Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds

We introduce the state-of-the-art deep learning Denoising Diffusion Probabilistic Model (DDPM) as a method to infer the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps. We adopt magnetohydrodynamic simulations with different global magnetic field strengths and large-scale dynamics, i.e., noncolliding and colliding GMCs. We train a diffusion model on both mass surface density maps and their corresponding mass-weighted number density maps from different viewing angles for all the simulations. We compare the diffusion model performance with a more traditional empirical two-component and three-component power-law fitting method and with a more traditional neural network machine learning approach (CASI-2D). We conclude that the diffusion model achieves an order of magnitude improvement on the accuracy of predicting number density compared to that by other methods. We apply the diffusion method to some example astronomical column density maps of Taurus and the Infrared Dark Clouds (IRDCs) G28.37+0.07 and G35.39-0.33 to produce maps of their mean volume densities.Comment: ApJ accepte

An empirical model to evaluate the effects of environmental humidity on the formation of wrinkled, creased and porous fibre morphology from electrospinning.

Controlling environmental humidity level and thus moisture interaction with an electrospinning solution jet has led to a fascinating range of polymer fibre morphological features; these include surface wrinkles, creases and surface/internal porosity at the individual fibre level. Here, by cross-correlating literature data of far-field electrospinning (FFES), together with our experimental data from near-field electrospinning (NFES), we propose a theoretical model, which can account, phenomenologically, for the onset of fibre microstructures formation from electrospinning solutions made of a hydrophobic polymer dissolved in a water-miscible or polar solvent. This empirical model provides a quantitative evaluation on how the evaporating solvent vapour could prevent or disrupt water vapor condensation onto the electrospinning jet; thus, on the condition where vapor condensation does occur, morphological features will form on the surface, or bulk of the fibre. A wide range of polymer systems, including polystyrene, poly(methyl methacrylate), poly-L-lactic acid, polycaprolactone were tested and validated. Our analysis points to the different operation regimes associated FFES versus NFES, when it comes to the system's sensitivity towards environmental moisture. Our proposed model may further be used to guide the process in creating desirable fibre microstructure

Multi-node Acceleration for Large-scale GCNs

Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount of time, due to the explosive size of graphs nowadays. Thus, large-scale GCNs call for a multi-node acceleration system (MultiAccSys) like TPU-Pod for large-scale neural networks. In this work, we aim to scale up single-node GCN accelerators to accelerate GCNs on large-scale graphs. We first identify the communication pattern and challenges of multi-node acceleration for GCNs on large-scale graphs. We observe that (1) coarse-grained communication patterns exist in the execution of GCNs in MultiAccSys, which introduces massive amount of redundant network transmissions and off-chip memory accesses; (2) overall, the acceleration of GCNs in MultiAccSys is bandwidth-bound and latency-tolerant. Guided by these two observations, we then propose MultiGCN, the first MultiAccSys for large-scale GCNs that trades network latency for network bandwidth. Specifically, by leveraging the network latency tolerance, we first propose a topology-aware multicast mechanism with a one put per multicast message-passing model to reduce transmissions and alleviate network bandwidth requirements. Second, we introduce a scatter-based round execution mechanism which cooperates with the multicast mechanism and reduces redundant off-chip memory accesses. Compared to the baseline MultiAccSys, MultiGCN achieves 4~12x speedup using only 28%~68% energy, while reducing 32% transmissions and 73% off-chip memory accesses on average. It not only achieves 2.5~8x speedup over the state-of-the-art multi-GPU solution, but also scales to large-scale graphs as opposed to single-node GCN accelerators.Comment: To appear in T

4-(2,5-Dihexyl­oxyphen­yl)benzoic acid

In the title compound, C25H34O4, one n-hexyl chain of the hex­yloxy group adopts a fully extended all-trans conformation, and the other n-hexyl chain displays disorder with site occupancies of 0.470 (3) and 0.530 (3). The dihedral angle between the benzene rings is 44.5 (3)°. In the crystal structure, inter­molecular O—H⋯O hydrogen bonds form dimers via crystallographic inversion centres

HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation

Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the neighboring feature vectors for each vertex in each semantic graph and then fuse the aggregated results across all semantic graphs for each vertex. Unfortunately, existing graph neural network accelerators are ill-suited to accelerate HGNNs. This is because they fail to efficiently tackle the specific execution patterns and exploit the high-degree parallelism as well as data reusability inside and across the processing of semantic graphs in HGNNs. In this work, we first quantitatively characterize a set of representative HGNN models on GPU to disclose the execution bound of each stage, inter-semantic-graph parallelism, and inter-semantic-graph data reusability in HGNNs. Guided by our findings, we propose a high-performance HGNN accelerator, HiHGNN, to alleviate the execution bound and exploit the newfound parallelism and data reusability in HGNNs. Specifically, we first propose a bound-aware stage-fusion methodology that tailors to HGNN acceleration, to fuse and pipeline the execution stages being aware of their execution bounds. Second, we design an independency-aware parallel execution design to exploit the inter-semantic-graph parallelism. Finally, we present a similarity-aware execution scheduling to exploit the inter-semantic-graph data reusability. Compared to the state-of-the-art software framework running on NVIDIA GPU T4 and GPU A100, HiHGNN respectively achieves an average 41.5$\times$ and 8.6$\times$ speedup as well as 106$\times$ and 73$\times$ energy efficiency with quarter the memory bandwidth of GPU A100

