Background
Despite extensive literature on the role of breastfeeding in maternal and child health and cognitive development, few studies have systematically tested whether breastfeeding predicts ...children's socio‐emotional outcomes. The present study examined associations between trajectories of breastfeeding and observed parent–child interaction qualities of maternal sensitivity, child positivity, and child negativity from 6 months to 3 years of age.
Methods
Data were drawn from the NICHD Study of Early Child Care and Youth Development (n = 1306 US families). Hierarchical linear modelling accounted for demographic and early characteristics, including home environment, maternal depression, and observed global relationship quality.
Results
Breastfeeding was associated with increases in observed maternal sensitivity over time, even after the effects of demographic and early characteristics were controlled. Accounting for the covariates, breastfeeding was not associated with child behaviour (i.e. positivity, negativity) in mother–child interaction across early childhood.
Conclusions
Improved relationship quality, specifically through changes in maternal behaviour, may be another advantage experienced by breastfeeding mothers and children.
Quantum machine learning has experienced significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In ...this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique (Formula: see text) for embedding classical data into quantum states and estimating the inner product on the 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the Formula: see text encoding increases predictive performance with up to + 2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers.
Purpose
Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis ...(TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning.
Methods
Fifty-two patients who underwent multi-parametric dual-tracer
18
FFMC and
68
GaGa-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the
68
GaGa-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (M
LH
). Furthermore, M
BCR
and M
OPR
predictive model schemes were built by combining M
LH
, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional
68
GaGa-PSMA-11 standardized uptake value (SUV) analyses.
Results
The area under the receiver operator characteristic curve (AUC) of the M
LH
model (0.86) was higher than the AUC of the
68
GaGa-PSMA-11 SUV
max
analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the M
BCR
and M
OPR
models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively.
Conclusion
Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.
The glycome acts as an essential interface between cells and the surrounding microenvironment. However, changes in glycosylation occur in nearly all breast cancers, which can alter this interaction. ...Here, we report that profiles of glycosylation vary between ER-positive and ER-negative breast cancers. We found that genes involved in the synthesis of sialyl-Lewis x (sLe(x); FUT3, FUT4, and ST3GAL6) are significantly increased in estrogen receptor alpha-negative (ER-negative) tumors compared with ER-positive ones. SLe(x) expression had no influence on the survival of patients whether they had ER-negative or ER-positive tumors. However, high expression of sLe(x) in ER-positive tumors was correlated with metastasis to the bone where sLe(x) receptor E-selectin is constitutively expressed. The ER-positive ZR-75-1 and the ER-negative BT20 cell lines both express sLe(x) but only ZR-75-1 cells could adhere to activated endothelial cells under dynamic flow conditions in a sLe(x) and E-selectin-dependent manner. Moreover, L/P-selectins bound strongly to ER-negative MDA-MB-231 and BT-20 cell lines in a heparan sulfate (HS)-dependent manner that was independent of sLe(x) expression. Expression of glycosylation genes involved in heparan biosynthesis (EXT1 and HS3ST1) was increased in ER-negative tumors. Taken together, our results suggest that the context of sLe(x) expression is important in determining its functional significance and that selectins may promote metastasis in breast cancer through protein-associated sLe(x) and HS glycosaminoglycans.
Tritium, radiocarbon and radiocesium concentrations in water column samples in coastal waters offshore Fukushima and in the western North Pacific Ocean collected in 2011–2012 during the ...Ka'imikai-o-Kanaloa (KoK) cruise are compared with other published results. The highest levels in surface seawater were observed for 134Cs and 137Cs in seawater samples collected offshore Fukushima (up to 1.1 Bq L−1), which represent an increase by about three orders of magnitude when compared with the pre-Fukushima concentration. Tritium levels were much lower (up to 0.15 Bq L−1), representing an increase by about a factor of 6. The impact on the radiocarbon distribution was measurable, but the observed levels were only by about 9% above the global fallout background. The 137Cs (and similarly 134Cs) inventory in the water column of the investigated western North Pacific region was (2.7 ± 0.4) PBq, while for 3H it was only (0.3 ± 0.2) PBq. Direct releases of highly contaminated water from the damaged Fukushima NPP, as well as dry and wet depositions of these radionuclides over the western North Pacific considerably changed their distribution patterns in seawater. Presently we can distinguish Fukushima labeled waters from global fallout background thanks to short-lived 134Cs. However, in the long-term perspective when 134Cs will decay, new distribution patterns of 3H, 14C and 137Cs in the Pacific Ocean should be established for future oceanographic and climate change studies in the Pacific Ocean.
