Saccadic latency in amblyopia.

We measured saccadic latencies in a large sample (total n = 459) of individuals with amblyopia or risk factors for amblyopia, e.g., strabismus or anisometropia, and normal control subjects. We presented an easily visible target randomly to the left or right, 3.5° from fixation. The interocular difference in saccadic latency is highly correlated with the interocular difference in LogMAR (Snellen) acuity-as the acuity difference increases, so does the latency difference. Strabismic and strabismic-anisometropic amblyopes have, on average, a larger difference between their eyes in LogMAR acuity than anisometropic amblyopes and thus their interocular latency difference is, on average, significantly larger than anisometropic amblyopes. Despite its relation to LogMAR acuity, the longer latency in strabismic amblyopes cannot be attributed either to poor resolution or to reduced contrast sensitivity, because their interocular differences in grating acuity and in contrast sensitivity are roughly the same as for anisometropic amblyopes. The correlation between LogMAR acuity and saccadic latency arises because of the confluence of two separable effects in the strabismic amblyopic eye-poor letter recognition impairs LogMAR acuity while an intrinsic sluggishness delays reaction time. We speculate that the frequent microsaccades and the accompanying attentional shifts, made while strabismic amblyopes struggle to maintain fixation with their amblyopic eyes, result in all types of reactions being irreducibly delayed

Tackling turbo consumption: An interview with Juliet Schor

Juliet Schor is Professor in Sociology at Boston College. She is author of The Overspent American, The Overworked American, and Born to Buy; co-editor of The Consumer Society Reader and Sustainable Planet; and co-founder of South End Press and The Center for a New American Dream, an organisation which ‘helps Americans consume responsibly to protect the environment, enhance quality of life, and promote social justice’. Jo Littler interviewed her about trends in contemporary consumerism and the issues involved in tackling its expansion

Table-driven Rules in Expert Systems

The structure and organization of expert systems can be usefully modeled after corresponding human experts. Often this modeling degrades because of insufficient expressive power in production system languages. Relational table techniques provide additional abstraction capabilities and are useful in extending the expressiveness of production system rules; the resulting systems can be easier to build, understand and debug because they can reflect more accurately human methods of reasoning. The number of superfluous rules is reduced by organizing much of the problem domain knowledge in relations in working memory. The relational table methods also provide a tool for the interfacing of knowledge bases and databases

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

Adsorption of the natural protein surfactant Rsn-2 onto liquid interfaces

To stabilize foams, droplets and films at liquid interfaces a range of protein biosurfactants have evolved in nature. Compared to synthetic surfactants, these combine surface activity with biocompatibility and low solution aggregation. One recently studied example is Rsn-2, a component of the foam nest of the frog Engystomops pustulosus, which has been predicted to undergo a clamshell-like opening transition at the air–water interface. Using atomistic molecular dynamics simulations and surface tension measurements we study the adsorption of Rsn-2 onto air–water and cyclohexane–water interfaces. The protein adsorbs readily at both interfaces, with adsorption mediated by the hydrophobic N-terminus. At the cyclohexane–water interface the clamshell opens, due to the favourable interaction between hydrophobic residues and cyclohexane molecules and the penetration of cyclohexane molecules into the protein core. Simulations of deletion mutants showed that removal of the N-terminus inhibits interfacial adsorption, which is consistent with the surface tension measurements. Deletion of the hydrophilic C-terminus also affects adsorption, suggesting that this plays a role in orienting the protein at the interface. The characterisation of the interfacial behaviour gives insight into the factors that control the interfacial adsorption of proteins, which may inform new applications of this and similar proteins in areas including drug delivery and food technology and may also be used in the design of synthetic molecules showing similar changes in conformation at interfaces

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

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

Vestibular afferent responses to linear accelerations in the alert squirrel monkey

The spontaneous activity of 40 otolith afferents and 44 canal afferents was recorded in 4 alert, intact squirrel monkeys. Polarization vectors and response properties of otolith afferents were determined during static re-orientations relative to gravity and during Earth-horizontal, sinusoidal, linear oscillations. Canal afferents were tested for sensitivity to linear accelerations. For regular otolith afferents, a significant correlation between upright discharge rate and sensitivity to dynamic acceleration in the horizontal plane was observed. This correlation was not present in irregular units. The sensitivity of otolith afferents to both static tilts and dynamic linear acceleration was much greater in irregularly discharging units than in regularly discharging units. The spontaneous activity and static and dynamic response properties of regularly discharging otolith afferents were similar to those reported in barbiturate-anesthetized squirrel monkeys. Irregular afferents also had similar dynamic response properties when compared to anesthetized monkeys. However, this sample of irregular afferents in alert animals had higher resting discharge rates and greater sensitivity to static tilts. The majority of otolith polarization vectors were oriented near the horizontal in the plane of the utricular maculae; however, directions of maximum sensitivity were different during dynamic and static testing. Canal afferents were not sensitive to static tilts or linear oscillations of the head

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