50 scaled model of caisson type breakwater through hydraulic model experiments, and the characteristics of data were investigated. Then an unsupervised algorithm was developed to online detect abnormal conditions caused by the drift, which can track the reconstruction error from the principal component analysis and the Euclidean distance between original and reconstructed signals. The experimental results showed that the proposed algorithm could be successfully applied to time-dependent dataset shifts with high accuracy and automatically calculate the threshold based on the adaptive model.

Prescriptive selection of machine learning hyperparameters with applications in power markets: Retailer’s optimal trading

We present a data-driven framework for optimal scenario selection in stochastic optimization with applications in power markets. The proposed methodology relies on the existence of auxiliary information and the use of machine learning techniques to narrow the set of possible realizations (scenarios) of the variables of interest. In particular, we implement a novel validation algorithm that allows optimizing each machine learning hyperparameter to further improve the prescriptive power of the resulting set of scenarios. Supervised machine learning techniques are examined, including kNN and decision trees, and the validation process is adapted to work with time-dependent datasets. Moreover, we extend the proposed methodology to work with unsupervised techniques with promising results. We test the proposed methodology in a realistic power market application: optimal trading strategy in forward and spot markets for an electricity retailer under uncertain spot prices. The results indicate that the retailer can greatly benefit from the proposed data-driven methodology and improve its market performance. Moreover, we perform an extensive set of numerical simulations to analyze under which conditions the best machine learning hyperparameters, in terms of prescriptive performance, differ from those that provide the best predictive accuracy.

Multi-output soft sensor modeling approach for penicillin fermentation process based on features of big data

The product quality indicators of the penicillin fermentation process have multiple semantics and are interrelated. There is a complex nonlinear mapping relationship between input characteristics and multiple-output objectives, and the time dependence is strong. As a result, the prediction accuracy of existing soft sensor models is poor, and it is difficult to meet the needs of industrial sites. To solve the above problems, this paper proposes a multi-output soft sensor modeling method for the penicillin fermentation process based on big data feature analysis. In this method, the process data is divided into several batches in order, and then the data features of multivariable and time-dependent datasets are extracted according to the deep sparse self-coding neural network method to realize the effective mining of the relationship between multivariable time series factors, based on the multi-output support vector regression method, several soft sensor models for different prediction targets are established. Meanwhile, to improve the Predictive performance of the soft sensor model, the improved black hole algorithm is used to optimize the model parameters. Finally, a simulation experiment is carried out based on the simulation dataset of the penicillin fermentation process to verify the effectiveness of the proposed method.

Survey on English Entity Linking on Wikidata: Datasets and approaches

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent increase in published papers. This survey focuses on four subjects: (1) Which Wikidata Entity Linking datasets exist, how widely used are they and how are they constructed? (2) Do the characteristics of Wikidata matter for the design of Entity Linking datasets and if so, how? (3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited by existing Entity Linking approaches? This survey reveals that current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Thus, the potential for multilingual and time-dependent datasets, naturally suited for Wikidata, is not lifted. Furthermore, we show that most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality. Almost all approaches employ specific properties like labels and sometimes descriptions but ignore characteristics such as the hyper-relational structure. Hence, there is still room for improvement, for example, by including hyper-relational graph embeddings or type information. Many approaches also include information from Wikipedia, which is easily combinable with Wikidata and provides valuable textual information, which Wikidata lacks.

Disentangling polydisperse biomolecular systems by Chemometrics decomposition of SAS data.

The structural characterization of polydisperse systems consisting of multiple coexisting species or conformations is very challenging or impossible with classical approaches. As a consequence, the structural bases of relevant questions related to protein folding, transient partner recognition, conformational transitions or fibrillation remain poorly understood. Small-Angle Scattering (SAS) techniques structurally probe species present in solution in a population-weighted manner, enabling the inspection of polydisperse systems. However, decomposition of these data to derive the contribution of individual components is not straightforward and requires the acquisition of large SAS datasets and adapted mathematical tools. Here, we present a detailed procedure for the usage of the program COSMiCS for the decomposition of SAS datasets. COSMiCS adapts the popular MCR-ALS chemometrics routine to the specificities of scattering data. Through the use of multiple SAS representations, the appropriate scaling of the data and the possibility to simultaneously decompose multiple orthogonal datasets, COSMiCS efficiently disentangles mixtures and provides species-specific structural and thermodynamic/kinetic information of the process under investigation. Although exemplified for a transient biomolecular interaction, our chemometrics strategy can be applied to many other biological processes that can be straightforwardly probed in last generation SAS beamlines. Indeed, recent experimental setups, including microfluidics and stop-flow devices, coupled to fast-reading detectors can yield large concentration or time-dependent datasets that can be decomposed with COSMiCS. Importantly, as an open-source code, previously known features of the system of interest can be introduced as constraints in the optimization, producing robust solutions for biological systems of increasing complexity.

