Additivity of the Renyi entropy of order 2 for positive-partial-transpose-inducing channels

We prove that the minimal Renyi entropy of order 2 (RE2) output of a positive-partial-transpose(PPT)-inducing channel joint to an arbitrary other channel is equal to the sum of the minimal RE2 output of the individual channels. PPT-inducing channels are channels with a Choi matrix which is bound entangled or separable. The techniques used can be easily recycled to prove additivity for some non-PPT-inducing channels such as the depolarizing and transpose depolarizing channels, though not all known additive channels. We explicitly make the calculations for generalized Werner-Holevo channels as an example of both the scope and limitations of our techniques.Comment: 4 page

Stimulation by a low-molecular-weight angiogenic factor of capillary endothelial cells in culture.

A low-mol.-wt compound isolated from rat Walker 256 carcinoma and found to induce neovascularization in vivo was tested on cultures of cow brain-derived endothelial cells (CBEC) growing on plastic and collagen substrates. This factor had a mitogenic effect on CBEC cultured on native collagen gels and for this reason has been called "endothelial-cell-stimulating angiogenesis factor" (ESAF). CBEC growing on plastic culture dishes or denatured collagen films were not stimulated by ESAF. The mitogenic effect of ESAF was equally apparent when added to cells already attached to the native collagen substrate or when the collagen substrate was pre-incubated with ESAF before plating the cells. A floating collagen gel pre-incubated with ESAF in cultures of CBEC growing on plastic dishes did not stimulate cell growth. Our data indicate that the substrate influences cell behaviour and that CBEC only respond to ESAF when growing on a native collagen substrate

AI for predicting chemical-effect associations at the chemical universe level – deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods – even if high throughput – are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.Supplementary information Supplementary data are available at bioRxiv online.Contact jana.schor{at}ufz.deCompeting Interest StatementThe authors have declared no competing interest

Implicit learning increases shot accuracy of football players when making strategic decisions during penalty kicking

Implicit learning has been proposed to improve athletes’ performance in dual-task situations. Yet, only a few studies tested this with a sports-relevant dual-task. Hence, the current study aimed to compare the effects of implicit and explicit training methods on penalty kicking performance. Twenty skilled football players were divided in two training groups and took part in a practice phase to improve kicking accuracy (i.e., without a goalkeeper) and in a post-test in order to check penalty kick performance (i.e., accuracy including a decision to kick to the side opposite the goalkeeper's dive). Results found that the implicit and explicit training method resulted in similar levels of decision-making, but after implicit training this was achieved with higher kicking accuracy. Additionally, applications for football players and coaches are discussed

AI for predicting chemical-effect associations at the chemical universe level: DeepFPlearn

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn

Quality of Child Health: Expanding the Scope and Flexibility of Measurement Approaches

Proposes a measurement framework to make data collection on the quality of children's health care more efficient and comprehensive. Considerations include focusing on measures with the greatest potential impact and adding new content or methods

Superconducting crossed correlations in ferromagnets: implications for thermodynamics and quantum transport

It is demonstrated that non local Cooper pairs can propagate in ferromagnetic electrodes having an opposite spin orientation. In the presence of such crossed correlations, the superconducting gap is found to depend explicitly on the relative orientation of the ferromagnetic electrodes. Non local Cooper pairs can in principle be probed with dc-transport. With two ferromagnetic electrodes, we propose a ``quantum switch'' that can be used to detect correlated pairs of electrons. With three or more ferromagnetic electrodes, the Cooper pair-like state is a linear superposition of Cooper pairs which could be detected in dc-transport. The effect also induces an enhancement of the ferromagnetic proximity effect on the basis of crossed superconducting correlations propagating along domain walls.Comment: 4 pages, RevTe

Good practice in mental health care for socially marginalised groups in Europe: a qualitative study of expert views in 14 countries

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited