SpOctA: A 3D Sparse Convolution Accelerator with Octree-Encoding-Based Map Search and Inherent Sparsity-Aware Processing

Point-cloud-based 3D perception has attracted great attention in various applications including robotics, autonomous driving and AR/VR. In particular, the 3D sparse convolution (SpConv) network has emerged as one of the most popular backbones due to its excellent performance. However, it poses severe challenges to real-time perception on general-purpose platforms, such as lengthy map search latency, high computation cost, and enormous memory footprint. In this paper, we propose SpOctA, a SpConv accelerator that enables high-speed and energy-efficient point cloud processing. SpOctA parallelizes the map search by utilizing algorithm-architecture co-optimization based on octree encoding, thereby achieving 8.8-21.2x search speedup. It also attenuates the heavy computational workload by exploiting inherent sparsity of each voxel, which eliminates computation redundancy and saves 44.4-79.1% processing latency. To optimize on-chip memory management, a SpConv-oriented non-uniform caching strategy is introduced to reduce external memory access energy by 57.6% on average. Implemented on a 40nm technology and extensively evaluated on representative benchmarks, SpOctA rivals the state-of-the-art SpConv accelerators by 1.1-6.9x speedup with 1.5-3.1x energy efficiency improvement.Comment: Accepted to ICCAD 202

Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations

Effects of fertilizer application schemes and soil environmental factors on nitrous oxide emission fluxes in a rice-wheat cropping system, east China

Nitrous oxide (N2O) is a potent greenhouse gas (GHG) with agricultural soils representing its largest anthropogenic source. However, the mechanisms involved in the N2O emission and factors affecting N2O emission fluxes in response to various nitrogenous fertilizer applications remain uncertain. We conducted a four-year (2012–2015) field experiment to assess how fertilization scheme impacts N2O emissions from a rice-wheat cropping system in eastern China. The fertilizer treatments included Control (CK), Conventional fertilizer (CF), CF with shallow-irrigation (CF+SI), CF with deep-irrigation system (CF+DI), Optimized fertilizer (OF), OF with Urease inhibitor (OF+UI), OF with conservation tillage (OF+CT) and Slow-release fertilizer (SRF). N2O emissions were measured by a closed static chamber method. N2O emission fluxes ranged from 0.61 μg m-2 h-1 to 1707 μg m-2 h-1, indicating a significant impact of nitrogen fertilizer and cropping type on N2O emissions. The highest crop yields for wheat (3515–3667 kg ha-1) and rice (8633–8990 kg ha-1) were observed under the SRF and OF+UI treatments with significant reduction in N2O emissions by 16.94–21.20% and 5.55–7.93%, respectively. Our findings suggest that the SRF and OF+UI treatments can be effective in achieving maximum crop yield and lowering N2O emissions for the rice-wheat cropping system in eastern China

Potent anti-tumor effects of a dual specific oncolytic adenovirus expressing apoptin in vitro and in vivo

<p>Abstract</p> <p>Background</p> <p>Oncolytic virotherapy is an attractive drug platform of cancer gene therapy, but efficacy and specificity are important prerequisites for success of such strategies. Previous studies determined that Apoptin is a p53 independent, bcl-2 insensitive apoptotic protein with the ability to specifically induce apoptosis in tumor cells. Here, we generated a conditional replication-competent adenovirus (CRCA), designated Ad-hTERT-E1a-Apoptin, and investigated the effectiveness of the CRCA a gene therapy agent for further clinical trials.</p> <p>Results</p> <p>The observation that infection with Ad-hTERT-E1a-Apoptin significantly inhibited growth of the melanoma cells, protecting normal human epidermal melanocytes from growth inhibition confirmed cancer cell selective adenoviral replication, growth inhibition, and apoptosis induction of this therapeutic approach. The <it>in vivo </it>assays performed by using C57BL/6 mice containing established primary or metastatic tumors expanded the <it>in vitro </it>studies. When treated with Ad-hTERT-E1a-Apoptin, the subcutaneous primary tumor volume reduction was not only observed in intratumoral injection group but in systemic delivery mice. In the lung metastasis model, Ad-hTERT-E1a-Apoptin effectively suppressed pulmonary metastatic lesions. Furthermore, treatment of primary and metastatic models with Ad-hTERT-E1a-Apoptin increased mice survival.</p> <p>Conclusions</p> <p>These data further reinforce the previously research showing that an adenovirus expressing Apoptin is more effective and advocate the potential applications of Ad-hTERT-E1a-Apoptin in the treatment of neoplastic diseases in future clinical trials.</p

