Researchers have derived many theoretical models for specifying users' insights as they interact with a visualization system. These representations are essential for understanding the insight discovery process, such as when inferring user interaction patterns that lead to insight or assessing the rigor of reported insights. However, theoretical models can be difficult to apply to existing tools and user studies, often due to discrepancies in how insight and its constituent parts are defined. This article calls attention to the consistent structures that recur across the visualization literature and describes how they connect multiple theoretical representations of insight. We synthesize a unified formalism for insights using these structures, enabling a wider audience of researchers and developers to adopt the corresponding models. Through a series of theoretical case studies, we use our formalism to compare and contrast existing theories, revealing interesting research challenges in reasoning about a user's domain knowledge and leveraging synergistic approaches in data mining and data management research.

Incentives have shown mixed results in increasing HIV testing rates in low-resource settings. We investigated the effectiveness of offering additional self-tests (HIVSTs) as an incentive to increase testing among partners receiving assisted partner services (APS). Western Kenya. We conducted a single-crossover study nested within a cluster-randomized controlled trial. Twenty-four facilities were randomized 1:1 to (1) control: provider-delivered testing or (2) intervention: offered 1 HIVST or provider-delivered testing for 6 months (pre-implementation), then switched to offering 2 HIVSTs for 6 months (post-implementation). A difference-in-differences approach using generalized linear mixed models, accounting for facility clustering and adjusting for age, sex, and income, was used to estimate the effect of the incentive on HIV testing and first-time testing among partners in APS. March 2021-June 2022, 1127 index clients received APS and named 8155 partners, among whom 2333 reported a prior HIV diagnosis and were excluded from analyses, resulting in 5822 remaining partners: 3646 (62.6%) and 2176 (37.4%) in the pre-implementation and post-implementation periods, respectively. Overall, 944/2176 partners (43%) were offered a second HIVST during post-preimplementation, of whom 34.3% picked up 2 kits, of whom 71.7% reported that the second kit encouraged HIV testing. Comparing partners offered 1 vs. two HIVSTs showed no difference in HIV testing (relative risk: 1.01, 95% confidence interval: 0.951 to 1.07) or HIV testing for the first time (relative risk: 1.23, 95% confidence interval: 0.671 to 2.24). Offering a second HIVST as an incentive within APS did not significantly impact HIV testing or first-time testing, although those opting for 2 kits reported it incentivized them to test.

Overactivity of the sympathetic nervous system is a hallmark of aging. The cellular mechanisms behind this overactivity remain poorly understood, with most attention paid to likely central nervous system components. In this work, we hypothesized that aging also affects the function of motor neurons in the peripheral sympathetic ganglia. To test this hypothesis, we compared the electrophysiological responses and ion-channel activity of neurons isolated from the superior cervical ganglia of young (12 weeks), middle-aged (64 weeks), and old (115 weeks) mice. These approaches showed that aging does impact the intrinsic properties of sympathetic motor neurons, increasing spontaneous and evoked firing responses. A reduction of M current emerged as a major contributor to age-related hyperexcitability. Thus, it is essential to consider the effect of aging on motor components of the sympathetic reflex as a crucial part of the mechanism involved in sympathetic overactivity.

To continue from our previous work [], we derive the full Standard Model prediction of the most general free neutron differential decay rate with all massive particles (neutron, proton, and electron) polarized, including the O(1/mN) recoil corrections and O(α/π) radiative corrections. For the latter we adopt the newly developed pseudoneutrino formalism which is compatible to realistic experimental setups, in which neutrinos and photons are not detected. We also provide readily executable notebooks to evaluate these corrections. Published by the American Physical Society 2024

Many anatomical and physiological features of cortical circuits, ranging from the biophysical properties of synapses to the connectivity patterns among different neuron types, exhibit consistent variation along the hierarchical axis from sensory to association areas. Notably, the scale of temporal correlation of neural activity at rest, known as the intrinsic timescale, increases systematically along this hierarchy in both primates and rodents, analogous to the growing scale and complexity of spatial receptive fields. However, how the timescales for task-related activity vary across brain regions and whether their hierarchical organization appears consistently across different mammalian species remain unexplored. Here, we show that both the intrinsic timescale and the timescales of task-related activity follow a similar hierarchical gradient in the cortices of monkeys, rats, and mice. We also found that these timescales covary similarly in both the cortex and basal ganglia, whereas the timescales of thalamic activity are shorter than cortical timescales and do not conform to the hierarchical order predicted by their cortical projections. These results suggest that the hierarchical gradient of cortical timescales might be a universal feature of intra-cortical circuits in the mammalian brain.

Deubiquitination is crucial for the proper functioning of numerous biological pathways, such as DNA repair, cell cycle progression, transcription, signal transduction and autophagy. Accordingly, pathogenic variants in deubiquitinating enzymes (DUBs) have been implicated in neurodevelopmental disorders and congenital abnormalities. ATXN7L3 is a component of the DUB module of the Spt-Ada-Gcn5 acetyltransferase (SAGA) complex and two other related DUB modules, and it serves as an obligate adaptor protein of three ubiquitin-specific proteases (USP22, USP27X or USP51). Through exome sequencing and by using GeneMatcher, we identified nine individuals with heterozygous variants in ATXN7L3. The core phenotype included global motor and language developmental delay, hypotonia and distinctive facial characteristics, including hypertelorism, epicanthal folds, blepharoptosis, a small nose and mouth, and low-set, posteriorly rotated ears. To assess pathogenicity, we investigated the effects of a recurrent nonsense variant [c.340C>T; p.(Arg114Ter)] in fibroblasts of an affected individual. ATXN7L3 protein levels were reduced, and deubiquitylation was impaired, as indicated by an increase in histone H2Bub1 levels. This is consistent with the previous observation of increased H2Bub1 levels in Atxn7l3-null mouse embryos, which have developmental delay and embryonic lethality. In conclusion, we present clinical information and biochemical characterization supporting ATXN7L3 variants in the pathogenesis of a rare syndromic neurodevelopmental disorder.

We present a new framework to measure the intrinsic properties of (deep) neural networks. While we focus on convolutional networks, our framework can be extrapolated to any network architecture. In particular, we evaluate two network properties, namely, capacity, which is related to expressivity, and compression, which is related to learnability. Both these properties depend only on the network structure and are independent of the network parameters. To this end, we propose two metrics: the first one, called layer complexity, captures the architectural complexity of any network layer; and, the second one, called layer intrinsic power, encodes how data are compressed along the network. The metrics are based on the concept of layer algebra, which is also introduced in this article. This concept is based on the idea that the global properties depend on the network topology, and the leaf nodes of any neural network can be approximated using local transfer functions, thereby allowing a simple computation of the global metrics. We show that our global complexity metric can be calculated and represented more conveniently than the widely used Vapnik-Chervonenkis (VC) dimension. We also compare the properties of various state-of-the-art architectures using our metrics and use the properties to analyze their accuracy on benchmark image classification datasets.