An electro-optical platform for the ultrasensitive detection of small extracellular vesicle sub-types and their protein epitope counts.

Methods for detecting proteins in small extracellular vesicles (sEVs) lack sensitivity and quantitative accuracy, missing clues about health and disease. Our study introduces the Nano-Extracellular Omics Sensing (NEXOS) platform, merging electrical (E-NEXOS) and optical detection (O-NEXOS). E-NEXOS determines the concentration of target sEV sub-types, and O-NEXOS quantifies the concentration of target protein epitopes (TEPs) on those TEVs. In this work, both technologies were compared to several sEV detection tools, showing superior detection limits for CD9+CD81+ and CD9+HER2+ sEVs. Furthermore, the additional information on TEVs and TEPs from bulk sEV samples, provided new phenotyping capabilities. We determined the average number of CD81 and HER2 proteins on CD9+ sEVs, a number which was later validated on spiked human plasma. These results highlight the compatibility of NEXOS with complex biofluids and, as importantly, hint at its many potential applications, ranging from basic research to the anticipated clinical translation of sEVs.

Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction.

Hydrological forecasting is of great importance for water resources management and planning, especially given the increasing occurrence of extreme events such as floods and droughts. The physics-informed machine learning (PIML) models effectively integrate conceptual hydrologic models with machine learning (ML) models. In this process, the intermediate variables of PIML models serve as bridges between inputs and outputs, while the impact of intermediate variables on the performance of PIML models remains unclear. To fill this knowledge gap, this study aims to encompass the construction of PIML models based on various hydrologic models, conduct comparative analyses of different intermediate variables based on a case study of 205 CAMELS basins, and further explore the relationship between the performance of PIML models and catchment characteristics. The optimal ML model for constructing PIML is first selected among four ML models within the 205 basins. The PIML models are then developed based on five monthly water balance models, namely TM, XM, MEP, SLM, and TVGM. To quantify the potential impact of difference in intermediate variables, two sets of experiments are further designed and performed, namely S1 with actual evapotranspiration as the intermediate variable and S2 with soil moisture as the intermediate variable. Results show that five PIML models generally outperformed the optimal standalone ML models, i.e., the Lasso model. Specifically, regardless of the choice of intermediate variables, the PIML-XM model consistently outperformed the other models within the same basins. Almost all constructed PIML models are affected by the intermediate variables in monthly runoff simulations. Typically, S1 exhibited better performance compared to S2. A greater impact of aridity index, forest fraction, and catchment area on model performance is observed in S2. These findings improve our understanding of constructing PIML models in hydrology by emphasizing their excellent performance in runoff simulations and highlighting the importance of intermediate variables.

Dual-energy CT for occult pelvic fractures: An audit and roc analysis.

INTRODUCTION: There is an increasing incidence of hip and pelvic fractures with an ageing population. Accurate and timely diagnosis is important in the emergency setting. While magnetic resonance imaging (MRI) is the gold standard, it is a limited resource. Dual energy CT (DECT) is comparable to MRI in detection of bone marrow oedema. Our hospital was the first centre in our country to introduce DECT for occult pelvic fractures. We aimed to describe its utility in occult pelvic fractures since commencement. METHODS: Retrospective study of consecutive pelvic bone CT (conventional or DECT) performed to look for an occult fracture over a 10-month period. Sensitivity and specificity calculated based on clinical and imaging follow-up. ROC study performed where three observers visually interpreted pelvic radiographs, conventional CT and DECT and scored their confidence for an acute fracture from 1 to 5. The null hypothesis was that DECT would not improve observer performance compared with conventional CT. RESULTS: DECT studies were performed on 178 patients of whom 84 (47%) had acute fractures. Sensitivity on audit was 99% and specificity was 100%. ROC analysis showed that, for all observers, the area under curve increased from radiograph to conventional CT to DECT. The difference between conventional CT and DECT was statistically significant for all observers where metal implants were not present. CONCLUSION: DECT improves accuracy compared to conventional CT in the diagnosis of occult pelvic fractures and should be used for this indication when available.

Potassium-mediated bacterial chemotactic response.

Bacteria in biofilms secrete potassium ions to attract free swimming cells. However, the basis of chemotaxis to potassium remains poorly understood. Here, using a microfluidic device, we found that Escherichia coli can rapidly accumulate in regions of high potassium concentration on the order of millimoles. Using a bead assay, we measured the dynamic response of individual flagellar motors to stepwise changes in potassium concentration, finding that the response resulted from the chemotaxis signaling pathway. To characterize the chemotactic response to potassium, we measured the dose-response curve and adaptation kinetics via an Förster resonance energy transfer (FRET) assay, finding that the chemotaxis pathway exhibited a sensitive response and fast adaptation to potassium. We further found that the two major chemoreceptors Tar and Tsr respond differently to potassium. Tar receptors exhibit a biphasic response, whereas Tsr receptors respond to potassium as an attractant. These different responses were consistent with the responses of the two receptors to intracellular pH changes. The sensitive response and fast adaptation allow bacteria to sense and localize small changes in potassium concentration. The differential responses of Tar and Tsr receptors to potassium suggest that cells at different growth stages respond differently to potassium and may have different requirements for potassium.

A simple active fluid model unites cytokinesis, cell crawling, and axonal outgrowth.

Axonal outgrowth, cell crawling, and cytokinesis utilize actomyosin, microtubule-based motors, cytoskeletal dynamics, and substrate adhesions to produce traction forces and bulk cellular motion. While it has long been appreciated that growth cones resemble crawling cells and that the mechanisms that drive cytokinesis help power cell crawling, they are typically viewed as unique processes. To better understand the relationship between these modes of motility, here, we developed a unified active fluid model of cytokinesis, amoeboid migration, mesenchymal migration, neuronal migration, and axonal outgrowth in terms of cytoskeletal flow, adhesions, viscosity, and force generation. Using numerical modeling, we fit subcellular velocity profiles of the motions of cytoskeletal structures and docked organelles from previously published studies to infer underlying patterns of force generation and adhesion. Our results indicate that, during cytokinesis, there is a primary converge zone at the cleavage furrow that drives flow towards it; adhesions are symmetric across the cell, and as a result, cells are stationary. In mesenchymal, amoeboid, and neuronal migration, the site of the converge zone shifts, and differences in adhesion between the front and back of the cell drive crawling. During neuronal migration and axonal outgrowth, the primary convergence zone lies within the growth cone, which drives actin retrograde flow in the P-domain and bulk anterograde flow of the axonal shaft. They differ in that during neuronal migration, the cell body is weakly attached to the substrate and thus moves forward at the same velocity as the axon. In contrast, during axonal outgrowth, the cell body strongly adheres to the substrate and remains stationary, resulting in a decrease in flow velocity away from the growth cone. The simplicity with which cytokinesis, cell crawling, and axonal outgrowth can be modeled by varying coefficients in a simple model suggests a deep connection between them.

Electric Field Effects in Hybrid Perovskites Studied via Picosecond Kerr Microscopy.

We present time-resolved Kerr rotation (TRKR) spectra in thin films of CH3NH3PbI3 (MAPI) hybrid perovskite using a unique picosecond microscopy technique at 4 K having a spatial resolution of 2 µm and temporal resolution of 1 ps, subjected to both an in-plane applied magnetic field up to 700 mT and an electric field up to 104 V/cm. We demonstrate that the obtained TRKR dynamics and spectra are substantially inhomogeneous across the MAPI films with prominent resonances at the exciton energy and interband transition of this compound. From the obtained quantum beating response as a function of magnetic field in the Voigt configuration, we also extract the inhomogeneity of the electron and hole Lande g-values and spin coherence time, T2*. We also report the TRKR dependence on both the applied magnetic field and electric field. From the change in the quantum beating dynamics, we found that T2* substantially decreases upon the application of an electric field. At the same time, from the induced spatial TRKR changes, we show that the electric field induced effects are caused by ion migration in the MAPI films.

Enhanced parameter estimation in multiparametric arterial spin labeling using artificial neural networks.

PURPOSE: Multiparametric arterial spin labeling (MP-ASL) can quantify cerebral blood flow (CBF) and arterial cerebral blood volume (CBVa). However, its accuracy is compromised owing to its intrinsically low SNR, necessitating complex and time-consuming parameter estimation. Deep neural networks (DNNs) offer a solution to these limitations. Therefore, we aimed to develop simulation-based DNNs for MP-ASL and compared the performance of a supervised DNN (DNNSup), physics-informed unsupervised DNN (DNNUns), and the conventional lookup table method (LUT) using simulation and in vivo data. METHODS: MP-ASL was performed twice during resting state and once during the breath-holding task. First, the accuracy and noise immunity were evaluated in the first resting state. Second, CBF and CBVa values were statistically compared between the first resting state and the breath-holding task using the Wilcoxon signed-rank test and Cliff's delta. Finally, reproducibility of the two resting states was assessed. RESULTS: Simulation and first resting-state analyses demonstrated that DNNSup had higher accuracy, noise immunity, and a six-fold faster computation time than LUT. Furthermore, all methods detected task-induced CBF and CBVa elevations, with the effect size being larger with the DNNSup (CBF, p = 0.055, Δ = 0.286; CBVa, p = 0.008, Δ = 0.964) and DNNUns (CBF, p = 0.039, Δ = 0.286; CBVa, p = 0.008, Δ = 1.000) than that with LUT (CBF, p = 0.109, Δ = 0.214; CBVa, p = 0.008, Δ = 0.929). Moreover, all the methods exhibited comparable and satisfactory reproducibility. CONCLUSION: DNNSup outperforms DNNUns and LUT with respect to estimation performance and computation time.

Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study.

BACKGROUND: The goal is to use three different machine learning models to predict the recurrence of breast cancer across a very heterogeneous sample of patients with varying disease kinds and stages. METHODS: A heterogeneous group of patients with varying cancer kinds and stages, including both triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC), was examined. Three distinct models were created using the following five machine learning techniques: Adaptive Boosting (AdaBoost), Random Under-sampling Boosting (RUSBoost), Extreme Gradient Boosting (XGBoost), support vector machines (SVM), and Logistic Regression. The clinical model used both clinical and pathology data in conjunction with the machine learning algorithms. The machine learning algorithms were combined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) imaging characteristics in the radiomic model, and the merged model combined the two types of data. Each technique was evaluated using several criteria, including the receiver operating characteristic (ROC) curve, precision, recall, and F1 score. RESULTS: The results suggest that the integration of clinical and radiomic data improves the predictive accuracy in identifying instances of breast cancer recurrence. The XGBoost algorithm is widely recognized as the most effective algorithm in terms of performance. CONCLUSION: The findings presented in this study offer significant contributions to the field of breast cancer research, particularly in relation to the prediction of cancer recurrence. These insights hold great potential for informing future investigations and clinical interventions that seek to enhance the accuracy and effectiveness of recurrence prediction in breast cancer patients.

Protocol to fabricate a self-adhesive and long-term stable hydrogel for sleep EEG recording.

Long-term continuous electroencephalogram (EEG) monitoring is crucial for neuroengineering but suffers from hardware limitations. Here, we present a protocol for EEG recording using a long-term stable and reagent-free-cross-linked hydrogel with configurable mechanical and adhesive properties. We describe steps for fabricating the hydrogel and performing material characterizations and stability tests. We detail procedures for setting up the EEG recording configuration and data analysis. This protocol can facilitate EEG recording experiments with the hydrogel, as well as other novel materials and devices. For complete details on the use and execution of this protocol, please refer to Hsieh et al.1.

Geometric phase predicts locomotion performance in undulating living systems across scales.

Self-propelling organisms locomote via generation of patterns of self-deformation. Despite the diversity of body plans, internal actuation schemes and environments in limbless vertebrates and invertebrates, such organisms often use similar traveling waves of axial body bending for movement. Delineating how self-deformation parameters lead to locomotor performance (e.g. speed, energy, turning capabilities) remains challenging. We show that a geometric framework, replacing laborious calculation with a diagrammatic scheme, is well-suited to discovery and comparison of effective patterns of wave dynamics in diverse living systems. We focus on a regime of undulatory locomotion, that of highly damped environments, which is applicable not only to small organisms in viscous fluids, but also larger animals in frictional fluids (sand) and on frictional ground. We find that the traveling wave dynamics used by mm-scale nematode worms and cm-scale desert dwelling snakes and lizards can be described by time series of weights associated with two principal modes. The approximately circular closed path trajectories of mode weights in a self-deformation space enclose near-maximal surface integral (geometric phase) for organisms spanning two decades in body length. We hypothesize that such trajectories are targets of control (which we refer to as "serpenoid templates"). Further, the geometric approach reveals how seemingly complex behaviors such as turning in worms and sidewinding snakes can be described as modulations of templates. Thus, the use of differential geometry in the locomotion of living systems generates a common description of locomotion across taxa and provides hypotheses for neuromechanical control schemes at lower levels of organization.

