Tissue Specificity Of A Baculovirus-Expressed, Basement Membrane-Degrading Protease In Larvae Of Heliothis virescens

Baculoviruses are arthropod-specific, double-stranded DNA viruses, with potential for use in insect pest management. Modern baculovirology is driven by the genetic enhancement of their insecticidal properties. A recombinant baculovirus (AcMLF9.ScathL) that expresses a cathepsin L-like protease, ScathL, kills larvae of the tobacco budworm Heliothis virescens (Fabricius) significantly faster than the wild type Autographa californica multiple nucleopolyhedrovirus (AcMNPV C6). AcMLF9.ScathL triggers melanization and tissue fragmentation shortly before death of infected larvae. To investigate the tissue specificity of ScathL expressed by AcMLF9.ScathL, we used light microscopy, transmission electron microscopy and scanning electron microscopy to examine the tissues of insects infected with AcMLF9.ScathL, with a virus expressing a catalytically inactive form of ScathL, AcMLF9.ScathL.C146A, or wild type virus AcMNPV C6 as control treatments. We found damage to the basement membrane overlaying the midgut, fat body and muscle fibers in larvae infected with AcMLF9.ScathL, but not in larvae infected with the control virus AcMLF9.ScathL.C146A, or the wild type virus AcMNPV C6. We injected yeast-expressed ScathL and can conclude that ScathL results in damage to the basement membrane and subsequent loss of tissue integrity. At high concentrations, ScathL results in complete loss of the gut. Loss of the gut may be an indirect effect resulting from lysis of cells that have lost their overlaying basement membrane. Because AcMLF9.ScathL triggers melanization shortly before death of the host insect, an alternative hypothesis is that larval death results from production of cytotoxic free radicals produced during the melanization process

Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding

Context plays an important role in human language understanding, thus it may also be useful for machines learning vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required. After that, we designed a model which still keeps an RNN as the encoder, while using a non-autoregressive convolutional decoder. We further combine a suite of effective designs to significantly improve model efficiency while also achieving better performance. Our model is trained on two different large unlabelled corpora, and in both cases the transferability is evaluated on a set of downstream NLP tasks. We empirically show that our model is simple and fast while producing rich sentence representations that excel in downstream tasks

Rethinking Skip-thought: A Neighborhood based Approach

We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn't aid our model to perform better, while it hurts the performance of the skip-thought model

3-D finite element analysis on shear lag effect of curved box girder under multi-dimensional seismic excitation

Shear lag effect of curved box girder under multi-dimensional seismic excitation is studied in this paper. Firstly, spatial finite element model is established based on ANSYS, and a seismic wave, which is recorded in second site, is chosen as ground acceleration time history. Secondly, elastic dynamic time-history analysis focused on shear lag effect is carried out, where 4 working conditions, 3-D seismic, longitudinal-vertical seismic, vertical seismic and transverse seismic only, are considered. Thirdly, critical angle of seismic waves is investigated, it is seen that under seismic excitation, there is a prominent shear lag effect on upper flange at mid-span of the curved box girder, and there are also various shear lag effect modes under the different working conditions of seismic excitation. The shear lag under 3-D seismic is severest, normal stress is concentrated on inside upper flange, then that under longitudinal-vertical seismic is less serious, in which case, the stress is appearing within a regional proximity to the junction between webs and flange, the next is under vertical seismic, and the shear lag effect under transverse seismic is most non-prominent. Finally, the numeric results are compared with the experimental results from a vibration table testing, which shows great consistencies

Reduced scale model test on cable membrane roof of Shangai Expo Central Axis

p. 2008-2018In this paper a reduced scale model test on cable membrane roof of Shanghai Expo Central Axis is introduced. The membrane pre-stresses, cable forces and membrane geometry at the initial state are carefully inspected. Numerical form-finding analysis is also carried out and its result is compared with the inspecton. The behaviors of the membrane roof under breaking of cables are observed. Test proves the practicability of the project in aspects of system safety, analysis and inspection.Zhang, Q.; Yang, Z.; Chen, L.; Tang, H.; Zhu, B. (2010). Reduced scale model test on cable membrane roof of Shangai Expo Central Axis. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/718

Hydrodynamic performance optimization of marine propellers based on fluid-structure coupling

Fiber-reinforced composites offer the benefits of high strength, high stiffness, lightweight, superior damping performance, and great design capability when compared to metal. The rigidity characteristics of the composite laminate in different directions may be adjusted to meet the requirements of the application by using appropriate materials and arranging the lay-up sequence. As a result, the purpose of this work is to explore the influence of lay-up type on propeller performance in terms of both hydrodynamic and structural performance. A transient fluid-structure interaction (FSI) algorithm based on the finite element method (FEM) combined with the computational fluid dynamics (CFD) technique is developed and used for the analysis of composite propellers. The hydrodynamic performance of the propeller is compared to that of a metallic material. Propeller propulsion efficiency, structural deformation, equivalent stress, and damage performance of different lay-up options under three different operating situations are compared. In addition, it is presented a parametric optimization approach to get the most appropriate lay-up program for composite blades with the best hydrodynamic properties and structural performance

