Molecular Choreography of Acute Exercise.

Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.

Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe "Onion Clustering": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks.

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.

Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.

The transcriptional regulatory network (TRN) is a graph framework that helps understand the complex transcriptional regulation mechanisms in the transcription process. Identifying the phenotype-specific transcription regulators is vital to reveal the functional roles of transcription elements in associating the specific phenotypes. Although many methods have been developed towards detecting the phenotype-specific transcription elements based on the static TRN in the past decade, most of them are not satisfactory for elucidating the phenotype-related functional roles of transcription regulators in multiple levels, as the dynamic characteristics of transcription regulators are usually ignored in static models. In this study, we introduce a novel framework called DTGN to identify the phenotype-specific transcription factors (TFs) and pathways by constructing dynamic TRNs. We first design a graph autoencoder model to integrate the phenotype-oriented time-series gene expression data and static TRN to learn the temporal representations of genes. Then, based on the learned temporal representations of genes, we develop a statistical method to construct a series of dynamic TRNs associated with the development of specific phenotypes. Finally, we identify the phenotype-specific TFs and pathways from the constructed dynamic TRNs. Results from multiple phenotypic datasets show that the proposed DTGN framework outperforms most existing methods in identifying phenotype-specific TFs and pathways. Our framework offers a new approach to exploring the functional roles of transcription regulators that associate with specific phenotypes in a dynamic model.

Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics.

Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases, the measured time series data may be subject to limitations, including limited duration, low sampling rate, observational noise, and partial nodal state measurement. However, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality, transfer entropy, and, a machine learning-based method. Furthermore, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.

Intermittent ERK oscillations downstream of FGF in mouse embryonic stem cells.

Signal transduction networks generate characteristic dynamic activities to process extracellular signals and guide cell fate decisions such as to divide or differentiate. The differentiation of pluripotent cells is controlled by FGF/ERK signaling. However, only a few studies have addressed the dynamic activity of the FGF/ERK signaling network in pluripotent cells at high time resolution. Here, we use live cell sensors in wild-type and Fgf4-mutant mouse embryonic stem cells to measure dynamic ERK activity in single cells, for defined ligand concentrations and differentiation states. These sensors reveal pulses of ERK activity. Pulsing patterns are heterogeneous between individual cells. Consecutive pulse sequences occur more frequently than expected from simple stochastic models. Sequences become more prevalent with higher ligand concentration, but are rarer in more differentiated cells. Our results suggest that FGF/ERK signaling operates in the vicinity of a transition point between oscillatory and non-oscillatory dynamics in embryonic stem cells. The resulting heterogeneous dynamic signaling activities add a new dimension to cellular heterogeneity that may be linked to divergent fate decisions in stem cell cultures.

ChiSCAT: Unsupervised Learning of Recurrent Cellular Micromotion Patterns from a Chaotic Speckle Pattern.

There is considerable evidence that action potentials are accompanied by "intrinsic optical signals", such as a nanometer-scale motion of the cell membrane. Here we present ChiSCAT, a technically simple imaging scheme that detects such signals with interferometric sensitivity. ChiSCAT combines illumination by a chaotic speckle pattern and interferometric scattering microscopy (iSCAT) to sensitively detect motion in any direction. The technique features reflective high-NA illumination, common-path suppression of vibrations, and a large field of view. This approach maximizes sensitivity to motion, but does not produce a visually interpretable image. We show that unsupervised learning based on matched filtering and motif discovery can recover underlying motion patterns and detect action potentials. We demonstrate these claims in an experiment on blebbistatin-paralyzed cardiomyocytes. ChiSCAT opens the door to action potential measurement in scattering tissue, including a living brain.

Temporal classification of short time series data.