Infants' representations of michottean triggering events.

The classic Michottean 'launching' event is consistent with a real-world Newtonian elastic collision. Previous research has shown that adult humans distinguish launching events that obey some of the physical constraints on Newtonian elastic collisions from events that do not do so early in visual processing, and that infants do so early in development (< 9 months of age). These include that in a launching event, the speed of the agent can be 3 times faster (or more) than that of the patient but the speed of the patient cannot be detectably greater than the speed of the agent. Experiment 1 shows that 7-8-month-old infants also distinguish canonical launching events from events in which the motion of the patient is rotated 90° from the trajectory of the motion of the agent (another outcome ruled out by the physics of elastic collisions). Violations of both the relative speed and the angle constraints create Michottean 'triggering' events, in which adults describe the motion of the patient as autonomous but not spontaneous, i.e., still initiated by contact with the causal agent. Experiments 2 and 3 begin to explore whether infants of this age construe Michottean triggering events as causal. We find that infants of this age are not sensitive to a reversal of the agent and patient in triggering events, thus failing to exhibit one of the signatures of representing an event as causal. We argue that there are likely several independent events schemas with causal content represented by young infants, and the literature on the origins of causal cognition in infancy would benefit from systematic investigations of event schemas other than launching events.

Quantifying gliding forces of filamentous cyanobacteria by self-buckling.

Filamentous cyanobacteria are one of the oldest and today still most abundant lifeforms on earth, with manifold implications in ecology and economics. Their flexible filaments, often several hundred cells long, exhibit gliding motility in contact with solid surfaces. The underlying force generating mechanism is not yet understood. Here, we demonstrate that propulsion forces and friction coefficients are strongly coupled in the gliding motility of filamentous cyanobacteria. We directly measure their bending moduli using micropipette force sensors, and quantify propulsion and friction forces by analyzing their self-buckling behavior, complemented with analytical theory and simulations. The results indicate that slime extrusion unlikely generates the gliding forces, but support adhesion-based hypotheses, similar to the better-studied single-celled myxobacteria. The critical self-buckling lengths align well with the peaks of natural length distributions, indicating the importance of self-buckling for the organization of their collective in natural and artificial settings.

Partitioning to ordered membrane domains regulates the kinetics of secretory traffic.

The organelles of eukaryotic cells maintain distinct protein and lipid compositions required for their specific functions. The mechanisms by which many of these components are sorted to their specific locations remain unknown. While some motifs mediating subcellular protein localization have been identified, many membrane proteins and most membrane lipids lack known sorting determinants. A putative mechanism for sorting of membrane components is based on membrane domains known as lipid rafts, which are laterally segregated nanoscopic assemblies of specific lipids and proteins. To assess the role of such domains in the secretory pathway, we applied a robust tool for synchronized secretory protein traffic (RUSH, Retention Using Selective Hooks) to protein constructs with defined affinity for raft phases. These constructs consist solely of single-pass transmembrane domains (TMDs) and, lacking other sorting determinants, constitute probes for membrane domain-mediated trafficking. We find that while raft affinity can be sufficient for steady-state PM localization, it is not sufficient for rapid exit from the endoplasmic reticulum (ER), which is instead mediated by a short cytosolic peptide motif. In contrast, we find that Golgi exit kinetics are highly dependent on raft affinity, with raft preferring probes exiting the Golgi ~2.5-fold faster than probes with minimal raft affinity. We rationalize these observations with a kinetic model of secretory trafficking, wherein Golgi export can be facilitated by protein association with raft domains. These observations support a role for raft-like membrane domains in the secretory pathway and establish an experimental paradigm for dissecting its underlying machinery.