Energy-conserving 3D elastic wave simulation with finite difference discretization on staggered grids with nonconforming interfaces

In this work, we describe an approach to stably simulate the 3D isotropic elastic wave propagation using finite difference discretization on staggered grids with nonconforming interfaces. Specifically, we consider simulation domains composed of layers of uniform grids with different grid spacings, separated by planar interfaces. This discretization setting is motivated by the observation that wave speeds of earth media tend to increase with depth due to sedimentation and consolidation processes. We demonstrate that the layer-wise finite difference discretization approach has the potential to significantly reduce the simulation cost, compared to its counterpart that uses holistically uniform grids. Such discretizations are enabled by summation-by-parts finite difference operators, which are standard finite difference operators with special adaptations near boundaries or interfaces, and simultaneous approximation terms, which are penalty terms appended to the discretized system to weakly impose boundary or interface conditions. Combined with specially designed interpolation operators, the discretized system is shown to preserve the energy-conserving property of the continuous elastic wave equation, and a fortiori ensure the stability of the simulation. Numerical examples are presented to corroborate these analytical developments

Rheology control and shrinkage mitigation of 3D printed geopolymer concrete using nanocellulose and magnesium oxide

3D printing concrete (3DPC) is an emerging technology that produces concrete using digital method and has revolutionized the traditional labor-intensive construction mode. However, the free formwork printing and layer-by-layer production of 3DPC induce severe shrinkage and plastic cracking during the early ages, especially for the geopolymer based materials. This research utilizes the nano-fibrillated cellulose (NFC) with the combination of magnesium oxides expansive agent (MEA) to mitigate the plastic and drying shrinkage of 3D printing geopolymer concrete (3DPGC), while optimizing its rheological behavior. The results show that after modification with proper dosages of NFC and MEA, 3DPGC showed reduced plastic and drying shrinkage at early ages, with improved printability, buildability, and mechanical strength. The underlying role of NFC and MEA on the performance of 3DPGC was thoroughly analyzed with rheometry, calorimetry, scanning electron microscopy, and internal humidity test. The water retention ability of nanocellulose can provide more moisture at early ages, thus mitigating cracking, while MEA can compromise the drying shrinkage at later ages. The contribution of the study shed light on the application of nanocellulose and MgO to increase the volume stability and mechanical performance of 3D printing geopolymer concrete.</p

Direct conversion of astrocytes into neuronal cells by drug cocktail

Direct conversion of astrocytes into neuronal cells by drug cocktail Cell Research advance online publication 2 October 2015; doi:10.1038/cr.2015.120 Dear Editor, Neurological disorder is one of the greatest threats to public health according to the World Health Organization. Because neurons have little or no regenerative capacity, conventional therapies for neurological disorders yielded poor outcomes. While the introduction of exogenous neural stem cells or neurons holds promise, many challenges still need to be tackled, including cell resource, delivery strategy, cell integration and cell maturation. Reprogramming of fibroblasts into induced pluripotent stem cells or directly into desirable neuronal cells by transcription factors (TFs) or small molecules can solve some problems, but other issues remain to be addressed, including safety, conversion efficiency and epigenetic memory [1, 2]. Astrocytes are considered to be the ideal starting candidate cell type for generating new neurons, due to their proximity in lineage distance to neurons and ability to proliferate after brain damage. Many studies have already revealed that astrocytes of the central nervous system can be reprogrammed into induced neuronal cells by virus-mediated overexpression of specific TFs in vitro and in vivo [3-6]. However, application of this virus-mediated direct conversion is still limited due to concerns on clinical safety. We have previously reported direct conversion of somatic cells into neural progenitor cells (NPCs) in vitro by cocktail of small molecules under hypoxia [7]. Here we set out to explore whether astrocytes can be induced into neuronal cells by the chemical cocktail in vitro

LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views, they fall short of producing accurate and realistic LiDAR patterns because the renderers rely on explicit 3D reconstruction and exploit game engines, that ignore important attributes of LiDAR points. We address this challenge by formulating, to the best of our knowledge, the first differentiable end-to-end LiDAR rendering framework, LiDAR-NeRF, leveraging a neural radiance field (NeRF) to facilitate the joint learning of geometry and the attributes of 3D points. However, simply employing NeRF cannot achieve satisfactory results, as it only focuses on learning individual pixels while ignoring local information, especially at low texture areas, resulting in poor geometry. To this end, we have taken steps to address this issue by introducing a structural regularization method to preserve local structural details. To evaluate the effectiveness of our approach, we establish an object-centric multi-view LiDAR dataset, dubbed NeRF-MVL. It contains observations of objects from 9 categories seen from 360-degree viewpoints captured with multiple LiDAR sensors. Our extensive experiments on the scene-level KITTI-360 dataset, and on our object-level NeRF-MVL show that our LiDAR-NeRF surpasses the model-based algorithms significantly.Comment: This paper introduces a new task of novel LiDAR view synthesis, and proposes a differentiable framework called LiDAR-NeRF with a structural regularization, as well as an object-centric multi-view LiDAR dataset called NeRF-MV