MOTIVATION: Within the frame of their genetic capacity, organisms are able to modify their molecular state to cope with changing environmental conditions or induced genetic disposition. As high throughput methods are becoming increasingly affordable, time series analysis techniques are applied frequently to study the complex dynamic interplay between genes, proteins, and metabolites at the physiological and molecular level. Common analysis approaches fail to simultaneously include (i) information about the replicate variance and (ii) the limited number of responses/shapes that a biological system is typically able to take. RESULTS: We present a novel approach to model and classify short time series signals, conceptually based on a classical time series analysis, where the dependency of the consecutive time points is exploited. Constrained spline regression with automated model selection separates between noise and signal under the assumption that highly frequent changes are less likely to occur, simultaneously preserving information about the detected variance. This enables a more precise representation of the measured information and improves temporal classification in order to identify biologically interpretable correlations among the data. AVAILABILITY AND IMPLEMENTATION: An open source F# implementation of the presented method and documentation of its usage is freely available in the TempClass repository, https://github.com/CSBiology/TempClass  [58].

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP.

BACKGROUND: Derivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations. RESULTS: The results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism's specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points. CONCLUSIONS: While we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Mitochondrial membrane potential instability on reperfusion after ischemia does not depend on mitochondrial Ca2+ uptake.

Physiologic Ca2+ entry via the Mitochondrial Calcium Uniporter (MCU) participates in energetic adaption to workload but may also contribute to cell death during ischemia/reperfusion (I/R) injury. The MCU has been identified as the primary mode of Ca2+ import into mitochondria. Several groups have tested the hypothesis that Ca2+ import via MCU is detrimental during I/R injury using genetically-engineered mouse models, yet the results from these studies are inconclusive. Furthermore, mitochondria exhibit unstable or oscillatory membrane potentials (ΔΨm) when subjected to stress, such as during I/R, but it is unclear if the primary trigger is an excess influx of mitochondrial Ca2+ (mCa2+), reactive oxygen species (ROS) accumulation, or other factors. Here, we critically examine whether MCU-mediated mitochondrial Ca2+ uptake during I/R is involved in ΔΨm instability, or sustained mitochondrial depolarization, during reperfusion by acutely knocking out MCU in neonatal mouse ventricular myocyte (NMVM) monolayers subjected to simulated I/R. Unexpectedly, we find that MCU knockout does not significantly alter mCa2+ import during I/R, nor does it affect ΔΨm recovery during reperfusion. In contrast, blocking the mitochondrial sodium-calcium exchanger (mNCE) suppressed the mCa2+ increase during Ischemia but did not affect ΔΨm recovery or the frequency of ΔΨm oscillations during reperfusion, indicating that mitochondrial ΔΨm instability on reperfusion is not triggered by mCa2+. Interestingly, inhibition of mitochondrial electron transport or supplementation with antioxidants stabilized I/R-induced ΔΨm oscillations. The findings are consistent with mCa2+ overload being mediated by reverse-mode mNCE activity and supporting ROS-induced ROS release as the primary trigger of ΔΨm instability during reperfusion injury.

Assessment of the impact of Uber system implementation on mortality from traffic injuries in Brazilian capital cities.

The rapid expansion of Uber Technologies, Inc.'s ride-sharing, courier service, and food delivery system and e-hailing applications has been transforming the logistics network and urban mobility around the world. We aimed to evaluate the impact of the Uber system on traffic injury (TI) mortality during its implementation in Brazilian capital cities. A quasiexperimental design of interrupted time series was used. The monthly mortality rates for TI standardized by age were analyzed. The date of availability of the Uber app, specific to each capital, was considered the start date. Data from the Brazilian Mortality Information System and the Brazilian Institute of Geography and Statistics were used. For the data analysis, from an interrupted time-series design, autoregressive integrated moving average (ARIMA) models with a transfer function were fitted. In 92.6% (n = 25) of Brazilian capitals, there was no impact of Uber system implementation, 12 months after the start of its activities, on TI mortality. A reduction in mortality from this cause was observed after the system was implemented in Belo Horizonte and Rio de Janeiro. The impact on TI mortality was progressive and continuous in both. More studies are needed to establish the factors associated with the inequalities observed in the impact of Uber system implementation between different locations and the heterogeneity of effects.

Refining Estimation of the Instantaneous Reproduction Number During Early Pandemic Stages: Addressing Case-Reporting Variability and Serial Interval Uncertainty.

During infectious disease outbreaks, estimates for the instantaneous reproduction number, R(t), are essential for understanding transmission dynamics. This study develops and analyzes new methodology to improve estimation of R(t) when observed case counts are subject to reporting patterns and available serial interval estimates are subject to uncertainty and non-representativeness. Specifically, we developed a Bayesian time-since-infection model with layers to adjust for reporting measurement error, integrate multiple candidate serial interval estimates, and estimate transmission with an autoregressive time-series model incorporating factors relevant to transmission. Additionally, we provide practical tools to identify reporting patterns and determine when to smooth case counts for more usable R(t) estimates. We evaluated model performance relative to widely adopted methodology by simulating outbreak data, finding improved R(t) estimation with the proposed methodology. We also used 2020 COVID-19 data to analyze transmission trends and predictors, identifying strong day-of-week and social distancing effects that subsequently reduced estimate volatility. In addition to new approaches for addressing serial interval uncertainty and incorporating transmission predictor information, this study provides an alternative approach for addressing case-reporting patterns without delaying detection or smoothing over relevant transmission signals. These tools and findings may be used or built upon for current and future outbreaks.

Suicide mortality following the implementation of tobacco packaging and pricing policies in Korea: an interrupted time-series analysis.

BACKGROUND: To prevent tobacco use in Korea, the national quitline number was added to tobacco packages in December 2012, tobacco prices were raised by 80% in January 2015, and graphic health warning labels were placed on tobacco packages in December 2016. This study evaluated the association of these tobacco packaging and pricing policies with suicide mortality in Korea. METHODS: Monthly mortality from suicide was obtained from Cause-of-Death Statistics in Korea from December 2007 to December 2019. Interrupted time-series analysis was performed using segmented Poisson regression models. Relative risks (RRs) and 95% confidence intervals (CIs) were calculated adjusted for suicide prevention strategies. RESULTS: Suicide mortality was 20 per 1,000,000 in December 2007 and showed a downward trend over the study period. After the implementation of tobacco packaging and pricing policies, suicide mortality immediately declined by - 0.09 percent points (95% CI = - 0.19 to 0.01; P > 0.05) for the national quitline number, - 0.22 percent points (95% CI = - 0.35 to - 0.09; P < 0.01) for tobacco prices, and - 0.30 percent points (95% CI = - 0.49 to - 0.11; P < 0.01) for graphic health warning labels. The corresponding RRs for these post-implementation changes compared with the pre-implementation level were 0.91 (95% CI = 0.83 to 1.00), 0.80 (95% CI = 0.70 to 0.91), and 0.74 (95% CI = 0.61 to 0.90), respectively. Significant associations between tobacco control policies and suicide mortality were observed even when stratified by sex and region. CONCLUSIONS: The findings of this study provide new evidence for an association between tobacco control policies and deaths by suicide. An array of effective tobacco control policies should be considered for prevention programs targeting suicide.

Predictability of ecological and evolutionary dynamics in a changing world.

Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power PP(t). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction R 2 of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship.

Unveiling the underlying drivers of Phanerozoic marine diversification.

In investigating global patterns of biodiversity through deep time, many large-scale drivers of diversification have been proposed, both biotic and abiotic. However, few robust conclusions about these hypothesized effectors or their roles have been drawn. Here, we use a linear stochastic differential equation (SDE) framework to test for the presence of underlying drivers of diversification patterns before examining specific hypothesized drivers. Using a global dataset of observations of skeletonized marine fossils, we infer origination, extinction and sampling rates (collectively called fossil time series) throughout the Phanerozoic using a capture-mark-recapture approach. Using linear SDEs, we then compare models including and excluding hidden (i.e. unmeasured) drivers of these fossil time series. We find evidence of large-scale underlying drivers of marine Phanerozoic diversification rates and present quantitative characterizations of these. We then test whether changing global temperature, sea-level, marine sediment area or continental fragmentation could act as drivers of the fossil time series. We show that it is unlikely any of these four abiotic factors are the hidden drivers we identified, though there is evidence for correlative links between sediment area and origination/extinction rates. Our characterization of the hidden drivers of Phanerozoic diversification and sampling will aid in the search for their ultimate identities.

Epidemiological characterization of hand, foot, and mouth disease among hospitalized children from 2014 to 2023 in a hospital in Henan Province: Longitudinal surveillance study.

Hand, foot, and mouth disease (HFMD) is an acute infectious illness primarily caused by enteroviruses. The present study aimed to describe the epidemiological characteristics of hospitalized HFMD patients in a hospital in Henan Province (Zhengzhou, China), and to predict the future epidemiological parameters. In this study, we conducted a retrospective analysis of general demographic and clinical data on hospitalized children who were diagnosed with HFMD from 2014 to 2023. We used wavelet analysis to determine the periodicity of the disease. We also conducted an analysis of the impact of the COVID-19 epidemic on the detection ratio of severe illness. Additionally, we employed a Seasonal Difference Autoregressive Moving Average (SARIMA) model to forecast characteristics of future newly hospitalized HFMD children. A total of 19 487 HFMD cases were included in the dataset. Among these cases, 1515 (7.8%) were classified as severe. The peak incidence of HFMD typically fell between May and July, exhibiting pronounced seasonality. The emergence of COVID-19 pandemic changed the ratio of severe illness. In addition, the best-fitted seasonal ARIMA model was identified as (2,0,2)(1,0,1)12. The incidence of severe cases decreased significantly following the introduction of the vaccine to the market (χ2 = 109.9, p < 0.05). The number of hospitalized HFMD cases in Henan Province exhibited a seasonal and declining trend from 2014 to 2023. Non-pharmacological interventions implemented during the COVID-19 pandemic have led to a reduction in the incidence of severe illness.

Temporal trends in hemodialysis access creation during the fistula first era.

OBJECTIVE: Although forearm arteriovenous fistulas (AVFs) are the preferred initial vascular access for hemodialysis based on national guidelines, there are no population-level studies evaluating trends in creation of forearm vs upper arm AVFs and arteriovenous grafts (AVGs). The purpose of this study was to report temporal trends in first-time permanent hemodialysis access type, and to assess the effect of national initiatives on rates of AVF placement. METHODS: Retrospective cross-sectional study (2012-2022) utilizing the Vascular Quality Initiative database. All patients older than 18 years with creation of first-time upper extremity surgical hemodialysis access were included. Anatomic location of the AVF or AVG (forearm vs upper arm) was defined based on inflow artery, outflow vein, and presumed cannulation zone. Primary analysis examined temporal trends in rates of forearm vs upper arm AVFs and AVGs using time series analyses (modified Mann-Kendall test). Subgroup analyses examined rates of access configuration stratified by age, sex, race, dialysis, and socioeconomic status. Interrupted time series analysis was performed to assess the effect of the 2015 Fistula First Catheter Last initiative on rates of AVFs. RESULTS: Of the 52,170 accesses, 57.9% were upper arm AVFs, 25.2% were forearm AVFs, 15.4% were upper arm AVGs, and 1.5% were forearm AVGs. From 2012 to 2022, there was no significant change in overall rates of forearm or upper arm AVFs. There was a numerical increase in upper arm AVGs (13.9 to 18.2 per 100; P = .09), whereas forearm AVGs significantly declined (1.8 to 0.7 per 100; P = .02). In subgroup analyses, we observed a decrease in forearm AVFs among men (33.1 to 28.7 per 100; P = .04) and disadvantaged (Area Deprivation Index percentile ≥50) patients (29.0 to 20.7 per 100; P = .04), whereas female (17.2 to 23.1 per 100; P = .03), Black (15.6 to 24.5 per 100; P < .01), elderly (age ≥80 years) (18.7 to 32.5 per 100; P < .01), and disadvantaged (13.6 to 20.5 per 100; P < .01) patients had a significant increase in upper arm AVGs. The Fistula First Catheter Last initiative had no effect on the rate of AVF placement (83.2 to 83.7 per 100; P=.37). CONCLUSIONS: Despite national initiatives to promote autogenous vascular access, the rates of first-time AVFs have remained relatively constant, with forearm AVFs only representing one-quarter of all permanent surgical accesses. Furthermore, elderly, Black, female, and disadvantaged patients saw an increase in upper arm AVGs. Further efforts to elucidate factors associated with forearm AVF placement, as well as potential physician, center, and regional variation is warranted.