Identification of lipid droplet structure-like/resident proteins in Caenorhabditis elegans.

The lipid droplet (LD) is a cellular organelle that stores neutral lipids in cells and has been linked with metabolic disorders. Caenorhabditis elegans has many characteristics which make it an excellent animal model for studying LDs. However, unlike in mammalian cells, no LD structure-like/resident proteins have been identified in C. elegans, which has limited the utility of this model for the study of lipid storage and metabolism. Herein based on three lines of evidence, we identified that MDT-28 and DHS-3 previously identified in C. elegans LD proteome were two LD structure-like/resident proteins. First, MDT-28 and DHS-3 were found to be the two most abundant LD proteins in the worm. Second, the proteins were specifically localized to LDs and we identified the domains responsible for this targeting in both proteins. Third and most importantly, the depletion of MDT-28 induced LD clustering while DHS-3 deletion reduced triacylglycerol content (TAG). We further characterized the proteins finding that MDT-28 was ubiquitously expressed in the intestine, muscle, hypodermis, and embryos, whereas DHS-3 was expressed mainly in intestinal cells. Together, these two LD structure-like/resident proteins provide a basis for future mechanistic studies into the dynamics and functions of LDs in C. elegans

Immunogenicity and protective potential of chimeric virus-like particles containing SARS-CoV-2 spike and H5N1 matrix 1 proteins

Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has posed a constant threat to human beings and the world economy for more than two years. Vaccination is the first choice to control and prevent the pandemic. However, an effective SARS-CoV-2 vaccine against the virus infection is still needed. This study designed and prepared four kinds of virus-like particles (VLPs) using an insect expression system. Two constructs encoded wild-type SARS-CoV-2 spike (S) fused with or without H5N1 matrix 1 (M1) (S and SM). The other two constructs contained a codon-optimized spike gene and/or M1 gene (mS and mSM) based on protein expression, stability, and ADE avoidance. The results showed that the VLP-based vaccine could induce high SARS-CoV-2 specific antibodies in mice, including specific IgG, IgG1, and IgG2a. Moreover, the mSM group has the most robust ability to stimulate humoral immunity and cellular immunity than the other VLPs, suggesting the mSM is the best immunogen. Further studies showed that the mSM combined with Al/CpG adjuvant could stimulate animals to produce sustained high-level antibodies and establish an effective protective barrier to protect mice from challenges with mouse-adapted strain. The vaccine based on mSM and Al/CpG adjuvant is a promising candidate vaccine to prevent the COVID-19 pandemic

Exploring the mechanism of JiGuCao capsule formula on treating hepatitis B virus infection via network pharmacology analysis and in vivo/vitro experiment verification

