Prenatal intuitive coparenting behaviors

Micro-analytic research on intuitive parenting behaviors has shed light on the temporal dynamics of parent and child interactions. Observations have shown that parents possess remarkable implicit communicative abilities allowing them to adapt to the clues infants give and therefore stimulate the development of many of the infants' abilities, such as communication skills. This work focused on observing intuitive parenting behaviors that were synchronized and coordinated between the parents. We call them prenatal intuitive coparenting behaviors and used an observation task - the Prenatal Lausanne Trilogue Play procedure - to observe them. For this task, the parents role-play their first encounter with their future baby, represented by a doll. Two cases from a study on pregnancy after assisted reproductive technology are provided to illustrate how these behaviors manifest themselves. The observations from the first case suggest that expectant parents can offer the baby a coparental framework, whereas the observations from the second case show that opportunities for episodes of prenatal intuitive coparenting can be missed due to certain relationship dynamics.These kinds of observations deepen our knowledge of the prenatal emergence of the coparenting relationship and allow us to hone our strategies for intervening during pregnancy with couples who experience coparenting difficulties. Furthermore, these observations provide a novel and complementary perspective on prenatal intuitive parenting and coparenting behaviors

Integrating radiomics into holomics for personalised oncology: from algorithms to bedside.

Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up of personalised treatments has also become a major research topic in the area of precision medicine. In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning. Radiomics algorithms now include genomics and immunomics data to improve patient stratification and prediction of treatment response. Several applications have already shown conclusive results demonstrating the potential of including other "omics" data to existing imaging features. We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other "omics" into clinical workflows. In particular, we point to the emerging paradigm shift in the implementation of big data infrastructures to facilitate databanks growth, data extraction and the development of expert software tools. Secured access, sharing, and integration of all health data, called "holomics", will accelerate the revolution of personalised medicine and oncology as well as expand the role of imaging specialists

Learning features for tissue classification with the classification restricted Boltzmann machine

__Abstract__ Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convolutional classification RBM, a combination of the existing convolutional RBM and classification RBM, and use it for discriminative feature learning. We evaluate the classification accuracy of convolutional and non-convolutional classification RBMs on two lung CT problems. We find that RBM-learned features outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy.

18 F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) allows to quantify the metabolic activity of a tumor (glycolysis) and has become a reference tool in oncology for the staging, restaging, radiotherapy planning and monitoring response in many cancers. Quantitative analyses have been introduced in order to overcome some of the limits of the visual methods, allowing an easier and more objective comparison of the inter- and intra-patients variations. The aims of this review were to report available evidences on the clinical value of quantitative PET/CT parameters in HNC. Forty-five studies, for a total of 2928 patients, were analyzed. Most of the data available dealt with the intensity of the metabolism, calculated from the Standard Uptake Value (SUV). Metabolic Tumor Volume (MTV) was well correlated with overall survival and disease free survival, with a higher predictive value than the maximum SUV. Spatial distribution of metabolism and textural analyses seems promising

Signature of survival: a ¹⁸F-FDG PET based whole-liver radiomic analysis predicts survival after ⁹⁰Y-TARE for hepatocellular carcinoma.

