Immunogene therapy with fusogenic nanoparticles modulates macrophage response to Staphylococcus aureus.

The incidence of adverse effects and pathogen resistance encountered with small molecule antibiotics is increasing. As such, there is mounting focus on immunogene therapy to augment the immune system's response to infection and accelerate healing. A major obstacle to in vivo gene delivery is that the primary uptake pathway, cellular endocytosis, results in extracellular excretion and lysosomal degradation of genetic material. Here we show a nanosystem that bypasses endocytosis and achieves potent gene knockdown efficacy. Porous silicon nanoparticles containing an outer sheath of homing peptides and fusogenic liposome selectively target macrophages and directly introduce an oligonucleotide payload into the cytosol. Highly effective knockdown of the proinflammatory macrophage marker IRF5 enhances the clearance capability of macrophages and improves survival in a mouse model of Staphyloccocus aureus pneumonia

Workshop on evaluating personal search

The first ECIR workshop on Evaluating Personal Search was held on 18th April 2011 in Dublin, Ireland. The workshop consisted of 6 oral paper presentations and several discussion sessions. This report presents an overview of the scope and contents of the workshop and outlines the major outcomes

Star formation in W3 - AFGL333: Young stellar content, properties and roles of external feedback

One of the key questions in the field of star formation is the role of stellar feedback on subsequent star formation process. The W3 giant molecular cloud complex at the western border of the W4 super bubble is thought to be influenced by the stellar winds of the massive stars in W4. AFGL333 is a ~10^4 Msun cloud within W3. This paper presents a study of the star formation activity within AFGL333 using deep JHKs photometry obtained from the NOAO Extremely Wide-Field Infrared Imager combined with Spitzer-IRAC-MIPS photometry. Based on the infrared excess, we identify 812 candidate young stellar objects in the complex, of which 99 are classified as Class I and 713 are classified as Class II sources. The stellar density analysis of young stellar objects reveals three major stellar aggregates within AFGL333, named here AFGL333-main, AFGL333-NW1 and AFGL333-NW2. The disk fraction within AFGL333 is estimated to be ~50-60%. We use the extinction map made from the H-Ks colors of the background stars to understand the cloud structure and to estimate the cloud mass. The CO-derived extinction map corroborates the cloud structure and mass estimates from NIR color method. From the stellar mass and cloud mass associated with AFGL333, we infer that the region is currently forming stars with an efficiency of ~4.5% and at a rate of ~2 - 3 Msun Myr-1pc-2. In general, the star formation activity within AFGL333 is comparable to that of nearby low mass star-forming regions. We do not find any strong evidence to suggest that the stellar feedback from the massive stars of nearby W4 super bubble has affected the global star formation properties of the AFGL333 region.Comment: 17 pages, 9 figures, Accepted for publication in Ap

Self-positioning Point-based Transformer for Point Cloud Understanding

Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to directly apply Transformers on point clouds due to their quadratic cost in the number of points. In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity. Specifically, this architecture consists of local self-attention and self-positioning point-based global cross-attention. The self-positioning points, adaptively located based on the input shape, consider both spatial and semantic information with disentangled attention to improve expressive power. With the self-positioning points, we propose a novel global cross-attention mechanism for point clouds, which improves the scalability of global self-attention by allowing the attention module to compute attention weights with only a small set of self-positioning points. Experiments show the effectiveness of SPoTr on three point cloud tasks such as shape classification, part segmentation, and scene segmentation. In particular, our proposed model achieves an accuracy gain of 2.6% over the previous best models on shape classification with ScanObjectNN. We also provide qualitative analyses to demonstrate the interpretability of self-positioning points. The code of SPoTr is available at https://github.com/mlvlab/SPoTr.Comment: Accepted paper at CVPR 202

Sleep, mood disorders, and the ketogenic diet: potential therapeutic targets for bipolar disorder and schizophrenia

Bipolar disorder and schizophrenia are serious psychiatric conditions that cause a significant reduction in quality of life and shortened life expectancy. Treatments including medications and psychosocial support exist, but many people with these disorders still struggle to participate in society and some are resistant to current therapies. Although the exact pathophysiology of bipolar disorder and schizophrenia remains unclear, increasing evidence supports the role of oxidative stress and redox dysregulation as underlying mechanisms. Oxidative stress is an imbalance between the production of reactive oxygen species generated by metabolic processes and antioxidant systems that can cause damage to lipids, proteins, and DNA. Sleep is a critical regulator of metabolic homeostasis and oxidative stress. Disruption of sleep and circadian rhythms contribute to the onset and progression of bipolar disorder and schizophrenia and these disorders often coexist with sleep disorders. Furthermore, sleep deprivation has been associated with increased oxidative stress and worsening mood symptoms. Dysfunctional brain metabolism can be improved by fatty acid derived ketones as the brain readily uses both ketones and glucose as fuel. Ketones have been helpful in many neurological disorders including epilepsy and Alzheimer’s disease. Recent clinical trials using the ketogenic diet suggest positive improvement in symptoms for bipolar disorder and schizophrenia as well. The improvement in psychiatric symptoms from the ketogenic diet is thought to be linked, in part, to restoration of mitochondrial function. These findings encourage further randomized controlled clinical trials, as well as biochemical and mechanistic investigation into the role of metabolism and sleep in psychiatric disorders. This narrative review seeks to clarify the intricate relationship between brain metabolism, sleep, and psychiatric disorders. The review will delve into the initial promising effects of the ketogenic diet on mood stability, examining evidence from both human and animal models of bipolar disorder and schizophrenia. The article concludes with a summary of the current state of affairs and encouragement for future research focused on the role of metabolism and sleep in mood disorders

Deformable Graph Transformer

Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically sampled relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves state-of-the-art performance on 7 graph benchmark datasets with 2.5 - 449 times less computational cost compared to transformer-based graph models with full attention.Comment: 16 pages, 3 figure

IN-SYNC. V. Stellar kinematics and dynamics in the Orion A Molecular Cloud

The kinematics and dynamics of young stellar populations enable us to test theories of star formation. With this aim, we continue our analysis of the SDSS-III/APOGEE IN-SYNC survey, a high resolution near infrared spectroscopic survey of young clusters. We focus on the Orion A star-forming region, for which IN-SYNC obtained spectra of $\sim2700$ stars. In Paper IV we used these data to study the young stellar population. Here we study the kinematic properties through radial velocities ($v_r$). The young stellar population remains kinematically associated with the molecular gas, following a $\sim10\:{\rm{km\:s}}^{-1}$ gradient along filament. However, near the center of the region, the $v_r$ distribution is slightly blueshifted and asymmetric; we suggest that this population, which is older, is slightly in foreground. We find evidence for kinematic subclustering, detecting statistically significant groupings of co-located stars with coherent motions. These are mostly in the lower-density regions of the cloud, while the ONC radial velocities are smoothly distributed, consistent with it being an older, more dynamically evolved cluster. The velocity dispersion $\sigma_v$ varies along the filament. The ONC appears virialized, or just slightly supervirial, consistent with an old dynamical age. Here there is also some evidence for on-going expansion, from a $v_r$--extinction correlation. In the southern filament, $\sigma_v$ is $\sim2$--$3$ times larger than virial in the L1641N region, where we infer a superposition along the line of sight of stellar sub-populations, detached from the gas. On the contrary, $\sigma_v$ decreases towards L1641S, where the population is again in agreement with a virial state.Comment: 14 pages, 13 figures, ApJ accepte