The JiGuCao capsule formula (JCF) has demonstrated promising curative effects in treating chronic hepatitis B (CHB) in clinical trials. Here, we aimed to investigate JCF’s function and mechanism in diseases related to the hepatitis B virus (HBV). We used mass spectrometry (MS) to identify the active metabolites of JCF and established the HBV replication mouse model by hydrodynamically injecting HBV replication plasmids into the mice’s tail vein. Liposomes were used to transfect the plasmids into the cells. The CCK-8 kit identified cell viability. We detected the levels of HBV s antigen (HBsAg) and HBV e antigen (HBeAg) by the quantitative determination kits. qRT-PCR and Western blot were used to detect the genes’ expression. The key pathways and key genes related to JCF on CHB treatment were obtained by network pharmacological analysis. Our results showed that JCF accelerated the elimination of HBsAg in mice. JCF and its medicated serum inhibited HBV replication and proliferation of HBV-replicating hepatoma cells in vitro. And the key targets of JCF in treating CHB were CASP3, CXCL8, EGFR, HSPA8, IL6, MDM2, MMP9, NR3C1, PTGS2, and VEGFA. Furthermore, these key targets were related to pathways in cancer, hepatitis B, microRNAs in cancer, PI3K-Akt signaling, and proteoglycans in cancer pathways. Finally, Cholic Acid, Deoxycholic Acid, and 3′, 4′, 7-Trihydroxyflavone were the main active metabolites of JCF that we obtained. JCF employed its active metabolites to perform an anti-HBV effect and prevent the development of HBV-related diseases

Nuclear cGMP-Dependent Kinase Regulates Gene Expression via Activity-Dependent Recruitment of a Conserved Histone Deacetylase Complex

Elevation of the second messenger cGMP by nitric oxide (NO) activates the cGMP-dependent protein kinase PKG, which is key in regulating cardiovascular, intestinal, and neuronal functions in mammals. The NO-cGMP-PKG signaling pathway is also a major therapeutic target for cardiovascular and male reproductive diseases. Despite widespread effects of PKG activation, few molecular targets of PKG are known. We study how EGL-4, the Caenorhabditis elegans PKG ortholog, modulates foraging behavior and egg-laying and seeks the downstream effectors of EGL-4 activity. Using a combination of unbiased forward genetic screen and proteomic analysis, we have identified a conserved SAEG-1/SAEG-2/HDA-2 histone deacetylase complex that is specifically recruited by activated nuclear EGL-4. Gene expression profiling by microarrays revealed >40 genes that are sensitive to EGL-4 activity in a SAEG-1–dependent manner. We present evidence that EGL-4 controls egg laying via one of these genes, Y45F10C.2, which encodes a novel protein that is expressed exclusively in the uterine epithelium. Our results indicate that, in addition to cytoplasmic functions, active EGL-4/PKG acts in the nucleus via a conserved Class I histone deacetylase complex to regulate gene expression pertinent to behavioral and physiological responses to cGMP. We also identify transcriptional targets of EGL-4 that carry out discrete components of the physiological response

Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units

The recent emergence of Graphics Processing Units (GPUs) as general-purpose parallel computing devices provides us with new opportunities to develop scalable learning methods for massive data. In this work, we consider the problem of parallelizing two inference methods on GPUs for latent Dirichlet Allocation (LDA) models, collapsed Gibbs sampling (CGS) and collapsed variational Bayesian (CVB). To address limited memory constraints on GPUs, we propose a novel data partitioning scheme that effectively reduces the memory cost. This partitioning scheme also balances the computational cost on each multiprocessor and enables us to easily avoid memory access conflicts. We use data streaming to handle extremely large datasets. Extensive experiments showed that our parallel inference methods consistently produced LDA models with the same predictive power as sequential training methods did but with 26x speedup for CGS and 196x speedup for CVB on a GPU with 30 multiprocessors. The proposed partitioning scheme and data streaming make our approach scalable with more multiprocessors. Furthermore, they can be used as general techniques to parallelize other machine learning models.

Modeling and Analysis of Novel Horizontal Ribbon Growth of Silicon Crystal

We present a novel horizontal ribbon growth (HRG) process and a theoretical analysis of this method. Assuming that the existence of the meniscus is defined by diffuse growth, we determine analytically the thickness and height of the meniscus and an explicit expression for the performance of meniscus under different conditions. We then calculate the thermal profile in melt part, as well as the conditions under which the undercooling is sufficient around the solidification point. We find that diffuse growth is more sensitive to small initial thickness, and find the minimum length of the melt part to obtain undercooling. Finally, we calculate the change rule of solidification position by a variational approach, as well as the stability of the process under different conditions. We also give an expression to the instability of past HRG methods