To generate a predictive whole-liver radiomics scoring system for progression-free survival (PFS) and overall survival (OS) in patients undergoing transarterial radioembolization using Yttrium-90 ( <sup>90</sup> Y-TARE) for unresectable hepatocellular carcinoma (uHCC). The generated pPET-RadScores were significantly correlated with survival for PFS (median of 11.4 mo [95% confidence interval CI: 6.3-16.5 mo] in low-risk group [PFS-pPET-RadScore < 0.09] vs. 4.0 mo [95% CI: 2.3-5.7 mo] in high-risk group [PFS-pPET-RadScore > 0.09]; <i>P</i> = 0.0004) and OS (median of 20.3 mo [95% CI: 5.7-35 mo] in low-risk group [OS-pPET-RadScore < 0.11] vs. 7.7 mo [95% CI: 6.0-9.5 mo] in high-risk group [OS-pPET-RadScore > 0.11]; <i>P</i> = 0.007). The multivariate analysis confirmed PFS-pPET-RadScore ( <i>P</i> = 0.006) and OS-pPET-RadScore ( <i>P</i> = 0.001) as independent negative predictors. Pretreatment <sup>18</sup> F-FDG PET whole-liver radiomics signature appears as an independent negative predictor for PFS and OS in patients undergoing <sup>90</sup> Y-TARE for uHCC. Pretreatment <sup>18</sup> F-FDG PET of 47 consecutive patients undergoing <sup>90</sup> Y-TARE for uHCC (31 resin spheres, 16 glass spheres) were retrospectively analyzed. For each patient, based on PET radiomics signature from whole-liver semi-automatic segmentation, PFS and OS predictive PET-radiomics scores (pPET-RadScores) were obtained using LASSO Cox regression. Using X-tile software, the optimal score to predict PFS (PFS-pPET-RadScore) and OS (OS-pPET-RadScore) served as cutoff to separate high and low-risk patients. Survival curves were estimated using the Kaplan-Meier method. The prognostic value of PFS and OS-pPET-RadScore, Barcelona-Clinic Liver Cancer staging system and serum alpha-fetoprotein level was analyzed to predict PFS and OS in multivariate analysis

Family coordination in families who have a child with autism spectrum disorder

Little is known about the interactions of families where there is a child with autism spectrum disorder (ASD). The present study applies the Lausanne Trilogue Play (LTP) to explore both its applicability to this population as well as to assess resources and areas of deficit in these families. The sample consisted of 68 families with a child with ASD, and 43 families with a typically developing (TD) child. With respect to the global score for family coordination there were several negative correlations: the more severe the symptoms (based on the child’s ADOS score), the more family coordination was dysfunctional. This correlation was particularly high when parents had to play together with the child. In the parts in which only one of the parents played actively with the child, while the other was simply present, some families did achieve scores in the functional range, despite the child’s symptom severity. The outcomes are discussed in terms of their clinical implications both for assessment and for interventio

Nanoindentation and birefringence measurements on fused silica specimen exposed to low-energy femtosecond pulses,” Opt.

Abstract: Femtosecond laser pulses used in a regime below the ablation threshold have two noticeable effects on Fused Silica (a-SiO2): they locally increase the material refractive index and modify its HF etching selectivity. The nature of the structural changes induced by femtosecond laser pulses in fused silica is not fully understood. In this paper, we report on nanoindentation and birefringence measurements on fused silica exposed to low-energy femtosecond laser pulses. Our findings further back the hypothesis of localized densification effect even at low energy regime

Fusion Techniques in Biomedical Information Retrieval

For difficult cases clinicians usually use their experience and also the information found in textbooks to determine a diagnosis. Computer tools can help them supply the relevant information now that much medical knowledge is available in digital form. A biomedical search system such as developed in the Khresmoi project (that this chapter partially reuses) has the goal to fulfil information needs of physicians. This chapter concentrates on information needs for medical cases that contain a large variety of data, from free text, structured data to images. Fusion techniques will be compared to combine the various information sources to supply cases similar to an example case given. This can supply physicians with answers to problems similar to the one they are analyzing and can help in diagnosis and treatment planning

Bag-of-Colors for Biomedical Document Image Classification

The number of biomedical publications has increased noticeably in the last 30 years. Clinicians and medical researchers regularly have unmet information needs but require more time for searching than is usually available to find publications relevant to a clinical situation. The techniques described in this article are used to classify images from the biomedical open access literature into categories, which can potentially reduce the search time. Only the visual information of the images is used to classify images based on a benchmark database of ImageCLEF 2011 created for the task of image classification and image retrieval. We evaluate particularly the importance of color in addition to the frequently used texture and grey level features. Results show that bags–of–colors in combination with the Scale Invariant Feature Transform (SIFT) provide an image representation allowing to improve the classification quality. Accuracy improved from 69.75% of the best system in ImageCLEF 2011 using visual information, only, to 72.5% of the system described in this paper. The results highlight the importance of color for the classification of biomedical images