What To Expect Before You\u27re Expecting: Clarifying Florida\u27s Statute Governing Pre-Embryo Disposition Agreements And Divorce

In 2013, Sofia Vergara, actress and star of the hit television show Modern Family, and her then fiance\u27 Nick Loeb, a Florida businessman, sought to conceive a child through in vitro fertilization (IVF) - a process by which an egg and sperm are fertilized outside of the body, and the resulting pre-embryo is implanted in the uterus with the objective of development to full-term

An Individual's Connection to Nature Can Affect Perceived Restorativeness of Natural Environments : Some Observations about Biophilia

This study investigates the relationship between the level to which a person feels connected to Nature and that person's ability to perceive the restorative value of a natural environment. We assume that perceived restorativeness may depend on an individual's connection to Nature and this relationship may also vary with the biophilic quality of the environment, i.e., the functional and aesthetic value of the natural environment which presumably gave an evolutionary advantage to our species. To this end, the level of connection to Nature and the perceived restorativeness of the environment were assessed in individuals visiting three parks characterized by their high level of "naturalness" and high or low biophilic quality. The results show that the perceived level of restorativeness is associated with the sense of connection to Nature, as well as the biophilic quality of the environment: individuals with different degrees of connection to Nature seek settings with different degrees of restorativeness and biophilic quality. This means that perceived restorativeness can also depend on an individual's "inclination" towards Nature

Towards Uncovering Feature Extraction from Temporal Signals in Deep CNN: The ECG Case Study

Despite all the progress made in biomedical field, the Electrocardiogram (ECG) is still one of the most commonly used signal in medical examinations. Over the years, the problem of ECG classification has been approached in many different ways, most of which rely on the extraction of features from the signal in the form of temporal or morphological characteristics. Although feature engineering can led to adequately good results, it mostly relies on human ability and experience in selecting the correct feature set. In the last decade, a growing class of techniques based on Convolutional Neural Network (CNN) has been proposed in opposition to feature engineering. The efficiency and accuracy of CNN-based approaches is indisputable, however their ability in extracting and using temporal features from raw signal is poorly understood. The main objective of this work was to uncover the differences and the relationships between CNN feature maps and human-curated temporal features, towards a deeper understanding of neural-based approaches for ECG. In fact, the proposed study succeeded in finding a similarity between the output stage of the first layers of a deep 1D-CNN with several temporal features, demonstrating that not only that the engineered features effectively works in ECG classification tasks, but also that CNN can improve those features by elaborating them towards an higher level of abstraction

Neural Biclustering in Gene Expression Analysis

Clustering in high dimensional spaces is a very difficult task. Dealing with DNA microarrays is even more difficult because gene subsets are coregulated and coexpressed only under specific conditions. Biclusterng addresses the problem of finding such submanifolds by exploiting both gene and condition (tissue) clustering. The paper proposes a self-organizing neural network, GH EXIN, which builds a hierarchical tree by adapting its architecture to data. It is integrated in a framework in which gene and tissue clustering are alternated and controlled by the quality of the bicluster. Examples of the approach and a biological validation of results are also given

Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

International audienceModel Output Statistics (MOS) refers to a method of post-processing the direct outputs of numerical weather prediction (NWP) models in order to reduce the biases introduced by a coarse horizontal resolution. This technique is especially useful in orographically complex regions, where large differences can be found between the NWP elevation model and the true orography. This study carries out a comparison of linear and non-linear MOS methods, aimed at the prediction of minimum temperatures in a fruit-growing region of the Italian Alps, based on the output of two different NWPs (ECMWF T511?L60 and LAMI-3). Temperature, of course, is a particularly important NWP output; among other roles it drives the local frost forecast, which is of great interest to agriculture. The mechanisms of cold air drainage, a distinctive aspect of mountain environments, are often unsatisfactorily captured by global circulation models. The simplest post-processing technique applied in this work was a correction for the mean bias, assessed at individual model grid points. We also implemented a multivariate linear regression on the output at the grid points surrounding the target area, and two non-linear models based on machine learning techniques: Neural Networks and Random Forest. We compare the performance of all these techniques on four different NWP data sets. Downscaling the temperatures clearly improved the temperature forecasts with respect to the raw NWP output, and also with respect to the basic mean bias correction. Multivariate methods generally yielded better results, but the advantage of using non-linear algorithms was small if not negligible. RF, the best performing method, was implemented on ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC were approximately 1.2°C, close to the natural variability inside the area itself

