A Preliminary Assessment of a Newly-Defined Multispectral Hue Space for Retrieving River Depth with Optical Imagery and In Situ Calibration Data

Bathymetry is a key element in the modeling of river systems for flood mapping, geomorphology, or stream habitat characterization. Standard practices rely on the interpolation of in situ depth measurements obtained with differential GPS or total station surveys, while more advanced techniques involve bathymetric LiDAR or acoustic soundings. However, these high-resolution active techniques are not so easily applied over large areas. Alternative methods using passive optical imagery present an interesting trade-off: they rely on the fact that wavelengths composing solar radiation are not attenuated at the same rates in water. Under certain assumptions, the logarithm of the ratio of radiances in two spectral bands is linearly correlated with depth. In this study, we go beyond these ratio methods in defining a multispectral hue that retains all spectral information. Given n coregistered bands, this spectral invariant lies on the (n−2)-sphere embedded in Rn−1, denoted Sn−2 and tagged ‘hue hypersphere’. It can be seen as a generalization of the RGB ‘color wheel’ (S1) in higher dimensions. We use this mapping to identify a hue-depth relation in a 35 km reach of the Garonne River, using high resolution (0.50 m) airborne imagery in four bands and data from 120 surveyed cross-sections. The distribution of multispectral hue over river pixels is modeled as a mixture of two components: one component represents the distribution of substrate hue, while the other represents the distribution of ‘deep water’ hue; parameters are fitted such that membership probability for the ‘deep’ component correlates with depth.

STUDY OF AN INTERMEDIATE AGE OPEN CLUSTER IC 1434 USING GROUND-BASED IMAGING AND GAIA DR2 ASTROMETRY

We present a detailed photometric and kinematical analysis of the poorly studied open cluster IC 1434 using CCD VRI, APASS, and Gaia DR2 database for the first time. By determining the membership probability of stars we identify the 238 most probable members with a probability higher than 60%; by using proper motion and parallax data as taken from the Gaia DR2 catalog. The mean proper motion of the cluster is obtained as μx=−3.89±0.19 and μy=−3.34±0.19 mas yr−1 in both the directions of right ascension and declination. The radial distribution of member stars provides the cluster extent as 7.6 arcmin. We estimate the interstellar reddening E(B−V) as 0.34 mag using the transformation equations from the literature. We obtain the values of cluster age and distance as 631±73 Myr and 3.2±0.1 kpc.

Exploring Decisional Conflict With Measures of Numeracy and Optimism in a Stated Preference Survey

Objectives Low optimism and low numeracy are associated with difficulty or lack of participation in making treatment-related health care decisions. We investigated whether low optimism and low self-reported numeracy scores could help uncover evidence of decisional conflict in a discrete-choice experiment (DCE). Methods Preferences for a treatment to delay type 1 diabetes were elicited using a DCE among 1501 parents in the United States. Respondents chose between two hypothetical treatments or they could choose no treatment (opt out) in a series of choice questions. The survey included a measure of optimism and a measure of subjective numeracy. We used latent class analyses where membership probability was predicted by optimism and numeracy scores. Results Respondents with lower optimism scores had a higher probability of membership in a class with disordered preferences ( P value for optimism coefficient = 0.032). Those with lower self-reported numeracy scores were more likely to be in a class with a strong preference for opting out and disordered preferences ( P = 0.000) or a class with a preference for opting out and avoiding serious treatment-related risks ( P = 0.015). Conclusions If respondents with lower optimism and numeracy scores are more likely to choose to opt out or have disordered preferences in a DCE, it may indicate that they have difficulty completing choice tasks.

Detection and Grading of Gliomas Using a Novel Two-Phase Machine Learning Method Based on MRI Images

The early detection and grading of gliomas is important for treatment decision and assessment of prognosis. Over the last decade numerous automated computer analysis tools have been proposed, which can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. In this paper, we used the gradient-based features extracted from structural magnetic resonance imaging (sMRI) images to depict the subtle changes within brains of patients with gliomas. Based on the gradient features, we proposed a novel two-phase classification framework for detection and grading of gliomas. In the first phase, the probability of each local feature being related to different types (e.g., diseased or healthy for detection, benign or malignant for grading) was calculated. Then the high-level feature representing the whole MRI image was generated by concatenating the membership probability of each local feature. In the second phase, the supervised classification algorithm was used to train a classifier based on the high-level features and patient labels of the training subjects. We applied this framework on the brain imaging data collected from Zhongnan Hospital of Wuhan University for glioma detection, and the public TCIA datasets including glioblastomas (WHO IV) and low-grade gliomas (WHO II and III) data for glioma grading. The experimental results showed that the gradient-based classification framework could be a promising tool for automatic diagnosis of brain tumors.

