Bilateral acute angle closure glaucoma precipitated by over the counter oral decongestant

Letter to the edito

Vector trace cells in the subiculum of the hippocampal formation

Successfully navigating in physical or semantic space requires a neural representation of allocentric (map-based) vectors to boundaries, objects and goals. Cognitive processes such as path-planning and imagination entail the recall of vector representations, but evidence of neuron-level memory for allocentric vectors has been lacking. Here, we describe a novel neuron type, vector trace cell (VTC), whose firing generates a new vector field when a cue is encountered and a ‘trace’ version of that field for hours after cue removal. VTCs are concentrated in subiculum, distal to CA1. Compared to non-trace cells, VTCs fire at further distances from cues and exhibit earlier-going shifts in preferred theta phase in response to newly introduced cues, which demonstrates a theta-linked neural substrate for memory encoding. VTCs suggest a vector-based model of computing spatial relationships between an agent and multiple spatial objects, or between different objects, freed from the constraints of direct perception of those objects

GEOLOGICAL INVESTIGATION OF PALAEOTSUNAMIS IN THE SAMOAN ISLANDS: INTERIM REPORT AND RESEARCH DIRECTIONS

The September 29, 2009 Samoa Tsunami provided the opportunity to sample the sediments deposited in the Samoan Islands landscape by the tsunami. Analysing the characteristics of the sediment deposits using an established suite of diagnostic criteria, and assessing how they differ from cyclone deposits enables the identification and dating of similar events in the geologic record. This helps to better understand the long-term frequency and likely magnitude of these events. Here we report on a pilot palaeotsunami field-sampling investigation carried out in 2010 at selected sites on Upolu and Savaii Islands in the Independent State of Samoa, and on Ta’u Island in American Samoa. We present empirical stratigraphic data for the investigated sites, and we demonstrate the existence of high energy marine inundation deposits at some of these sites which were laid down by past tsunamis and/or cyclones. We review and discuss the analytical outcomes, as well as summarise the overarching directions of this research. We propose that there is a need for this study to continue and for such studies to be carried out in other islands in the Pacific. By doing this, we can build on the sparse palaeotsunami database in the region, thereby helping to improve our understanding of the long-term frequency, impact distribution, and likely magnitude of these events. Further, we can start assessing their likely sources and the long-term risk these hazards pose to coastal cities and communities in the Pacific

Extracting the rho meson wavefunction from HERA data

We extract the light-cone wavefunctions of the rho meson using the HERA data on diffractive rho photoproduction. We find good agreement with predictions for the distribution amplitude based on QCD sum rules and from the lattice. We also find that the data prefer a transverse wavefunction with enhanced end-point contributions.Comment: 13 pages, 7 figures, significant improvements over the original version with a new section on distribution amplitudes adde

Reconceptualizing Indigeneity Within the Fraternity and Sorority Community

The purpose of this article provides fraternity and sorority communities with an understanding of the impact of a Native American sisterhood on the cultivation in how it conceptualized itself, its centering an Indigenous structure, and how it affirms the women who join through building relationality. Through the first research inquiry of a Native American sisterhood, the findings reveal/highlight how the sisterhood has centered Indigeneity in its conception to how it lives today. Fraternity and sorority communities need to shift their culture to honor Indigenous ways of being

Implementing the Flinders Model of self-management support with Aboriginal people who have diabetes: findings from a pilot study

Programs to increase patients' capacity to manage their chronic disease are growing in popularity with policy makers, health professionals and the general public. However, until this pilot in regional South Australia, Indigenous people rarely participated in such programs. The pilot included extensive consultations with the Indigenous community, ownership of the program by an Aboriginal community-controlled health service, and a key role for Aboriginal and Torres Strait Islander Health Workers as the main coordinators of self-management support. The result was that 60 Aboriginal people participated and achieved notable improvements in health outcomes and personal goals. This pilot demonstrates that mainstream programs are relevant for Aboriginal communities as long as Aboriginal people lead the adaptation process

A comparative study between motivated learning and reinforcement learning

This paper analyzes advanced reinforcement learning techniques and compares some of them to motivated learning. Motivated learning is briefly discussed indicating its relation to reinforcement learning. A black box scenario for comparative analysis of learning efficiency in autonomous agents is developed and described. This is used to analyze selected algorithms. Reported results demonstrate that in the selected category of problems, motivated learning outperformed all reinforcement learning algorithms we compared with

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials