Memory-Efficient Topic Modeling

As one of the simplest probabilistic topic modeling techniques, latent Dirichlet allocation (LDA) has found many important applications in text mining, computer vision and computational biology. Recent training algorithms for LDA can be interpreted within a unified message passing framework. However, message passing requires storing previous messages with a large amount of memory space, increasing linearly with the number of documents or the number of topics. Therefore, the high memory usage is often a major problem for topic modeling of massive corpora containing a large number of topics. To reduce the space complexity, we propose a novel algorithm without storing previous messages for training LDA: tiny belief propagation (TBP). The basic idea of TBP relates the message passing algorithms with the non-negative matrix factorization (NMF) algorithms, which absorb the message updating into the message passing process, and thus avoid storing previous messages. Experimental results on four large data sets confirm that TBP performs comparably well or even better than current state-of-the-art training algorithms for LDA but with a much less memory consumption. TBP can do topic modeling when massive corpora cannot fit in the computer memory, for example, extracting thematic topics from 7 GB PUBMED corpora on a common desktop computer with 2GB memory.Comment: 20 pages, 7 figure

A New Approach to Speeding Up Topic Modeling

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. In this paper, we propose a fast and accurate batch algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA algorithms require repeated scanning of the entire corpus and searching the complete topic space. To process massive corpora having a large number of topics, the training iteration of batch LDA algorithms is often inefficient and time-consuming. To accelerate the training speed, ABP actively scans the subset of corpus and searches the subset of topic space for topic modeling, therefore saves enormous training time in each iteration. To ensure accuracy, ABP selects only those documents and topics that contribute to the largest residuals within the residual belief propagation (RBP) framework. On four real-world corpora, ABP performs around $10$ to $100$ times faster than state-of-the-art batch LDA algorithms with a comparable topic modeling accuracy.Comment: 14 pages, 12 figure

Novel SPP Water Management Strategy and Its Applications

Clean freshwater is the most precious resource in the world and the development of water resources has had a very long history, as early as humans changed from being hunters and food collectors to modern civilization. At very early stage, people had to rely on creeks, rivers and lakes for their water demand that was relatively small, and today humans have accumulated the knowledge and techniques for water storage, building artificial lakes or reservoirs to meet their huge water demand due to industrialization and urbanization. The Wworld’s earliest large dam was the Sadd-el-kafara Dam built in Egypt between 2950 and 2690 B.C. Up to now, water from lakes and reservoirs is still the main source for people’s water supply. However these large water bodies suffer two problems incurred by nature and human being, one is sedimentation and the other water pollution. Two of them jointly reduce the available amount of clean water and deteriorate the water quality. Consequently, approximate 1.1 billion people lack of safe drinking water and between 2 and 5 million people die annually from water-related disease (Gleick, 2004). It is understandable that with the population growth in the world, it is difficult to provide sufficient clean water to meet the demand; on the other hand, our natural systems are under pressure from drought (too little), floods (too much), pollution (too dirty), climate change, and other stresses. This creates serious challenges for water management

Construction by artificial intelligence and immunovalidation of hypoallergenic mite allergen Der f 36 vaccine

BackgroundThe house dust mite (HDM) is widely recognized as the most prevalent allergen in allergic diseases. Allergen-specific immunotherapy (AIT) has been successfully implemented in clinical treatment for HDM. Hypoallergenic B-cell epitope-based vaccine designed by artificial intelligence (AI) represents a significant progression of recombinant hypoallergenic allergen derivatives.MethodThe three-dimensional protein structure of Der f 36 was constructed using Alphafold2. AI-based tools were employed to predict B-cell epitopes, which were subsequently verified through IgE-reaction testing. Hypoallergenic Der f 36 was then synthesized, expressed, and purified. The reduced allergenicity was assessed by enzyme-linked immunosorbent assay (ELISA), immunoblotting, and basophil activation test. T-cell response to hypoallergenic Der f 36 and Der f 36 was evaluated based on cytokine expression in the peripheral blood mononuclear cells (PBMCs) of patients. The immunogenicity was evaluated and compared through rabbit immunization with hypoallergenic Der f 36 and Der f 36, respectively. The inhibitory effect of the blocking IgG antibody on the specific IgE-binding activity and basophil activation of Der f 36 allergen was also examined.ResultsThe final selected non-allergic B-cell epitopes were 25–48, 57–67, 107–112, 142–151, and 176–184. Hypoallergenic Der f 36 showed significant reduction in IgE-binding activity. The competitive inhibition of IgE-binding to Der f 36 was investigated using the hypoallergenic Der f 36, and only 20% inhibition could be achieved, which is greatly reduced when compared with inhibition by Der f 36 (98%). The hypoallergenic Der f 36 exhibited a low basophil-stimulating ratio similar to that of the negative control, and it could induce an increasing level of IFN‐γ but not Th2 cytokines IL-5 and IL-13 in PBMCs. The vaccine-specific rabbit blocking IgG antibodies could inhibit the patients’ IgE binding and basophil stimulation activity of Derf 36.ConclusionThis study represents the first application of an AI strategy to facilitate the development of a B-cell epitope-based hypoallergenic Der f 36 vaccine, which may become a promising immunotherapy for HDM-allergic patients due to its reduced allergenicity and its high immunogenicity in inducing blocking of IgG

Tramadol Pretreatment Enhances Ketamine-Induced Antidepressant Effects and Increases Mammalian Target of Rapamycin in Rat Hippocampus and Prefrontal Cortex

Several lines of evidence have demonstrated that acute administration of ketamine elicits fast-acting antidepressant effects. Moreover, tramadol also has potential antidepressant effects. The aim of this study was to investigate the effects of pretreatment with tramadol on ketamine-induced antidepressant activity and was to determine the expression of mammalian target of rapamycin (mTOR) in rat hippocampus and prefrontal cortex. Rats were intraperitoneally administrated with ketamine at the dose of 10 mg/kg or saline 1 h before the second episode of the forced swimming test (FST). Tramadol or saline was intraperitoneally pretreated 30 min before the former administration of ketamine or saline. The locomotor activity and the immobility time of FST were both measured. After that, rats were sacrificed to determine the expression of mTOR in hippocampus and prefrontal cortex. Tramadol at the dose of 5 mg/kg administrated alone did not elicit the antidepressant effects. More importantly, pretreatment with tramadol enhanced the ketamine-induced antidepressant effects and upregulated the expression of mTOR in rat hippocampus and prefrontal cortex. Pretreatment with tramadol enhances the ketamine-induced antidepressant effects, which is associated with the increased expression of mTOR in rat hippocampus and prefrontal cortex

Insecticidal Activity of the Leaf and Stem Water Extract of Gelsemium elegans against Solenopsis invicta

A comprehensive green worker ants control method that can be used to replace traditional chemical synthetic insecticides. In this study, the leaves and stems of Gelsemium elegans were extracted with water as the solvent, and the bioactivity of G. elegans against worker ants was determined by the “water tube” method. The bioassay results of insecticidal activity showed that when the time was extended to the 10th day, the mortality of worker ants treated with G. elegans extract reached 55.00% (1/20 leaf extract), 46.67% (1/20 stem extract) and 45.00% (1 mg/kg koumine). And the behavioral impact test results showed that the aggregation rate was reduced to 56.67% (1/100 leaf extract), 60.00% (1/100 stem extract) and 60.00% (0.5 mg/kg koumine); the climbing rate was reduced to 60.00 % (1/100 leaf extract), 58.33% (1/100 stem extract) and 58.33% (0.5 mg/kg koumine). The effect on the walking ability of worker ants is obvious. The walking rate drops to 1.53cm/s (1/100 leaf extract), 1.60cm/s (1/100 stem extract) and 1.47cm/s (0.5 mg/kg koumine). Therefore, we conclude that the water extract of G. elegans can be used for long-term continuous control of worker ants, which can be used to replace traditional chemical synthetic insecticides

The Fumigating Activity of Litsea cubeba oil and Citral on Solenopsis invicta

This paper studied the fumigating activity of Litsea cubeba oil and citral on Solenopsis invicta, identified and analyzed the chemical constituents and volatile components of L. cubeba oil via solid-phase microextraction which were then identified via gas chromatography-mass spectrometry. The results showed that citral and (z)-3,7-dimethylocta-2,6-diena were the main components of L. cubeba oil, as well as its volatile compounds. According to the experimental results, L. cubeba oil and citral had good fumigating activity on workers, and also had significant inhibition on the walking ability and climbing ability of workers. At the same time, the effects of the two agentia on the fumigating activity and behavioral inhibition of microergate were stronger than those of macroergate. After treating with L. cubeba oil and citral for 24 hours, the walking rate and grasping rate of microergate were both 0 %. The results showed that L. cubeba oil and citral had good control effect on S. invicta