Updated parameters of 1743 open clusters based on Gaia DR2

ABSTRACT In this study, we follow up our recent paper (Monteiro et al. 2020) and present a homogeneous sample of fundamental parameters of open clusters in our Galaxy, entirely based on Gaia DR2 data. We used published membership probability of the stars derived from Gaia DR2 data and applied our isochrone fitting code, updated as in Monteiro et al. (2020), to GBP and GRPGaia DR2 data for member stars. In doing this, we take into account the nominal errors in the data and derive distance, age, and extinction of each cluster. This work therefore provides parameters for 1743 open clusters and, as a by-product, a list of likely not physical or dubious open clusters is provided as well for future investigations. Furthermore, it was possible to estimate the mean radial velocity of 831 clusters (198 of which are new and unpublished so far), using stellar radial velocities from Gaia DR2 catalogue. By comparing the open cluster distances obtained from isochrone fitting with those obtained from a maximum likelihood estimate of individual member parallaxes, we found a systematic offset of (−0.05 ± 0.04) mas.

Genetic and environmental factors predict multivariate trajectories of maternal distress after birth

Background: Maternal distress influences her own wellbeing and shapes her offspring’s psychosocial adjustment and neurodevelopment across childhood. The aim of this study was to analyze multivariate trajectories of maternal postpartum distress using a latent class modeling approach and to find genetic and psychosocial factors that predict membership within a given group (latent class).Methods: Maternal self-reports of depressive symptoms, parenting stress, general stress, and marital stress were measured at regular intervals during the first six years postpartum in 261 mothers participating in the Maternal Adversity, Vulnerability and Neurodevelopment Study. Genetic risk was determined by calculating a polygenic risk score for Major Depressive Disorder (MDD-PRS). Additionally, we assessed maternal history of early life adversity (mELA), educational level, and prenatal symptoms of depression as psychosocial risk factors. Using Latent Gold® Software, we identified latent classes of mothers based on their 1) average levels of distress and 2) change in distress over time.Results: We identified four latent classes based on average levels of distress and found that class membership probability was influenced by an interaction between MDD-PRS and prenatal depressive symptoms (WaldInteraction(3)=13.19, p=0.004; WaldMDD-PRS(3)=6.02, p=0.11; WaldDepression(3)=41.96; p<0.001), mELA (Wald(3)=8.64, p=0.035), and educational level (Wald(3)=11.61, p=0.009). Furthermore, we found five classes of mothers with distinct across- time trajectories, which were associated with mELA (Wald(3)=12.67, p=0.013).Conclusions: Our findings might become relevant in the clinical setting, e.g. for identifying pregnant women at risk for distress in the postpartum based on her prenatal symptoms of depression and genetic risk, mELA, and educational level.

Multi-colour photometry and Gaia EDR3 astrometry of two couples of binary clusters (NGC 5617 and Trumpler 22) and (NGC 3293 and NGC 3324).

This paper presents a comprehensive analysis of two pairs of binary clusters (NGC 5617 and Trumpler 22) and (NGC 3293 and NGC 3324) located in the fourth quadrant of our Galaxy. For this purpose we use different data taken from VVV survey, WISE, VPHAS, APASS, GLIMPSE along with Gaia EDR3 astrometric data. We identified 584, 429, 692 and 273 most probable cluster members with membership probability higher than $80 \%$ towards the region of clusters NGC 5617, Trumpler 22, NGC 3293 and NGC 3324. We estimated the value of $R=\frac{A_{V}}{E(B-V)}$ as ∼ 3.1 for clusters NGC 5617 and Trumpler 22, which indicates normal extinction law. The value of R ∼3.8 and ∼1.9 represent the abnormal extinction law towards the clusters NGC 3293 and NGC 3324. Our Kinematical analysis show that all these clusters have circular orbits. Ages are found to be 90 ± 10 and 12 ± 3 Myr for the cluster pairs (NGC 5617 and Trumpler 22) and (NGC 3293 and NGC 3324), respectively. The distances of 2.43 ± 0.08, 2.64 ± 0.07, 2.59 ± 0.1 and 2.80 ± 0.2 kpc estimated using parallax are alike to the values calculated by using the distance modulus. We have also identified 18 and 44 young stellar object candidates present in NGC 5617 and Trumpler 22, respectively. Mass function slopes are found to be in fair agreement with the Salpeter’s value. The dynamical study of these objects shows a lack of faint stars in their inner regions, which leads to the mass-segregation effect. Our study indicates that NGC 5617 and Trumpler 22 are dynamically relaxed but the other pair of clusters are not. Orbital alongwith the physical parameters show that the clusters in both pairs are physically connected.

Nationwide multicenter questionnaire surveys on countermeasures against antimicrobial resistance and infections in hospitals

Background The goals of the National Action Plan on Antimicrobial Resistance (AMR) of Japan include “implementing appropriate infection prevention and control” and “appropriate use of antimicrobials,” which are relevant to healthcare facilities. Specifically, linking efforts between existing infection control teams and antimicrobial stewardship programs was suggested to be important. Previous studies reported that human resources, such as full-time equivalents of infection control practitioners, were related to improvements in antimicrobial stewardship. Methods We posted questionnaires to all teaching hospitals (n = 1017) regarding hospital countermeasures against AMR and infections. To evaluate changes over time, surveys were conducted twice (1st survey: Nov 2016, 2nd survey: Feb 2018). A latent transition analysis (LTA) was performed to identify latent statuses, which refer to underlying subgroups of hospitals, and effects of the number of members in infection control teams per bed on being in the better statuses. Results The number of valid responses was 678 (response rate, 66.7%) for the 1st survey and 559 (55.0%) for the 2nd survey. More than 99% of participating hospitals had infection control teams, with differences in activity among hospitals. Roughly 70% had their own intervention criteria for antibiotics therapies, whereas only about 60 and 50% had criteria established for the use of anti-methicillin-resistant Staphylococcus aureus antibiotics and broad-spectrum antibiotics, respectively. Only 50 and 40% of hospitals conducted surveillance of catheter-associated urinary tract infections and ventilator-associated pneumonia, respectively. Less than 50% of hospitals used maximal barrier precautions for central line catheter insertion. The LTA identified five latent statuses. The membership probability of the most favorable status in the 2nd study period was slightly increased from the 1st study period (23.6 to 25.3%). However, the increase in the least favorable status was higher (26.3 to 31.8%). Results of the LTA did not support a relationship between increasing the number of infection control practitioners per bed, which is reportedly related to improvements in antimicrobial stewardship, and being in more favorable latent statuses. Conclusions Our results suggest the need for more comprehensive antimicrobial stewardship programs and increased surveillance activities for healthcare-associated infections to improve antimicrobial stewardship and infection control in hospitals.

Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations

This study shows the application of a system to monitor the state of damage of railway wheel steel specimens during rolling contact fatigue tests. This system can make continuous measurements with an evaluation of damage without stopping the tests and without destructive measurements. Four tests were carried out to train the system by recording torque and vibration data. Both statistical and spectral features were extracted from the sensors signals. A Principal Component Analysis (PCA) was performed to reduce the volume of the initial dataset; then, the data were classified with the k-means algorithm. The results were then converted into probabilities curves. Metallurgical investigations (optical micrographs, wear curves) and hardness tests were carried out to assess the trends of machine learning analysis. The training tests were used to train the proposed algorithm. Three validation tests were performed by using the real-time results of the k-means algorithm as a stop condition. Metallurgical analysis was performed also in this case. The validation tests follow the results of the training test and metallurgical analysis confirms the damage found with the machine learning analysis: when the membership probability of the cluster corresponding to the damage state reaches a value higher than 0.5, the metallurgical analysis clearly shows the cracks on the surface of the specimen due to the rolling contact fatigue (RCF) damage mechanism. These preliminary results are positive, even if reproduced on a limited set of specimens. This approach could be integrated in rolling contact fatigue tests to provide additional information on damage progression.

How Do Art Skills Influence Visual Search? – Eye Movements Analyzed With Hidden Markov Models

The results of two experiments are analyzed to find out how artistic expertise influences visual search. Experiment I comprised survey data of 1,065 students on self-reported visual memory skills and their ability to find three targets in four images of artwork. Experiment II comprised eye movement data of 50 Visual Literacy (VL) experts and non-experts whose eye movements during visual search were analyzed for nine images of artwork as an external validation of the assessment tasks performed in Sample I. No time constraint was set for completion of the visual search task. A latent profile analysis revealed four typical solution patterns for the students in Sample I, including a mainstream group, a group that completes easy images fast and difficult images slowly, a fast and erroneous group, and a slow working student group, depending on task completion time and on the probability of finding all three targets. Eidetic memory, performance in art education and visual imagination as self-reported visual skills have significant impact on latent class membership probability. We present a hidden Markov model (HMM) approach to uncover underlying regions of attraction that result from visual search eye-movement behavior in Experiment II. VL experts and non-experts did not significantly differ in task time and number of targets found but they did differ in their visual search process: compared to non-experts, experts showed greater precision in fixating specific prime and target regions, assessed through hidden state fixation overlap. Exploratory analysis of HMMs revealed differences between experts and non-experts in image locations of attraction (HMM states). Experts seem to focus their attention on smaller image parts whereas non-experts used wider parts of the image during their search. Differences between experts and non-experts depend on the relative saliency of targets embedded in images. HMMs can determine the effect of expertise on exploratory eye movements executed during visual search tasks. Further research on HMMs and art expertise is required to confirm exploratory results.