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1. Accuracy of Cranial Shape Measurements Using Smartphones: A Prospective Study

   https://doi.org/10.1177/10556656241271681  

   Kocabalkanli, Can; Fenton, Regina; Gaetani, Susan; Aalamifar, Fereshteh; Linguraru, Marius George; Seifabadi, Reza (September 2025, The Cleft Palate Craniofacial Journal)

   ObjectiveProspectively validate the accuracy of smartphone-based digital cranial measurements for the diagnosis and treatment of deformational plagiocephaly and/or brachycephaly (DPB), compared with calipers used in the standard of care. Design/MethodsBird's-eye-view head photos were captured via smartphone, and their heads were measured with hand calipers by an expert user. CI/CVAI/CVA were calculated from photos and caliper measurements, and from 3D photogrammetry of the head as ground truth. Digital and caliper measurements were compared against 3D-based ground truth using mean absolute error, Spearman correlation coefficient, and Bland-Altman method. Statistical significance between methods was assessed using Wilcoxon Rank-Sum test. Participants71 infants aged 2–11 months (20 female, 51 male) with DPB ResultsThe mean absolute errors for CI, CVAI, CVA were 1.63 ± 1.44, 1.45 ± 1.29, 2.38 ± 1.86 mm for smartphone, and 2.60 ± 1.96, 1.43 ± 1.22, 2.04 ± 1.81 mm for calipers, respectively. The correlation coefficients for CI, CVAI, CVA between smartphone and ground truth were 0.90, 0.94, 0.80 (p < 0.001), and 0.87, 0.93, 0.84 (p < 0.001) between calipers and ground truth, respectively. Bland-Altman results were (0.08, [−4.18, 4.34]), (−0.05, [−3.85, 3.76]), (−0.82, [−6.52, 4.87]) for smartphone, and (1.41, [−4.34, 7.15]), (0.28, [−3.37, 3.94]), (0.16, [−5.18, 5.49]) for caliper measurements respectively. Digital and caliper measurements were similar (p = 0.12) except for CI, where digital measurements were more accurate (p = 0.04). ConclusionSmartphone-based cranial measurements have very high correlation with 3D-based ground truth, and they are comparable or superior to caliper measurements. Digital measurements can be performed in pediatric offices or from home to help with the early detection and treatment of DPB. 

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2. T1 and T2 measurements across multiple 0.55T MRI systems using open‐source vendor‐neutral sequences

   https://doi.org/10.1002/mrm.30281  

   Keenan, Kathryn E; Tasdelen, Bilal; Javed, Ahsan; Ramasawmy, Rajiv; Rizzo, Rudy; Martin, Michele N; Stupic, Karl F; Seiberlich, Nicole; Campbell‐Washburn, Adrienne E; Nayak, Krishna S (January 2025, Magnetic Resonance in Medicine)

   Abstract PurposeTo compare T1 and T2 measurements across commercial and prototype 0.55T MRI systems in both phantom and healthy participants using the same vendor‐neutral pulse sequences, reconstruction, and analysis methods. MethodsStandard spin echo measurements and abbreviated protocol measurements of T1, B1, and T2 were made on two prototype 0.55 T systems and two commercial 0.55T systems using an ISMRM/NIST system phantom. Additionally, five healthy participants were imaged at each system using the abbreviated protocol for T1, B1, and T2 measurement. The phantom measurements were compared to NMR‐based reference measurements to determine accuracy, and both phantom and in vivo measurements were compared to assess reproducibility and differences between the prototype and commercial systems. ResultsVendor‐neutral sequences were implemented across all four systems, and the code for pulse sequences and reconstruction is freely available. For participants, there was no difference in the mean T1 and T2 relaxation times between the prototype and commercial systems. In the phantom, there were no significant differences between the prototype and commercial systems for T1 and T2 measurements using the abbreviated protocol. ConclusionQuantitative T1 and T2 measurements at 0.55T in phantom and healthy participants are not statistically different across the prototype and commercial systems. 