The Paradox of Predictability Provides a Bridge Between Micro- and Macroevolution.

The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence are often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this "paradox of predictability." We begin by explaining why the divergence-variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative-genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding about how evolution works within and across timescales.

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear. PURPOSE: To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG-LSTM network. STUDY TYPE: Retrospective. POPULATION: Center A: 235 patients (47.7 ± 10.0 years) were divided 7:3 into training (n = 164) and validation set (n = 71). Center B: 150 patients (48.5 ± 10.4 years) were used as test set. FIELD STRENGTH/SEQUENCE: 3-T, T2-weighted spin-echo sequence imaging, and gradient echo DCE sequence imaging. ASSESSMENT: Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG-LSTM network to establish seven DL models using time-series DCE-MR images: pre-NAC images (t0 model), early NAC images (t1 model), post-NAC images (t2 model), pre-NAC and early NAC images (t0 + t1 model), pre-NAC and post-NAC images (t0 + t2 model), pre-NAC, early NAC and post-NAC images (t0 + t1 + t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set. STATISTICAL TESTS: The DeLong, Student's t-test, Mann-Whitney U, Chi-squared, Fisher's exact, Hosmer-Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. P < 0.05 was considered significant. RESULTS: Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927. DATA CONCLUSION: The combined model that used time-series DCE-MR images, clinical features and imaging features shows promise for identifying pCR in BC. TECHNICAL EFFICACY: Stage 4.

Mood regulation in euthymic patients with a history of antidepressant-induced mania.

INTRODUCTION: The use of antidepressants in bipolar disorder (BD) remains contentious, in part due to the risk of antidepressant-induced mania (AIM). However, there is no information on the architecture of mood regulation in patients who have experienced AIM. We compared the architecture of mood regulation in euthymic patients with and without a history of AIM. METHODS: Eighty-four euthymic participants were included. Participants rated their mood, anxiety and energy levels daily using an electronic (e-) visual analog scale, for a mean (SD) of 280.8(151.4) days. We analyzed their multivariate time series by computing each variable's auto-correlation, inter-variable cross-correlation, and composite multiscale entropy of mood, anxiety, and energy. Then, we compared the data features of participants with a history of AIM and those without AIM, using analysis of covariance, controlling for age, sex, and current treatment. RESULTS: Based on 18,103 daily observations, participants with AIM showed significantly stronger day-to-day auto-correlation and cross-correlation for mood, anxiety, and energy than those without AIM. The highest cross-correlation in participants with AIM was between mood and energy within the same day (median (IQR), 0.58 (0.27)). The strongest negative cross-correlation in participants with AIM was between mood and anxiety series within the same day (median (IQR), -0.52 (0.34)). CONCLUSION: Patients with a history of AIM have a different underlying mood architecture compared to those without AIM. Their mood, anxiety and energy stay the same from day-to-day; and their anxiety is negatively correlated with their mood.