Proteomic profiling and biomarker discovery for predicting the response to PD-1 inhibitor immunotherapy in gastric cancer patients

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment; however, a significant proportion of gastric cancer (GC) patients do not respond to this therapy. Consequently, there is an urgent need to elucidate the mechanisms underlying resistance to ICIs and identify robust biomarkers capable of predicting the response to ICIs at treatment initiation.Methods: In this study, we collected GC tissues from 28 patients prior to the administration of anti-programmed death 1 (PD-1) immunotherapy and conducted protein quantification using high-resolution mass spectrometry (MS). Subsequently, we analyzed differences in protein expression, pathways, and the tumor microenvironment (TME) between responders and non-responders. Furthermore, we explored the potential of these differences as predictive indicators. Finally, using machine learning algorithms, we screened for biomarkers and constructed a predictive model.Results: Our proteomics-based analysis revealed that low activity in the complement and coagulation cascades pathway (CCCP) and a high abundance of activated CD8 T cells are positive signals corresponding to ICIs. By using machine learning, we successfully identified a set of 10 protein biomarkers, and the constructed model demonstrated excellent performance in predicting the response in an independent validation set (N = 14; area under the curve [AUC] = 0.959).Conclusion: In summary, our proteomic analyses unveiled unique potential biomarkers for predicting the response to PD-1 inhibitor immunotherapy in GC patients, which may provide the impetus for precision immunotherapy

Enhancing the electrocatalytic activity of 2H-WS2 for hydrogen evolution via defect engineering

Transition metal dichalcogenides (TMDs), such as MoS2 and WS2, are promising alternative non-noble metal catalysts to drive the electrocatalytic H2 evolution reaction (HER). However, their catalytic performance is inherently limited by the small number of active sites as well as their poor electrical conductivity. Here, we grow vertically aligned 2H-WS2 on different substrates to expose their edge sites for the HER and introduce a scalable approach to tune these active sites via defect engineering. In a thermal hydrogen treatment procedure, sulfur vacancies and metallic tungsten nanoparticles are formed. The extent of desulfurization, and thus the HER activity, can be tuned via controlling the H2 annealing conditions. The obtained W/WS2-x electrocatalysts are evaluated experimentally and theoretically to arrive at a better understanding of how to modify the inherently inert 2H-WS2 for more efficient HER.</p

MiR-550a-3p restores damaged vascular smooth muscle cells by inhibiting thrombomodulin in an <em>in vitro</em> atherosclerosis model

Thrombomodulin (TM) is involved in the pathological process of atherosclerosis; however, the underlying mechanism remains unclear. Oxidised low-density lipoprotein (Ox-LDL; 100 μg/mL) was used to induce human vascular smooth muscle cells (HVSMCs) into a stable atherosclerotic cell model. The expression levels of miR-550a-3p and TM were detected by real-time reverse transcription-polymerase chain reaction. Cell proliferation was estimated using CCK8 and EDU assays. Wound scratch and transwell assays were used to measure the ability of cells to invade and migrate. Propidium iodide fluorescence-activated cell sorting was used to detect apoptosis and cell cycle changes. A dual-luciferase reporter assay was performed to determine the binding of miR-550a-3p to TM. Our results suggested the successful development of a cellular atherosclerosis model. Our data revealed that TM overexpression significantly promoted the proliferation, invasion, migration, and apoptosis of HVSMCs as well as cell cycle changes. Upregulation of miR-550a-3p inhibited the growth and metastasis of HVSMCs. Furthermore, miR-550a-3p was confirmed to be a direct target of TM. Restoration of miR-550a-3p expression rescued the effects of TM overexpression. Thus, miR-550a-3p might play a role in atherosclerosis and, for the first time, normalised the function of injured vascular endothelial cells by simultaneous transfection of TM and miR-550a-3p. These results suggest that the miR-550a-3p/TM axis is a potential therapeutic target for atherosclerosis