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1. Machine learning-based menstrual phase identification using wearable device data

   https://doi.org/10.1038/s44294-025-00078-8  

   Kilungeja, Grentina; Graham, Krystal; Liu, Xudong; Nasseri, Mona (December 2025, npj Women's Health)

   This study applies machine learning to identify menstrual cycle phases using physiological signals recorded from a wrist-worn device. These signals include skin temperature, electrodermal activity (EDA), interbeat interval (IBI), and heart rate (HR), and were collected without requiring participant input. Data from 65 cycles across 18 subjects were analyzed, and multiple classifiers including random forest (RF) models were trained to classify the phases. Using a leave-last-cycle-out approach, and features from non-overlapping fixed-size windows, the RF model achieved 87% accuracy and an area under the receiver operating characteristic curve (AUC-ROC) of 0.96 when classifying three phases (period, ovulation, and luteal). For daily phase tracking using a sliding window, the RF model achieved 68% accuracy and an AUC-ROC of 0.77 when classifying four phases (period, follicular, ovulation, luteal). While these results highlight the potential of wrist-based physiological signals to enable automated phase tracking, reduce the burden of self-reporting, and improve access to cycle tracking solutions, further validation is needed to enhance the results. 

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2. Extended metric validation of a semi-physical Space Weather Modeling Framework conductance model on field-aligned current estimations

   https://doi.org/10.3389/fspas.2024.1354615  

   Hathaway, Erika Y; Mukhopadhyay, Agnit; Liemohn, Michael W; Keebler, Timothy; Anderson, Brian J; Vines, Sarah K; Barnes, Robin J (August 2024, Frontiers in Astronomy and Space Sciences)

   In this study, a detailed metric survey on the “Galaxy 15” (April 2010) space weather event is conducted to validate MAGNetosphere–Ionosphere–Thermosphere (MAGNIT), a semi-physical auroral ionospheric conductance model characterizing four precipitation sources, against AMPERE measurements via field-aligned current (FAC) characteristics. As part of this study, the comparative performance of three ionosphere electrodynamic specifications involving auroral conductance models, MAGNIT, Ridley Legacy Model (RLM) (empirical), and Conductance Model for Extreme Events (CMEE) (empirical), within the Space Weather Modeling Framework (SWMF), is demonstrated. Overall, MAGNIT exhibits marginally improved predictions; root mean square error values in upward and downward FACs of MAGNIT predictions compared to AMPERE data are smaller than those of CMEE and Ridley Ionosphere Model (RIM) by $\sim$ 12.7% and $\sim$ 6.24% before the storm, $\sim$ 4.52% and $\sim$ 2.13% better during the main phase, $\sim$ 1.98% and $\sim$ 1.27% worse during the second minimum, and better by $\sim$ 1.84% and $\sim$ 1.49% by the beginning of the recovery, respectively. In all three model configurations, the dusk and night magnetic local time (MLT) sectors over-predict throughout the storm, while the day and dawn MLT sectors under-predict in response to interplanetary magnetic field (IMF) conditions. In addition to accuracy and bias, similar results and conclusions are drawn from additional metrics, including in the categories of correlation, precision, extremes, and skill, and recommendations are made for the best-performing model configuration in each metric category. Visual data–model comparisons conducted by studying the FAC location and latitude/MLT spread throughout various phases of the storm suggest that the spatial extent of the FACs is captured relatively well in the night-side auroral oval, unlike in the day-side oval. The spread in latitude of the FACs matches that in the previous literature on other model performances. This information on auroral precipitation sources and their weight on FACs, along with metrics from model–data comparisons, can be used to modify MAGNIT settings to optimize SWMF model performance. 

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3. Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

   https://doi.org/10.3389/fmedt.2022.1034801  

   Tanade, Cyrus; Chen, S James; Leopold, Jane A; Randles, Amanda (December 2022, Frontiers in Medical Technology)

   BackgroundPersonalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR ${}_{\mathrm{baseline}}$ ), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines. AimsTo determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR ${}_{\mathrm{invasive}}$ ) as gold standard. Materials and MethodsPersonalized coronary geometries ( $N=50$ ) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR ${}_{\mathrm{baseline}}$ , was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR ${}_{\mathrm{baseline}}$ was validated against FFR ${}_{\mathrm{invasive}}$ and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR ${}_{\mathrm{streamlined}}$ was created by only incorporating the most sensitive inputs and FFR ${}_{\text{semi-streamlined}}$ additionally included patient-specific distal location. ResultsFFR ${}_{\mathrm{baseline}}$ was validated against FFR ${}_{\mathrm{invasive}}$ via correlation ( $r=0.714$ , $p<0.001$ ), agreement (mean difference: $0.01\pm 0.09$ ), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR ${}_{\text{semi-streamlined}}$ provided identical diagnostic performance with FFR ${}_{\mathrm{baseline}}$ . Compared to FFR ${}_{\mathrm{baseline}}$ vs. FFR ${}_{\mathrm{invasive}}$ , FFR ${}_{\mathrm{streamlined}}$ vs. FFR ${}_{\mathrm{invasive}}$ had decreased correlation ( $r=0.64$ , $p<0.001$ ), improved agreement (mean difference: $0.01\pm 0.08$ ), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90). ConclusionStreamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement. 

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4. Diffeomorphism groups of prime 3-manifolds

   https://doi.org/10.1515/crelle-2023-0069  

   Bamler, Richard H; Kleiner, Bruce (October 2023, Journal für die reine und angewandte Mathematik (Crelles Journal))

   Abstract LetXbe acompact orientable non-Haken 3-manifold modeled on the Thurston geometry $\mathrm{Nil}$ {\operatorname{Nil}}. We show that the diffeomorphism group $\mathrm{Diff}\left(X\right)$ {\operatorname{Diff}(X)}deformation retracts to the isometry group $\mathrm{Isom}\left(X\right)$ {\operatorname{Isom}(X)}. Combining this with earlier work by many authors, this completes the determination the homotopy type of $\mathrm{Diff}\left(X\right)$ {\operatorname{Diff}(X)}for any compact, orientable, prime 3-manifoldX. 

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5. Neutron skins: A perspective from dispersive optical models

   https://doi.org/10.3389/fphy.2024.1487314  

   Atkinson, M C; Dickhoff, W H (October 2024, Frontiers in Physics)

   Moreno, O (Ed.)

   An overview of neutron skin predictions obtained using an empirical nonlocal dispersive optical model (DOM) is presented. The DOM links both scattering and bound-state experimental data through a subtracted dispersion relation which allows for fully consistent, data-informed predictions for nuclei where such data exist. Large skins were predicted for both⁴⁸Ca ( $R_{\text{skin}}^{48}=0.25\pm 0.023$ fm in 2017) and²⁰⁸Pb ( $R_{\text{skin}}^{208}=0.25\pm 0.05$ fm in 2020). Whereas the DOM prediction in²⁰⁸Pb is within 1 $\sigma$ of the subsequent PREX-2 measurement, the DOM prediction in⁴⁸Ca is over 2 $\sigma$ larger than the thin neutron skin resulting from CREX. From the moment it was revealed, the thin skin in⁴⁸Ca has puzzled the nuclear-physics community as no adequate theories simultaneously predict both a large skin in²⁰⁸Pb and a small skin in⁴⁸Ca. The DOM is unique in its ability to treat both structure and reaction data on the same footing, providing a unique perspective on this $R_{\text{skin}}$ puzzle. It appears vital that more neutron data be measured in both the scattering and bound-state domain for⁴⁸Ca to clarify the situation. 

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