Time-series blood cytokine profiles correlate with treatment responses in triple-negative breast cancer patients.

BACKGROUND: Triple-negative breast cancer (TNBC) is the most heterogeneous breast cancer subtype. Partly due to its heterogeneity, it is currently challenging to stratify TNBC patients and predict treatment outcomes. METHODS: In this study, we examined blood cytokine profiles of TNBC patients throughout treatments (pre-treatment, during chemotherapy, pre-surgery, and 1 year after the surgery in a total of 294 samples). We analyzed the obtained cytokine datasets using weighted correlation network analyses, protein-protein interaction analyses, and logistic regression analyses. RESULTS: We identified five cytokines that correlate with good clinical outcomes: interleukin (IL)-1α, TNF-related apoptosis-inducing ligand (TRAIL), Stem Cell Factor (SCF), Chemokine ligand 5 (CCL5 also known as RANTES), and IL-16. The expression of these cytokines was decreased during chemotherapy and then restored after the treatment. Importantly, patients with good clinical outcomes had constitutively high expression of these cytokines during treatments. Protein-protein interaction analyses implicated that these five cytokines promote an immune response. Logistic regression analyses revealed that IL-1α and TRAIL expression levels at pre-treatment could predict treatment outcomes in our cohort. CONCLUSION: We concluded that time-series cytokine profiles in breast cancer patients may be useful for understanding immune cell activity during treatment and for predicting treatment outcomes, supporting precision medicine. TRIAL REGISTRATION: The study has been registered with the University Hospital Medical Information Network Clinical Trials Registry ( http://www.umin.ac.jp/ctr/index-j.htm ) with the unique trial number UMIN000023162. The association Japan Breast Cancer Research Group trial number is JBCRG-22. The clinical outcome of the JBCRG-22 study was published in Breast Cancer Research and Treatment on 25 March 2021. https://doi.org/10.1007/s10549-021-06184-w .

Bayesian traction force estimation using cell boundary-dependent force priors.

Understanding the principles of cell migration necessitates measurements of the forces generated by cells. In traction force microscopy (TFM), fluorescent beads are placed on a substrate's surface and the substrate strain caused by the cell traction force is observed as displacement of the beads. Mathematical analysis can estimate traction force from bead displacement. However, most algorithms estimate substrate stresses independently of cell boundary, which results in poor estimation accuracy in low-density bead environments. To achieve accurate force estimation at low density, we proposed a Bayesian traction force estimation (BTFE) algorithm that incorporates cell-boundary-dependent force as a prior. We evaluated the performance of the proposed algorithm using synthetic data generated with mathematical models of cells and TFM substrates. BTFE outperformed other methods, especially in low-density bead conditions. In addition, the BTFE algorithm provided a reasonable force estimation using TFM images from the experiment.

Face-mask-aware Facial Expression Recognition based on Face Parsing and Vision Transformer.

As wearing face masks is becoming an embedded practice due to the COVID-19 pandemic, facial expression recognition (FER) that takes face masks into account is now a problem that needs to be solved. In this paper, we propose a face parsing and vision Transformer-based method to improve the accuracy of face-mask-aware FER. First, in order to improve the precision of distinguishing the unobstructed facial region as well as those parts of the face covered by a mask, we re-train a face-mask-aware face parsing model, based on the existing face parsing dataset automatically relabeled with a face mask and pixel label. Second, we propose a vision Transformer with a cross attention mechanism-based FER classifier, capable of taking both occluded and non-occluded facial regions into account and reweigh these two parts automatically to get the best facial expression recognition performance. The proposed method outperforms existing state-of-the-art face-mask-aware FER methods, as well as other occlusion-aware FER methods, on two datasets that contain three kinds of emotions (M-LFW-FER and M-KDDI-FER datasets) and two datasets that contain seven kinds of emotions (M-FER-2013 and M-CK+ datasets).

Differential temperature sensitivity of synaptic and firing processes in a neural mass model of epileptic discharges explains heterogeneous response of experimental epilepsy to focal brain cooling.

Experiments with drug-induced epilepsy in rat brains and epileptic human brain region reveal that focal cooling can suppress epileptic discharges without affecting the brain's normal neurological function. Findings suggest a viable treatment for intractable epilepsy cases via an implantable cooling device. However, precise mechanisms by which cooling suppresses epileptic discharges are still not clearly understood. Cooling experiments in vitro presented evidence of reduction in neurotransmitter release from presynaptic terminals and loss of dendritic spines at post-synaptic terminals offering a possible synaptic mechanism. We show that termination of epileptic discharges is possible by introducing a homogeneous temperature factor in a neural mass model which attenuates the post-synaptic impulse responses of the neuronal populations. This result however may be expected since such attenuation leads to reduced post-synaptic potential and when the effect on inhibitory interneurons is less than on excitatory interneurons, frequency of firing of pyramidal cells is consequently reduced. While this is observed in cooling experiments in vitro, experiments in vivo exhibit persistent discharges during cooling but suppressed in magnitude. This leads us to conjecture that reduction in the frequency of discharges may be compensated through intrinsic excitability mechanisms. Such compensatory mechanism is modelled using a reciprocal temperature factor in the firing response function in the neural mass model. We demonstrate that the complete model can reproduce attenuation of both magnitude and frequency of epileptic discharges during cooling. The compensatory mechanism suggests that cooling lowers the average and the variance of the distribution of threshold potential of firing across the population. Bifurcation study with respect to the temperature parameters of the model reveals how heterogeneous response of epileptic discharges to cooling (termination or suppression only) is exhibited. Possibility of differential temperature effects on post-synaptic potential generation of different populations is also explored.

Discharge-rate persistence of baseline activity during fixation reflects maintenance of memory-period activity in the macaque posterior parietal cortex.

Recent evidence has demonstrated that spatiotemporal patterns of spontaneous activity reflect the patterns of activity evoked by sensory stimuli. However, few studies have examined whether response profiles of task-evoked activity, which is not related to external sensory stimuli but rather to internal processes, are also reflected in those of spontaneous activity. To address this, we recorded activity of neurons in the lateral intraparietal area (LIP) when monkeys performed reaction-time and delayed-response visual-search tasks. We particularly focused on the target location-dependent modulation of delay-period activity (delay-period modulation) in the delayed-response task, and the discharge-rate persistency in fixation-period activity (baseline-activity maintenance) in the reaction-time task. Baseline-activity maintenance was assessed by the correlation between the spike counts of 2 separate bins. We found that baseline-activity maintenance, calculated from bins separated by a long interval (200-500 ms), was correlated with delay-period modulation, whereas that calculated from bins separated by a short interval (~100 ms) was correlated with trial-to-trial fluctuations in baseline activity, suggesting a link between the capability to hold task-related information in delay-period activity and the degree of baseline-activity maintenance in a timescale-dependent manner.

Decoding cellular deformation from pseudo-simultaneously observed Rho GTPase activities.

Limitations in simultaneously observing the activity of multiple molecules in live cells prevent researchers from elucidating how these molecules coordinate the dynamic regulation of cellular functions. Here, we propose the motion-triggered average (MTA) algorithm to characterize pseudo-simultaneous dynamic changes in arbitrary cellular deformation and molecular activities. Using MTA, we successfully extract a pseudo-simultaneous time series from individually observed activities of three Rho GTPases: Cdc42, Rac1, and RhoA. To verify that this time series encoded information on cell-edge movement, we use a mathematical regression model to predict the edge velocity from the activities of the three molecules. The model accurately predicts the unknown edge velocity, providing numerical evidence that these Rho GTPases regulate edge movement. Data preprocessing using MTA combined with mathematical regression provides an effective strategy for reusing numerous individual observations of molecular activities.

Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning.

Background: Phenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge. Methods: We recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms. Results: Participants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep-wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit. Conclusion: We found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery.

Multi-level attention pooling for graph neural networks: Unifying graph representations with multiple localities.

Graph neural networks (GNNs) have been widely used to learn vector representation of graph-structured data and achieved better task performance than conventional methods. The foundation of GNNs is the message passing procedure, which propagates the information in a node to its neighbors. Since this procedure proceeds one step per layer, the range of the information propagation among nodes is small in the lower layers, and it expands toward the higher layers. Therefore, a GNN model has to be deep enough to capture global structural information in a graph. On the other hand, it is known that deep GNN models suffer from performance degradation because they lose nodes' local information, which would be essential for good model performance, through many message passing steps. In this study, we propose multi-level attention pooling (MLAP) for graph-level classification tasks, which can adapt to both local and global structural information in a graph. It has an attention pooling layer for each message passing step and computes the final graph representation by unifying the layer-wise graph representations. The MLAP architecture allows models to utilize the structural information of graphs with multiple levels of localities because it preserves layer-wise information before losing them due to oversmoothing. Results of our experiments show that the MLAP architecture improves the graph classification performance compared to the baseline architectures. In addition, analyses on the layer-wise graph representations suggest that aggregating information from multiple levels of localities indeed has the potential to improve the discriminability of learned graph representations.