A survey on data integration for multi-omics sample clustering

Due to the current high availability of omics, data-driven biology has greatly expanded, and several papers have reviewed state-of-the-art technologies. Nowadays, two main types of investigation are available for a multi-omics dataset: extraction of relevant features for a meaningful biological interpretation and clustering of the samples. In the latter case, a few reviews refer to some outdated or no longer available methods, whereas others lack the description of relevant clustering metrics to compare the main approaches. This work provides a general overview of the major techniques in this area, divided into four groups: graph, dimensionality reduction, statistical and neural-based. Besides, eight tools have been tested both on a synthetic and a real biological dataset. An extensive performance comparison has been provided using four clustering evaluation scores: Peak Signal-to-Noise Ratio (PSNR), Davies-Bouldin(DB) index, Silhouette value and the harmonic mean of cluster purity and efficiency. The best results were obtained by using the dimensionality reduction, either explicitly or implicitly, as in the neural architecture

Violation of Cluster Decomposition and Absence of Light-Cones in Local Integer and Half-Integer Spin Chains

We compute the ground-state correlation functions of an exactly solvable chain of integer spins, recently introduced in [R. Movassagh and P. W. Shor, arXiv: 1408.1657], whose ground state can be expressed in terms of a uniform superposition of all colored Motzkin paths. Our analytical results show that for spin s >= 2 there is a violation of the cluster decomposition property. This has to be contrasted with s = 1, where the cluster property holds. Correspondingly, for s = 1 one gets a light-cone profile in the propagation of excitations after a local quench, while the cone is absent for s = 2, as shown by time dependent density-matrix renormalization group. Moreover, we introduce an original solvable model of half-integer spins, which we refer to as Fredkin spin chain, whose ground state can be expressed in terms of superposition of all Dyck paths. For this model we exactly calculate the magnetization and correlation functions, finding that for s = 1/2, a conelike propagation occurs, while for higher spins, s >= 3/2, the colors prevent any cone formation and clustering is violated, together with square root deviation from the area law for the entanglement entropy. \ua9 2016 American Physical Society

Percutaneous Trans-Thoracic Procedures in Children With Tumors of Thoracic Wall, Mediastinum and Lung. The Experience of a Single Institution

Background While percutaneous trans-thoracic procedures (PTTP) are commonly performed in adults with tumors of thoracic wall, mediastinum and lung, the experience is limited in children, in whom however less invasive methods should be the choice for the diagnosis or the identification of small pulmonary nodules that need to be removed, sparing lung tissue. The results of the PTTP performed by the interventional radiologists in our Pediatric Surgery Department are analyzed. Methods CT-guided biopsies, utilizing a 64-slice CTscanner, with low-radiation dose, were performed applying the coaxial technique with 16-18G needles with a single tissue path. For localization of lung nodules before surgery, two 20G-hook wires were positioned beyond the nodule. CT images after each manipulation of the needles were obtained. US-guided biopsies were performed either with or without coaxial technique through a needle bracket. Younger patients required sedation. All patients underwent a chest radiogram two hours after the procedure and remained under observation for 24 hours. Results From January 2015 to March 2019, 23 procedures were performed in 22 patients (Age:16M- 19Y): 6 patients underwent CT-guided biopsy (4 lung nodules, 2 mediastinal mass); 3 underwent 4 CT-guided hook-wire localization of pulmonary nodules, just before surgery; 13 underwent US-guided biopsy (posterior mediastinum 2; anterior mediastinum 5, thoracic/intrathoracic mass 5). Adequate core biopsies were obtained in all patients, except three, who underwent thoracoscopy/thoracotomy. The hook-wires were successfully positioned in all cases, as confirmed by histology. After the procedure, two patients presented perilesional hemorrhage and one pneumothorax, but they did not required treatment. Conclusion PTTP were successful in most patients, without significant complications. These techniques should be encouraged to avoid diagnostic aggressive surgical approaches in children with cancer. For all cases a multidisciplinary team is essential to discuss the indications and planning the procedures