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3. Magellan/M2FS and MMT/Hectochelle Spectroscopy of Dwarf Galaxies and Faint Star Clusters within the Galactic Halo

   https://doi.org/10.5281/zenodo.7837922  

   Walker, Matthew G.; Caldwell, Nelson; Mateo, Mario; Olszewksi, Edward W.; Pace, Andrew B.; Bailey III, John I.; Koposov, Sergey E.; Roederer, Ian U. (January 2023, Zenodo)

   m2fs_HiRes_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the Magellan/M2FS spectrograph in HiRes configuration</p> m2fs_MedRes_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the Magellen/M2FS spectrograph in MedRes configuration</p> hecto_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the MMT/Hectochelle spectrograph</p> fits_files.tar.gz: Supplementary data products, including all sky-subtracted spectra from individual targets and best-fitting model spectra.</p> template_spectra.tar.gz: synthetic template spectra (columns are wavelength in air (Angstroms), normalized flux)</p> 

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4. Investigating the reliability of aggregate measurements of learning process data: From theory to practice

   https://doi.org/10.1111/jcal.12951  

   Zhang, Yingbin; Ye, Yafei; Paquette, Luc; Wang, Yibo; Hu, Xiaoyong (February 2024, Journal of Computer Assisted Learning)

   Abstract BackgroundLearning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an implicit assumption that such measurements are free of errors. ObjectivesThis study addresses these gaps by investigating the psychometric pros and cons of aggregate measurements. MethodsThis study proposes a framework for aggregating process data, which includes the conditions where aggregation is appropriate, and a guideline for selecting the proper reliability evidence and the computing procedure. We support and demonstrate the framework by analysing undergraduates' academic procrastination and programming proficiency in an introductory computer science course. Results and ConclusionAggregation over a period is acceptable and may improve measurement reliability only if the construct of interest is stable during the period. Otherwise, aggregation may mask meaningful changes in behaviours and should be avoided. While selecting the type of reliability evidence, a critical question is whether process data can be regarded as repeated measurements. Another question is whether the lengths of processes are unequal and individual events are unreliable. If the answer to the second question is no, segmenting each process into a fixed number of bins assists in computing the reliability coefficient. Major TakeawaysThe proposed framework can be a general guideline for aggregating process data in LA research. Researchers should check and report the reliability evidence for aggregate measurements before the ensuing interpretation. 

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5. Offset Between Profiling Float and Shipboard Oxygen Observations at Depth Imparts Bias on Float pH and Derived p CO ₂

   https://doi.org/10.1029/2024GB008185  

   Bushinsky, Seth M; Nachod, Zachary; Fassbender, Andrea J; Tamsitt, Veronica; Takeshita, Yuichiro; Williams, Nancy (May 2025, Global Biogeochemical Cycles)

   Abstract Profiles of oxygen measurements from Argo profiling floats now vastly outnumber shipboard profiles. To correct for drift, float oxygen data are often initially adjusted to deployment casts, ship‐based climatologies, or, recently, measurements of atmospheric oxygen for in situ calibration. Air calibration enables accurate measurements in the upper ocean but may not provide similar accuracy at depth. Using a quality controlled shipboard data set, we find that the entire Argo oxygen data set is offset relative to shipboard measurements (float minus ship) at pressures of 1,450–2,000 db by a median of −1.9 μmol kg⁻¹(mean ± SD of −1.9 ± 3.9, 95% confidence interval around the mean of {−2.2, −1.6}) and air‐calibrated floats are offset by −2.7 μmol kg⁻¹(−3.0 ± 3.4 (CI_95%{−3.7, −2.4}). The difference between float and shipboard oxygen is likely due to offsets in the float oxygen data and not oxygen changes at depth or biases in the shipboard data set. In addition to complicating the calculation of long‐term ocean oxygen changes, these float oxygen offsets impact the adjustment of float nitrate and pH measurements, therefore biasing important derived quantities such as the partial pressure of CO₂(pCO₂) and dissolved inorganic carbon. Correcting floats with air‐calibrated oxygen sensors for the float‐ship oxygen offsets alters float pH by a median of 3.0 mpH (3.1 ± 3.7) and float‐derived surfacepCO₂by −3.2 μatm (−3.2 ± 3.9). This adjustment to floatpCO₂represents half, or more, of the bias in float‐derivedpCO₂reported in studies comparing floatpCO₂to shipboardpCO₂measurements. 

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