Identification of a New HCV Subtype 6xg Among Injection Drug Users in Kachin, Myanmar

Characterizing hepatitis C virus (HCV) genetic diversity not only allows us to trace its origin and evolutionary history, but also provides valuable insights into diagnosis, prevention and therapy of HCV infection. Although eight HCV genotypes and 86 subtypes have been classified, there are still some HCV variants that need to be assigned. The genotype 6 is the most diverse HCV genotype and mainly prevalent in Southeast Asia. In this study, we identified a new HCV subtype 6xg from injection drug users (IDUs) in Kachin, Myanmar. A distinctive feature of 6xg from other subtypes of the genotype 6 was a Lys insertion in NS5A gene, which changes the RRKR/K motif into RRKKR/K. Bayesian analyses showed that HCV 6xg originated during 1984–1988, and experienced a rapid population expansion during 2005–2009. We characterized HCV subtype profile among IDUs in this region, and detected six HCV subtypes, including 1a (12.0%), 3a (12.0%), 3b (24.0%), 6n (16.0%), 6xa (20.0%), and 6xg (12.0%). Importantly, we found that HCV subtype distribution in Kachin was very similar to that in Dehong prefecture of Yunnan, but very distinct from those in other regions of Myanmar and Yunnan, indicating that the China–Myanmar border region shared a unique HCV subtype pattern. The appearance of 6xg and the unique HCV subtype profile among IDUs in the China–Myanmar border region have significant epidemiological and public health implications

Differences and allometric relationships among assimilative branch traits of four shrubs in Central Asia

Shrubs play a major role in maintaining ecosystem stability in the arid deserts of Central Asia. During the long-term adaptation to extreme arid environments, shrubs have developed special assimilative branches that replace leaves for photosynthesis. In this study, four dominant shrubs with assimilative branches, namely Haloxylon ammodendron, Haloxylon persicum, Calligonum mongolicum, and Ephedra przewalskii, were selected as the research objects, and the dry mass, total length, node number, and basal diameter of their assimilative branches and the average length of the first three nodes were carefully measured, and the allometric relationships among five traits of four species were systematically compared. The results indicated that: (1) Four desert shrubs have different assimilative branches traits. Compared with H. persicum and H. ammodendron, C. mongolicum and E. przewalskii have longer internodes and fewer nodes. The dry mass of H. ammodendron and the basal diameter of H. persicum were the smallest; (2) Significant allometric scaling relationships were found between dry mass, total length, basal diameter, and each trait of assimilative branches, all of which were significantly less than 1; (3) The scaling exponents of the allometric relationship between four traits and the dry mass of assimilative branches of H. persicum were greater or significantly greater than those of H. ammodendron. The scaling exponents of the relationships between the basal diameter, dry mass, and total length of E. przewalskii were higher than those of the other three shrubs. Therefore, although different species have adapted to drought and high temperatures by convergence, there was great variability in morphological characteristics of assimilative branches, as well as in the scaling exponents of relationships among traits. The results of this study will provide valuable insights into the ecological functions of assimilative branches and survival strategies of these shrubs to cope with aridity and drought in desert environments

雷公藤红素通过靶向核受体Nur77促进损伤线粒体自噬而抑制炎症反应

文章简介线粒体在细胞死亡、自噬、免疫和炎症中起着不可或缺的作用。前期研究发现,孤儿核受体Nur77通过靶向线粒体诱导细胞凋亡。本文报道了Nur77作为具有抗炎作用的雷公藤红素的直接靶点,介导雷公藤红素通过自噬清除损伤线粒体,抑制炎症反应而达到治疗炎症疾病包括肥胖症的功能。研究人员发现,雷公藤红素的结合

miR-182 Regulates Metabolic Homeostasis by Modulating Glucose Utilization in Muscle

SummaryUnderstanding the fiber-type specification and metabolic switch in skeletal muscle provides insights into energy metabolism in physiology and diseases. Here, we show that miR-182 is highly expressed in fast-twitch muscle and negatively correlates with blood glucose level. miR-182 knockout mice display muscle loss, fast-to-slow fiber-type switching, and impaired glucose metabolism. Mechanistic studies reveal that miR-182 modulates glucose utilization in muscle by targeting FoxO1 and PDK4, which control fuel selection via the pyruvate dehydrogenase complex (PDHC). Short-term high-fat diet (HFD) feeding reduces muscle miR-182 levels by tumor necrosis factor α (TNFα), which contributes to the upregulation of FoxO1/PDK4. Restoration of miR-182 expression in HFD-fed mice induces a faster muscle phenotype, decreases muscle FoxO1/PDK4 levels, and improves glucose metabolism. Together, our work establishes miR-182 as a critical regulator that confers robust and precise controls on fuel usage and glucose homeostasis. Our study suggests that a metabolic shift toward a faster and more glycolytic phenotype is beneficial for glucose control