Image-based Visualization of Large Volumetric Data Using Moments.

We present a novel image-based representation to interactively visualize large and arbitrarily structured volumetric data. This image-based representation is created from a fixed view and models the scalar densities along each viewing ray. Then, any transfer function can be applied and changed interactively to visualize the data. In detail, we transform the density in each pixel to the Fourier basis and store Fourier coefficients of a bounded signal, i.e. bounded trigonometric moments. To keep this image-based representation compact, we adaptively determine the number of moments in each pixel and present a novel coding and quantization strategy. Additionally, we perform spatial and temporal interpolation of our image representation and discuss the visualization of introduced uncertainties. Moreover, we use our representation to add single scattering illumination. Lastly, we achieve accurate results even with changes in the view configuration. We evaluate our approach on two large volume datasets and a time-dependent SPH dataset.

An analytic theory of shallow networks dynamics for hinge loss classification* *This article is an updated version of: Pellegrini F and Biroli G 2020 An analytic theory of shallow networks dynamics for hinge loss classification Advances in Neural Information Processing Systems vol 33 ed H Larochelle, M Ranzato, R Hadsell, M F Balcan and H Lin (New York: Curran Associates)

Neural networks have been shown to perform incredibly well in classification tasks over structured high-dimensional datasets. However, the learning dynamics of such networks is still poorly understood. In this paper we study in detail the training dynamics of a simple type of neural network: a single hidden layer trained to perform a classification task. We show that in a suitable mean-field limit this case maps to a single-node learning problem with a time-dependent dataset determined self-consistently from the average nodes population. We specialize our theory to the prototypical case of a linearly separable data and a linear hinge loss, for which the dynamics can be explicitly solved in the infinite dataset limit. This allows us to address in a simple setting several phenomena appearing in modern networks such as slowing down of training dynamics, crossover between rich and lazy learning, and overfitting. Finally, we assess the limitations of mean-field theory by studying the case of large but finite number of nodes and of training samples.

Integrating mechanistic and deep learning models for accurately predicting the enrichment of polyhydroxyalkanoates accumulating bacteria in mixed microbial cultures

The enrichment of polyhydroxyalkanoates (PHA) accumulating bacteria (PAB) in mixed microbial cultures (MMC) is extremely difficult to be predicted and optimized. Here we demonstrate that mechanistic and deep learning models can be integrated innovatively to accurately predict the dynamic enrichment of PAB. Well-calibrated activated sludge models (ASM) of the PAB enrichment process provide time-dependent data under different operating conditions. Recurrent neural network (RNN) models are trained and tested based on the time-dependent dataset generated by ASM. The accurate prediction performance is achieved (R2 > 0.991) for three different PAB enrichment datasets by the optimized RNN model. The optimized RNN model can also predict the equilibrium concentration of PAB (R2 = 0.944) and corresponding time, which represents the end of the PAB enrichment process. This study demonstrates the strength of integrating mechanistic and deep learning models to predict long-term variations of specific microbes, helping to optimize their selection process for PHA production.

With topological data analysis, predicting stock market crashes

We are investigating the evolution of four big US stock market indexes' regular returns after the 2000 technology crash and the 2007-2009 financial crisis. Our approach is based on topological data processing (TDA). To identify and measure topological phenomena occurring in multidimensional time series, we use persistence homology. We obtain time-dependent point cloud data sets using a sliding window, which we connect a topological space for. Our research indicates that a new method of econometric analysis is offered by TDA, which complements the traditional statistical tests. The tool may be used to predict early warning signs of market declines that are inevitable.

Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them.

Time-dependent physiological data sets are often difficult to interpret objectively. Biosignals such as electromyogram, electroencephalogram, or single-neuron recordings can be interpreted using various linear and nonlinear methods. Each analysis technique aims at the explanation of different data features that might be visible or not to the naked eye. Here, we used linear decomposition based on machine learning to extract motor primitives (the time-dependent coefficients of muscle synergies) from the hindlimb electromyographic activity of mice during normal and mechanically perturbed locomotion. We set out to investigate the effects of calculation parameters and data quality on two nonlinear metrics derived from fractal analysis: the Higuchi's fractal dimension (HFD) and the Hurst exponent (H). Both HFD and H proved to be exceptionally sensitive to changes in motor primitives induced by external perturbations to locomotion. We discuss the potential pitfalls that might arise from fractal analysis by using examples based on surrogate data. We conclude giving some simple, data-driven suggestions to reduce the chance of misinterpretations when metrics such as HFD and H are applied to any biological signal containing elements of periodicity.NEW & NOTEWORTHY Despite the lack of consensus on how to perform fractal analysis of physiological time series, many studies rely on this technique. Here, we shed light on the potential pitfalls of using the Higuchi's fractal dimension and the Hurst exponent. We expose and suggest how to solve the drawbacks of such methods when applied to data from normal and perturbed locomotion by combining in vivo recordings and computational approaches.

MiRNA target identification and prediction as a function of time in gene expression data

ABSTRACT The understanding of miRNA target interactions is still limited due to conflicting data and the fact that high-quality validation of targets is a time-consuming process. Faster methods like high-throughput screens and bioinformatics predictions are employed but suffer from several problems. One of these, namely the potential occurrence of downstream (i.e. secondary) effects in high-throughput screens has been only little discussed so far. However, such effects limit usage for both the identification of interactions and for the training of bioinformatics tools. In order to analyse this problem more closely, we performed time-dependent microarray screening experiments overexpressing human miR-517a-3p, and, together with published time-dependent datasets of human miR-17-5p, miR-135b and miR-124 overexpression, we analysed the dynamics of deregulated genes. We show that the number of deregulated targets increases over time, whereas seed sequence content and performance of several miRNA target prediction algorithms actually decrease over time. Bioinformatics recognition success of validated miR-17 targets was comparable to that of data gained only 12 h post-transfection. We therefore argue that the timing of microarray experiments is of critical importance for detecting direct targets with high confidence and for the usability of these data for the training of bioinformatics prediction tools.

Topological data analysis in investment decisions

This article explores the applications of Topological Data Analysis (TDA) in the finance field, especially addressing the primordial problem of asset allocation. Firstly, we build a rationale on why TDA can be a better alternative to traditional risk indicators such as standard deviation using real data sets. We apply Takens embedding theorem to reconstruct the time series of returns in a high dimensional space. We adopt the sliding window approach to draw the time-dependent point cloud data sets and associate a topological space with them. We then apply the persistent homology to discover the topological patterns that appear in the multidimensional time series. The temporal changes in the persistence landscapes, which are the real-valued functions that encode the persistence of topological patterns, are captured via Lp norm. The time series of the Lp norms shows that it is better at measuring the dynamics of returns than the standard deviation.Inspired by our findings, we explore an application of TDA in Enhanced Indexing (EI) that aims to build a portfolio of fewer assets than that in the index to outperform the latter. We propose a two-step procedure to accomplish this task. In step one, we utilize the Lp norms of the assets to propose a filtration technique of selecting a few assets from a larger pool of assets. In step two, we propose an optimization model to construct an optimal portfolio from the class of filtered assets for EI. To test the efficiency of this enhanced algorithm, experiments are carried out on ten data sets from financial markets across the globe. Our extensive empirical analysis exhibits that the proposed strategy delivers superior performance on several measures, including excess mean returns from the benchmark index and tail reward-risk ratios than some of the existing models of EI in the literature. The proposed filtering strategy is also noted to be beneficial for both risk-seeking and risk-averse investors.

In Vivo Biokinetics of 177Lu-OPS201 in Mice and Pigs as a Model for Predicting Human Dosimetry.

Introduction 177Lu-OPS201 is a high-affinity somatostatin receptor subtype 2 antagonist for PRRT in patients with neuroendocrine tumors. The aim is to find the optimal scaling for dosimetry and to compare the biokinetics of 177Lu-OPS201 in animals and humans. Methods Data on biokinetics of 177Lu-OPS201 were analyzed in athymic nude Foxn1 nu mice (28 F, weight: 26 ± 1 g), Danish Landrace pigs (3 F-1 M, weight: 28 ± 2 kg), and patients (3 F-1 M, weight: 61 ± 17 kg) with administered activities of 0.19–0.27 MBq (mice), 97–113 MBq (pigs), and 850–1086 MBq (patients). After euthanizing mice (up to 168 h), the organ-specific activity contents (including blood) were measured. Multiple planar and SPECT/CT scans were performed until 250 h (pigs) and 72 h (patients) to quantify the uptake in the kidneys and liver. Blood samples were taken up to 23 h (patients) and 300 h (pigs). In pigs and patients, kidney protection was applied. Time-dependent uptake data sets were created for each species and organ/tissue. Biexponential fits were applied to compare the biokinetics in the kidneys, liver, and blood of each species. The time-integrated activity coefficients (TIACs) were calculated by using NUKFIT. To determine the optimal scaling, several methods (relative mass scaling, time scaling, combined mass and time scaling, and allometric scaling) were compared. Results A fast blood clearance of the compound was observed in the first phase (<56 h) for all species. In comparison with patients, pigs showed higher liver retention. Based on the direct comparison of the TIACs, an underestimation in mice (liver and kidneys) and an overestimation in pigs' kidneys compared to the patient data (kidney TIAC: mice = 1.4 h, pigs = 7.7 h, and patients = 5.8 h; liver TIAC: mice = 0.7 h, pigs = 4.1 h, and patients = 5.3 h) were observed. Most similar TIACs were obtained by applying time scaling (mice) and combined scaling (pigs) (kidney TIAC: mice = 3.9 h, pigs = 4.8 h, and patients = 5.8 h; liver TIAC: mice = 0.9 h, pigs = 4.7 h, and patients = 5.3 h). Conclusion If the organ mass ratios between the species are high, the combined mass and time scaling method is optimal to minimize the interspecies differences. The analysis of the fit functions and the TIACs shows that pigs are better mimicking human biokinetics.

A Geometrical Method for Low-Dimensional Representations of Simulations

We propose a new data analysis approach for the efficient post-processing of bundles of finite element data from numerical simulations. The approach is based on the mathematical principles of symmetry. We consider the case where simulations of an industrial product are contained in the space of surface meshes embedded in R^3. Furthermore, we assume that distance preserving transformations exist, albeit unknown, which map simulation to simulation. In this setting, a discrete Laplace-Beltrami operator can be constructed on the mesh, which is invariant to isometric transformations and therefore valid for all simulations. The eigenfunctions of such an operator are used as a common basis for all (isometric) simulations. One can use the projection coefficients instead of the full simulations for further analysis. To extend the idea of invariance, we employ a discrete Fokker-Planck operator, that in the continuous limit converges to an operator invariant to a nonlinear transformation, and use its eigendecomposition accordingly. The data analysis approach is applied to time-dependent datasets from numerical car crash simulations. One observes that only a few spectral coefficients are necessary to describe the data variability, and low dimensional structures are obtained. The eigenvectors are seen to recover different independent variation modes such as translation, rotation, or global and local deformations. An effective analysis of the data from bundles of numerical simulations is made possible, in particular an analysis for many simulations in time.

Mining data in a dynamic PRA framework

Computational, also known as Dynamic, Probabilistic Risk Assessment (PRA) methods employ system simulation codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as system failure. In contrast to Classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and sequencing of events is set by the analyst, accident progression is dictated by the system control logic and its interaction with the system temporal evolution. Due to the nature of the problem, Dynamic PRA methods can be expensive form a computational point of view since a large number of accident scenarios is simulated. Consequently, they also generate a large amount of data (database storage may be on the order of gigabytes or higher). We investigate and apply several methods and algorithms to analyze these large time-dependent data sets. The objective is to present a broad overview of methods and algorithms that can be used to improve data quality and to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers.

Topological data analysis of financial time series: Landscapes of crashes

We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007–2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a ’persistence landscapes’. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which complements the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.