Secondary Metabolites from the Marine Sponge Genus Phyllospongia.

Phyllospongia, one of the most common marine sponges in tropical and subtropical oceans, has been shown to be a prolific producer of natural products with a broad spectrum of biological activities. This review for the first time provides a comprehensive overview of secondary metabolites produced by Phyllospongia spp. over the 37 years from 1980 to 2016

In vivo evidence for NMDA receptor mediated excitotoxicity in a murine genetic model of Huntington Disease

N-methyl-D-aspartate receptor (NMDAR) mediated excitotoxicity is implicated as a proximate cause of neurodegeneration in Huntington Disease (HD). However, this hypothesis has not been tested rigorously in vivo. NMDAR NR2B-subunits are the predominant NR2 subunit expressed by the striatal medium spiny neurons that degenerate in HD. To test this hypothesis, we crossed a well validated murine genetic model of HD (Hdh(CAG)150) with a transgenic line overexpressing NMDAR NR2B-subunits. In the resulting double mutant line, we show exacerbation of selective striatal neuron degeneration. These results provide the first direct in vivo evidence of NR2B-NMDAR mediated excitotoxicity in the context of HD. Our results are consistent with prior suggestions that direct and/or indirect interactions of mutant huntingtin with NMDARs are a proximate cause of neurodegeneration in HD

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.Comment: Accepted to CVPR 2018; Code and data available at https://www.github.com/richzhang/PerceptualSimilarit

Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings

Asymmetrical distance structures (quasimetrics) are ubiquitous in our lives and are gaining more attention in machine learning applications. Imposing such quasimetric structures in model representations has been shown to improve many tasks, including reinforcement learning (RL) and causal relation learning. In this work, we present four desirable properties in such quasimetric models, and show how prior works fail at them. We propose Interval Quasimetric Embedding (IQE), which is designed to satisfy all four criteria. On three quasimetric learning experiments, IQEs show strong approximation and generalization abilities, leading to better performance and improved efficiency over prior methods. Project Page: https://www.tongzhouwang.info/interval_quasimetric_embedding Quasimetric Learning Code Package: https://www.github.com/quasimetric-learning/torch-quasimetricComment: NeurIPS 2022 NeurReps Workshop Proceedings Trac

On the Learning and Learnablity of Quasimetrics

Our world is full of asymmetries. Gravity and wind can make reaching a place easier than coming back. Social artifacts such as genealogy charts and citation graphs are inherently directed. In reinforcement learning and control, optimal goal-reaching strategies are rarely reversible (symmetrical). Distance functions supported on these asymmetrical structures are called quasimetrics. Despite their common appearance, little research has been done on the learning of quasimetrics. Our theoretical analysis reveals that a common class of learning algorithms, including unconstrained multilayer perceptrons (MLPs), provably fails to learn a quasimetric consistent with training data. In contrast, our proposed Poisson Quasimetric Embedding (PQE) is the first quasimetric learning formulation that both is learnable with gradient-based optimization and enjoys strong performance guarantees. Experiments on random graphs, social graphs, and offline Q-learning demonstrate its effectiveness over many common baselines.Comment: Project page: https://ssnl.github.io/quasimetric/ Code: https://github.com/SsnL/poisson_quasimetric_embeddin

The influence of magnetite nano particles on the behavior of insulating oils for pulse power applications

The effects of the addition of magnetite nanoparticles on the breakdown strength of three insulating liquids have been examined. The liquids considered are: a mineral transformer oil; a synthetic ester liquid, Midel 7131, and a specialist high permittivity liquid for pulse power applications THESO. The expected increases in breakdown strength were observed in the mineral oil and synthetic ester liquids. However in the case of the high permittivity liquid no significant changes in the breakdown strength were observed. Possible explanations for the differences in the observed behavior for the THESO insulating liquid are discussed

Coping Strategies toward Food Security: A Case of Morogoro Region

This paper discusses the adequacy of off-farm coping strategies in enhancing food security for rural communities and support climate change adaptation. Using data from 2010 household cross-sectional survey in Morogoro, principle components and wealth index were performed to ascertain farmers’ characteristics and ability of the off-farm activities to meet food requirement for each individual household for the whole agricultural season. The findings revealed however, that, the ability of coping strategies from off-farming activities was limited in meeting food requirements throughout the year, hence rendering food insecurity to majority of farmers in the rural areas. Climate variability coping strategies are vital in increasing small holder farmers’ resilience to climate change and weather variability. With appropriate support from governments and development partners, farmers are encouraged to diversify to more viable farming and non-farming strategies so as to increase the chances of non-farm activities in decreasing the problem of food inadequacy. Keywords: Food security, Off-farm, Climate Variability, Coping strategies, Tanzani