Behavioral and neuro-cognitive bases for emergence of norms and socially shared realities via dynamic interaction.

In the digital era, new socially shared realities and norms emerge rapidly, whether they are beneficial or harmful to our societies. Although these are emerging properties from dynamic interaction, most research has centered on static situations where isolated individuals face extant norms. We investigated how perceptual norms emerge endogenously as shared realities through interaction, using behavioral and fMRI experiments coupled with computational modeling. Social interactions fostered convergence of perceptual responses among people, not only overtly but also at the covert psychophysical level that generates overt responses. Reciprocity played a critical role in increasing the stability (reliability) of the psychophysical function within each individual, modulated by neural activity in the mentalizing network during interaction. These results imply that bilateral influence promotes mutual cognitive anchoring of individual views, producing shared generative models at the collective level that enable endogenous agreement on totally new targets-one of the key functions of social norms.

Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO2 system.

Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on simple linear regression algorithms that are prone to underfitting the training data. These models rely heavily on experimental and expensive computational quantum mechanical descriptors, which significantly limit their practical use. Herein, we present the application of empirical descriptors and complex machine learning algorithms to the development of high-performance quantitative structure-toxicity relationship (QSTR) models of TiO2 hybridized with multi-metallic (Ag, Au, Pt) alloy nanoparticles (multi-metallic NPs/TiO2). To confirm the viability of empirical descriptors as model input, we selected five distinct machine learning algorithms for predicting the toxicity of multi-metallic alloy NPs/TiO2 system in Chinese hamster ovary cell line. Notably, an empirical descriptor-based QSTR model (kernel ridge regression) revealed a predictive performance that is on par with density functional theory (DFT) descriptor-based counterparts. More specifically, the results indicated that model selection is influenced by descriptor choice, such that complex DFT descriptors worked best with a complex algorithm (random forest regression; RMSET = 0.0954, MAET = 0.0811, RT2 = 0.9411), whereas more straightforward empirical descriptors were most suitable with a simpler algorithm (kernel ridge regression; RMSET = 0.1244, MAET = 0.1106, RT2 = 0.8999). Moreover, our model outperforms existing QSAR models built on the same data set. This study offers a new perspective on using empirical features to develop accurate predictive computational models for the rapid discovery and profiling of safe-by-design nanomaterials.

Expert Programmers Have Fine-Tuned Cortical Representations of Source Code.

Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many studies have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain of programmers are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on a source-code categorization task. Our results suggest that programming expertise is built on fine-tuned cortical representations specialized for the domain of programming.

Leveraging Longitudinal Lifelog Data Using Survival Models for Predicting Risk of Relapse among Patients with Depression in Remission.

Managing depression relapse is a challenge given factors such as inconsistent follow-up and cumbersome psychological distress evaluation methods which leaves patients with a high risk of relapse to leave their symptoms untreated. In an attempt to bridge this gap, we proposed an approach on the use of personal longitudinal lifelog activity data gathered from individual smartphones of patients in remission and maintenance therapy (N=87) to predict their risk of depression relapse. Through the use of survival models, we modeled the activity data as covariates to predict survival curves to determine if patients are at risk of relapse. We compared three models: CoxPH, Random Survival Forests, and DeepSurv, and found that DeepSurv performed the best in terms of Concordance Index and Brier Score. Our results show the possibility of utilizing lifelog data as a means of predicting the onset of relapse and towards building eventual tools for a more coherent patient evaluation and intervention system.

Contractile Activity of Myotubes Derived from Human Induced Pluripotent Stem Cells: A Model of Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy (DMD) is a genetic disorder that results from deficiency of the dystrophin protein. In recent years, DMD pathological models have been created using induced pluripotent stem (iPS) cells derived from DMD patients. In addition, gene therapy using CRISPR-Cas9 technology to repair the dystrophin gene has been proposed as a new treatment method for DMD. However, it is not known whether the contractile function of myotubes derived from gene-repaired iPS cells can be restored. We therefore investigated the maturation of myotubes in electrical pulse stimulation culture and examined the effect of gene repair by observing the contractile behaviour of myotubes. The contraction activity of myotubes derived from dystrophin-gene repaired iPS cells was improved by electrical pulse stimulation culture. The iPS cell method used in this study for evaluating muscle contractile activity is a useful technique for analysing the mechanism of hereditary muscular disease pathogenesis and for evaluating the efficacy of new drugs and gene therapy.

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis.

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.

Feasibility of focal brain cooling for partial epilepsy with secondary generalization: A computational study.

Experiments with animal models of epilepsy have consistently shown that focal cooling of epilepsy-induced brain region reversibly suppresses or terminates epileptic discharge activity. Recently, we formulated a physiologically plausible temperature dependence in a neural mass model that can reproduce the effect of focal cooling on epileptic discharge activity. This can be used to implement a temperature control in an implantable cooling device for thermal neuromodulation of the epileptogenic zone in patients with partial epilepsy when seizure activity is detected. However, there have been no experiments that looked into the effect of focal cooling in animal models of epilepsy with secondary generalization in which the seizure activity spreads from the pathologic region to other regions of the brain. Using the temperature-dependent neural mass model and a physiological coupling model, we show that focal cooling stops the propagation of low-frequency discharge activity; on the other hand, it increases the amount of coupling required to propagate high-frequency discharge activity. Moreover, discharge activities that are propagated with cooling are lower in both magnitude and frequency compared to those propagated without cooling. These results suggest the feasibility of focal cooling as an effective alternative therapeutic treatment for medically intractable partial epilepsy even with secondary generalization.Clinical Relevance- The computational study establishes focal cooling of the brain region with partial epilepsy not only suppresses epileptic discharges but can also prevent its generalization to other brain regions.

Characterizing Strategic Contributions of Physical Therapy to Natural Standing Motion in the Muscle Synergy Space.

Understanding the contributions of therapist skill during intervention is essential for improving existing rehabilitation methodologies. This study aims to characterize therapist intervention on an important activity of daily living, the sit-to-stand motion. Using the concept of muscle synergy, we quantify and compare naturally-occurring standing strategies with those induced by a physical therapist. In this paper, we show that natural standing strategies are not shared among healthy subjects. However, each subject retains their own set of strategies. Moreover, the results suggest that a therapist does not introduce new strategies during therapy, but rather modulates the existing strategies of the individuals. Using such a low-dimensional representation of standing behavior allows for development of low-cost tools for wider distribution.

Fabricating Muscle-Neuron Constructs with Improved Contractile Force Generation.

IMPACT STATEMENT: In this study, we fabricated innervated skeletal muscle tissue constructs comprising C2C12 myoblasts and PC12 neural cells using a magnetic force-based tissue engineering technique. We found that the C2C12/PC12 co-culture enhanced neural differentiation of PC12 cells and sarcomere formation of C2C12 myotubes, accompanying with neuromuscular junction formation. The innervated skeletal muscle tissue constructs generated significantly higher contractile forces compared with aneural (C2C12 monoculture) skeletal muscle tissue constructs. These innervated skeletal muscle tissue constructs can be a useful tool for drug testing and biological research for neuromuscular diseases.

Emotional Contagion From Humans to Dogs Is Facilitated by Duration of Ownership.

Emotional contagion is a primitive form of empathy that does not need higher psychological functions. Recent studies reported that emotional contagion exists not only between humans but also among various animal species. The dog (Canis familiaris) is a unique animal and the oldest domesticated species. Dogs have coexisted with humans for more than 30,000 years and are woven into human society as partners bonding with humans. Dogs have acquired human-like communication skills and, likely as a result of the domestication process, the ability to read human emotions; therefore, it is feasible that there may be emotional contagion between human and dogs. However, the higher time-resolution of measurement of emotional contagion between them is yet to be conducted. We assessed the emotional reactions of dogs and humans by heart rate variability (HRV), which reflects emotion, under a psychological stress condition on the owners. The correlation coefficients of heart beat (R-R) intervals (RRI), the standard deviations of all RR intervals (SDNN), and the square root of the mean of the sum of the square of differences between adjacent RR intervals (RMSSD) between dogs and owners were positively correlated with the duration of dog ownership. Dogs' sex also influenced the correlation coefficients of the RRI, SDNN, and RMSSD in the control condition; female showed stronger values. These results suggest that emotional contagion from owner to dog can occur especially in females and the time sharing the same environment is the key factor in inducing the efficacy of emotional contagion.

Non-monotonic convergence of online learning algorithms for perceptrons with noisy teacher.

Learning curves of simple perceptron were derived here. The learning curve of the perceptron learning with noisy teacher was shown to be non-monotonic, which has never appeared even though the learning curves have been analyzed for half a century. In this paper, we showed how this phenomenon occurs by analyzing the asymptotic property of the perceptron learning using a method in systems science, that is, calculating the eigenvalues of the system matrix and the corresponding eigenvectors. We also analyzed the AdaTron learning and the Hebbian learning in the same way and found that the learning curve of the AdaTron learning is non-monotonic whereas that of the Hebbian learning is monotonic.