Accelerated phosphorus accumulation and acidification of soils under plastic greenhouse condition in four representative organic vegetable cultivation sites

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by for personal use, not for redistribution. The definitive version was published in Scientia Horticulturae 195 (2015): 67-83, doi:10.1016/j.scienta.2015.08.041.Organic vegetable cultivation under plastic greenhouse conditions is expanding rapidly in the suburb of big cities in China due to the increasing demand for organic, out-of-season green vegetables and the sustainable development of agriculture. Phosphorus (P) is not only an important plant nutrient, but also a major contaminant in the water environment. However, information on the accumulation and distribution of P in organic vegetable soils under plastic greenhouse conditions is limited, relative to the open cultivation systems. Therefore, twenty-six plastic greenhouse vegetable soils (PGVS) were selected randomly from four representative organic vegetable cultivation sites located in the suburb of Nanjing, China. For comparison, 15 open vegetable soils (OVS) near the PGVS with similar soil and cultivation practices were selected. Soil pH, organic matter (OM) and the various P accumulation characteristics were investigated. We found that soil pH in PGVS were significantly decreased by 0.57~1.17 unit with obvious signs of acidification, compared with that in OVS. Soil OM was different for different sampling locations, but in general it was higher in PGVS than OVS. Soil total P (TP), inorganic P (Pi) and Olsen-P of PGVS were higher than those in the OVS. Olsen-P of all soil samples were far above the recommended optimum value of 20 mg kg-1 for field crops, and over 60% soil samples were considered excessive (>150 mg kg-1 ) in the PGVS and OVS. There were significant correlations between total P, available P and soil pH in those vegetable soils. Al-P/Fe-P ratio was also significantly correlated with vegetable soil pH (YpH = 7.44 - 1.32 XAl-P/Fe-P, r = - 0.705, p < 0.01). Soil total Pi was negatively correlated with soil pH in vegetable soils (r = -0.328, p < 0.05), but the interactive effect of soil various Pi and soil pH need to be further investigated through a series of controlled tests. Our results suggest that the rapid P accumulation and acidification make the current plastic greenhouse vegetable production in the study area unsustainable and better organic manure management practices need to be implemented to sustain crop yields while minimizing the impact of vegetable production on the environment.This work was supported by the National Natural Science Foundation, China (grant no. 41571286; 51479055); Open Research Fund Program of State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Science, Nanjing 210008, China (grant no.Y412201419); and the Fund of Jiangsu Overseas Research ＆ Training Program for University Prominent Young ＆ Middle-aged Teachers and Presidents

A Fast Clustering Algorithm based on pruning unnecessary distance computations in DBSCAN for High-Dimensional Data

Clustering is an important technique to deal with large scale data which are explosively created in internet. Most data are high-dimensional with a lot of noise, which brings great challenges to retrieval, classification and understanding. No current existing approach is “optimal” for large scale data. For example, DBSCAN requires O(n2) time, Fast-DBSCAN only works well in 2 dimensions, and ρ-Approximate DBSCAN runs in O(n) expected time which needs dimension D to be a relative small constant for the linear running time to hold. However, we prove theoretically and experimentally that ρ-Approximate DBSCAN degenerates to an O(n2) algorithm in very high dimension such that 2D >  > n. In this paper, we propose a novel local neighborhood searching technique, and apply it to improve DBSCAN, named as NQ-DBSCAN, such that a large number of unnecessary distance computations can be effectively reduced. Theoretical analysis and experimental results show that NQ-DBSCAN averagely runs in O(n*log(n)) with the help of indexing technique, and the best case is O(n) if proper parameters are used, which makes it suitable for many realtime data

A Survey on Transformer Compression

Transformer plays a vital role in the realms of natural language processing (NLP) and computer vision (CV), specially for constructing large language models (LLM) and large vision models (LVM). Model compression methods reduce the memory and computational cost of Transformer, which is a necessary step to implement large language/vision models on practical devices. Given the unique architecture of Transformer, featuring alternative attention and feedforward neural network (FFN) modules, specific compression techniques are usually required. The efficiency of these compression methods is also paramount, as retraining large models on the entire training dataset is usually impractical. This survey provides a comprehensive review of recent compression methods, with a specific focus on their application to Transformer-based models. The compression methods are primarily categorized into pruning, quantization, knowledge distillation, and efficient architecture design (Mamba, RetNet, RWKV, etc.). In each category, we discuss compression methods for both language and vision tasks, highlighting common underlying principles. Finally, we delve into the relation between various compression methods, and discuss further directions in this domain.Comment: Model Compression, Transformer, Large Language Model, Large Vision Model, LL