•Radiocesium, tritium and radiocarbon showed elevated levels in seawater of the western North Pacific.•The water column inventories of 137Cs and 3H were 2.7 ± 0.4 and 0.3 ± 0.2 PBq, respectively.•The radiocarbon levels were by about 9% above the global fallout background.•Released radionuclides will be useful in future oceanographic and climate studies.
Background
Cancer is a leading cause of death worldwide. While routine diagnosis of cancer is performed mainly with biopsy sampling, it is suboptimal to accurately characterize tumor heterogeneity. ...Positron emission tomography (PET)-driven radiomic research has demonstrated promising results when predicting clinical endpoints. This study aimed to investigate the added value of quantum machine learning both in simulator and in real quantum computers utilizing error mitigation techniques to predict clinical endpoints in various PET cancer patients.
Methods
Previously published PET radiomics datasets including 11C-MET PET glioma, 68GA-PSMA-11 PET prostate and lung 18F-FDG PET with 3-year survival, low-vs-high Gleason risk and 2-year survival as clinical endpoints respectively were utilized in this study. Redundancy reduction with 0.7, 0.8, and 0.9 Spearman rank thresholds (SRT), followed by selecting 8 and 16 features from all cohorts, was performed, resulting in 18 dataset variants. Quantum advantage was estimated by Geometric Difference (GD
Q
) score in each dataset variant. Five classic machine learning (CML) and their quantum versions (QML) were trained and tested in simulator environments across the dataset variants. Quantum circuit optimization and error mitigation were performed, followed by training and testing selected QML methods on the 21-qubit IonQ Aria quantum computer. Predictive performances were estimated by test balanced accuracy (BACC) values.
Results
On average, QML outperformed CML in simulator environments with 16-features (BACC 70% and 69%, respectively), while with 8-features, CML outperformed QML with + 1%. The highest average QML advantage was + 4%. The GD
Q
scores were ≤ 1.0 in all the 8-feature cases, while they were > 1.0 when QML outperformed CML in 9 out of 11 cases. The test BACC of selected QML methods and datasets in the IonQ device without error mitigation (EM) were 69.94% BACC, while EM increased test BACC to 75.66% (76.77% in noiseless simulators).
Conclusions
We demonstrated that with error mitigation, quantum advantage can be achieved in real existing quantum computers when predicting clinical endpoints in clinically relevant PET cancer cohorts. Quantum advantage can already be achieved in simulator environments in these cohorts when relying on QML.
To address the lack of a simple and standardized instrument to assess overall panic disorder severity, the authors developed a scale for the measurement of panic disorder severity.
Ten independent ...evaluators used the seven-item Panic Disorder Severity Scale to assess 186 patients with principal DSM-III-R diagnoses of panic disorder (with no or mild agoraphobia) who were participating in the Multicenter Collaborative Treatment Study of Panic Disorder. In addition, 89 of these patients were reevaluated with the same scale after short-term treatment. A subset of 24 patients underwent two independent assessments to establish interrater reliability. Internal consistency, convergent and discriminant validity, and sensitivity to change were also determined.
The Panic Disorder Severity Scale was associated with excellent interrater reliability, moderate internal consistency, and favorable levels of validity and sensitivity to change. Individual items showed good convergent and discriminant validity. Analysis suggested a two-factor model fit the data best.
The Panic Disorder Severity Scale is a simple, efficient way for clinicians to rate severity in patients with established diagnoses of panic disorder. However, further research with more diverse groups of panic disorder patients and with a broader range of convergent and discriminant validity measures is needed.
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