Ductus venosus agenesis and fetal malformations: what can we expect? - a systematic review of the literature

Background: The ductus venosus agenesis (DVA) is a rare condition with a variable prognosis that relies partly on the presence of associated conditions. The purpose of our study was to analyze the literature regarding the postnatal outcome of fetuses with DVA associated with fetal malformations, in order to discuss the best management options for couples. Methods: We performed a systematic review of the literature of MEDLINE and SCOPUS electronic databases in a 25-year period from 1992 to September 2017. Results: We found 340 cases of DVA associated with fetal abnormalities. The most common chromosomal abnormalities were: monosomy X (12/48, 25%), trisomy 21 (11/48, 22.9%) and trisomy 18 (6/48, 12.5%). From the 340 cases with DVA, in 31 cases the umbilical venous shunt type was not reported. Of the fetuses, 60.8% (188/309) had an extrahepatic umbilical venous drainage while 39.2% (121/309) presented an intrahepatic connection. The DVA was associated in 71 cases (23.0%) with cardiac abnormalities, in 82 cases (26.5%) with extracardiac abnormalities and in 85 cases (27.5%) with both cardiac and extracardiac abnormalities. Conclusion: DVA associated with both cardiac and extracardiac malformations may confer a poorer fetal outcome, a clinically relevant fact that should clarify what can be expected from this entity and help prenatal counseling

Evidence for entanglement at high temperatures in an engineered molecular magnet

The molecular compound [Fe$_{2}$($\mu_{2}$-oxo)(C$_{3}$H$_{4}$N$_{2}$)$_{6}$(C$_{2}$O$_{4}$)$_{2}$] was designed and synthesized for the first time and its structure was determined using single-crystal X-ray diffraction. The magnetic susceptibility of this compound was measured from 2 to 300 K. The analysis of the susceptibility data using protocols developed for other spin singlet ground-state systems indicates that the quantum entanglement would remain at temperatures up to 732 K, significantly above the highest entanglement temperature reported to date. The large gap between the ground state and the first-excited state (282 K) suggests that the spin system may be somewhat immune to decohering mechanisms. Our measurements strongly suggest that molecular magnets are promising candidate platforms for quantum information processing

Experimental evaluation of an interleaved boost topology optimized for peak power tracking control

This paper provides an experimental evaluation of a four phase Floating Interleaved Boost Converter for a photovoltaic power system application. This converter offers improved efficiency and voltage gain, while having lower input current ripple than other DC-DC boost converters. A dual loop, discrete, linear feedback was developed to regulate inductor currents and output capacitor voltages. Maximum Power Point Tracking capability was included. Results of all control functions were used to validate the control development, and point to areas for further improvement

Singular value decomposition and matrix reorderings in quantum information theory

We review Schmidt and Kraus decompositions in the form of singular value decomposition using operations of reshaping, vectorization and reshuffling. We use the introduced notation to analyse the correspondence between quantum states and operations with the help of Jamiolkowski isomorphism. The presented matrix reorderings allow us to obtain simple formulae for the composition of quantum channels and partial operations used in quantum information theory. To provide examples of the discussed operations we utilize a package for the Mathematica computing system implementing basic functions used in the calculations related to quantum information theory.Comment: 11 pages, no figures, see http://zksi.iitis.pl/wiki/projects:mathematica-qi for related softwar

Theoretical analysis and control results for the Fitzhugh-Nagumo equation

In this paper we are concerned with some theoretical questions for the FitzHugh-Nagumo equation. First, we recall the system, we briefly explain the meaning of the variables and we present a simple proof of the existence and uniqueness of strong solution. We also consider an optimal control problem for this system. In this context, the goal is to determine how can we act on the system in order to get good properties. We prove the existence of optimal state-control pairs and, as an application of the Dubovitski-Milyoutin formalism, we deduce the corresponding optimality system. We also connect the optimal control problem with a controllability question and we construct a sequence of controls that produce solutions that converge strongly to desired states. This provides a strategy to make the system behave as desired. Finally, we present some open questions related to the control of this equation

Fully convolutional neural networks for polyp segmentation in colonoscopy

Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer, accounting for nearly 10% of all forms of cancer in the world. Even though colonoscopy is considered the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the operator skills and level of hand-eye coordination. In this work, we propose to adapt fully convolution neural networks (FCN), to identify and segment polyps in colonoscopy images. We converted three established networks into a fully convolution architecture and fine-tuned their learned representations to the polyp segmentation task. We validate our framework on the 2015 MICCAI polyp detection challenge dataset, surpassing the state-of-the-art in automated polyp detection. Our method obtained high segmentation accuracy and a detection precision and recall of 73.61% and 86.31%, respectively

Rapid mixing implies exponential decay of correlations

We provide an analysis of the correlation properties of spin and fermionic systems on a lattice evolving according to open system dynamics generated by a local primitive Liouvillian. We show that if the Liouvillian has a spectral gap which is independent of the system size, then the correlations between local observables decay exponentially as a function of the distance between their supports. We prove, furthermore, that if the Log-Sobolev constant is independent of the system size, then the system satisfies clustering of correlations in the mutual information - a much more stringent form of correlation decay. As a consequence, in the latter case we get an area law (with logarithmic corrections) for the mutual information. As a further corollary, we obtain a stability theorem for local distant perturbations. We also demonstrate that gapped free-fermionic systems exhibit clustering of correlations in the covariance and in the mutual information. We conclude with a discussion of the implications of these results for the classical simulation of open quantum systems with matrix-product operators and the robust dissipative preparation of topologically ordered states of lattice spin systems.Comment: 25 pages, 2 figures, replaced by final versio

Plant mixtures and soil management in the melon crop.

Melon (Cucumis melo L.) is a crop economically important for Brazil and for other countries, making it the eighth most produced fruit in the world and the third most fresh fruit exported from Brazil. However, soil tillage for cultivation involves the use of plowing, disking and ridging preparation to the realization of planting. That practices accelerates the process of soil degradation, decreasing soil organic matter and favoring the salinization process. Conservation practices such as the use of green manures and no tillage, are incorporated into the concept of systems to compose a low carbon emission. To adjust a technological model of tillage of melon plant for the Brazilian semiarid system and to compose the crops rotation systems of this vegetable, are being conducted long-term experiments, using plant mixtures as green manures and two tillage systems of soil. This study aimed to monitoring the impact of green manure crops in the form of plant mixtures, and soil management on productivity and fruit quality of melon. The initial stage of the experiment allows us to infer that the cultivation of plant mixtures did not change the productivity and fruit quality though, in the second year of cultivation, management factor affecting the productivity

Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks

Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC), and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), fine-tune them and study their capabilities for polyp segmentation and detection. We additionally use shape-from-shading (SfS) to recover depth and provide a richer representation of the tissue’s structure in colonoscopy images. Depth is incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation interception over union (IU) of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state-of-the-art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the first work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance