Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations

Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks. However, challenge still remains in balancing the trade-off of various tasks since model performance is sensitive to the relationships between them. Less correlated or even conflict tasks will deteriorate the performance by introducing unhelpful or negative information. Therefore, it is important to efficiently exploit and learn fine-grained feature representation corresponding to each task. In this paper, we propose an Adaptive Pattern Extraction Multi-task (APEM) framework, which is adaptive and flexible for large-scale industrial application. APEM is able to fully utilize the feature information by learning the interactions between the input feature fields and extracted corresponding tasks-specific information. We first introduce a DeepAuto Group Transformer module to automatically and efficiently enhance the feature expressivity with a modified set attention mechanism and a Squeeze-and-Excitation operation. Second, explicit Pattern Selector is introduced to further enable selectively feature representation learning by adaptive task-indicator vectors. Empirical evaluations show that APEM outperforms the state-of-the-art MTL methods on public and real-world financial services datasets. More importantly, we explore the online performance of APEM in a real industrial-level recommendation scenario.Comment: 18 pages, 9 figure

Comparative Proteomic Analysis Provides Insight into the Key Proteins Involved in Cucumber (Cucumis sativus L.) Adventitious Root Emergence under Waterlogging Stress

Waterlogging is a common abiotic stress in both natural and agricultural systems, and it primarily affects plant growth by the slow oxygen diffusion in water. To sustain root function in the hypoxic environment, a key adaptation for waterlogging tolerant plants is the formation of adventitious roots (ARs). We found that cucumber waterlogging tolerant line Zaoer-N seedlings adapt to waterlogging stress by developing a larger number of ARs in hypocotyls, while almost no AR is generated in sensitive line Pepino. To understand the molecular mechanisms underlying AR emergence, the iTRAQ-based quantitative proteomics approach was employed to map the proteomes of hypocotyls cells of the Zaoer-N and Pepino under control and waterlogging conditions. A total of 5,508 proteins were identified and 146 were differentially regulated proteins (DRPs), of which 47 and 56 DRPs were specific to tolerant and sensitive line, respectively. In the waterlogged Zaoer-N hypocotyls, DRPs related to alcohol dehydrogenases (ADH), 1-aminocyclopropane-1-carboxylicacid oxidases, peroxidases, 60S ribosomal proteins, GSDL esterases/lipases, histone deacetylases and histone H5 and were strongly overrepresented to manage the energy crisis, promote ethylene release, minimize oxidative damage, mobilize storage lipids, and stimulate cell division, differentiation and growth. The evaluations of ethylene production, ADH activity, pyruvate decarboxylase (PDC) activity and ethanol production were in good agreement with the proteomic results. qRT-PCR analysis of the corresponding 146 genes further confirmed the accuracy of the observed protein abundance. These findings shed light on the mechanisms underlying waterlogging triggered cucumber ARs emergence, and provided valuable information for the breeding of cucumber with enhanced tolerance to waterlogging

Farnesoid X Receptor (FXR) Aggravates Amyloid-β-Triggered Apoptosis by Modulating the cAMP-Response Element-Binding Protein (CREB)/Brain-Derived Neurotrophic Factor (BDNF) Pathway In Vitro

BACKGROUND: Alzheimer’s disease (AD), which results in cognitive deficits, usually occurs in older people and is mainly caused by amyloid beta (Aß) deposits and neurofibrillary tangles. The bile acid receptor, farnesoid X receptor (FXR), has been extensively studied in cardiovascular diseases and digestive diseases. However, the role of FXR in AD is not yet understood. The purpose of the present study was to investigate the mechanism of FXR function in AD. MATERIAL AND METHODS: Lentivirus infection, flow cytometry, real-time PCR, and western blotting were used to detect the gain or loss of FXR in cell apoptosis induced by Aß. Co-immunoprecipitation was used to analyze the molecular partners involved in Aß-induced apoptosis. RESULTS: We found that the mRNA and protein expression of FXR was enhanced in Ab-triggered neuronal apoptosis in differentiated SH-SY5Y cells and in mouse hippocampal neurons. Overexpression of FXR aggravated Aß-triggered neuronal apoptosis in differentiated SH-SY5Y cells, and this effect was further increased by treatment with the FXR agonist 6ECDCA. Molecular mechanism analysis by co-immunoprecipitation and immunoblotting revealed that FXR interacted with the cAMP-response element-binding protein (CREB), leading to decreased CREB and brain-derived neurotrophic factor (BDNF) protein levels. Low expression of FXR mostly reversed the Aß-triggered neuronal apoptosis effect and prevented the reduction in CREB and BDNF. CONCLUSIONS: These data suggest that FXR regulates Aß-induced neuronal apoptosis, which may be dependent on the CREB/BDNF signaling pathway in vitro

Online predicting conformance of business process with recurrent neural networks

Conformance Checking is a problem to detect and describe the differences between a given process model representing the expected behaviour of a business process and an event log recording its actual execution by the Process-aware Information System (PAIS). However, such existing conformance checking techniques are offline and mainly applied for the completely executed process instances, which cannot provide the real-time conformance-oriented process monitoring for an on-going process instance. Therefore, in this paper, we propose three approaches for online conformance prediction by constructing a classification model automatically based on the historical event log and the existing reference process model. By utilizing Recurrent Neural Networks, these approaches can capture the features that have a decisive effect on the conformance for an executed case to build a prediction model and then use this model to predict the conformance of a running case. The experimental results on two real datasets show that our approaches outperform the state-of-the-art ones in terms of prediction accuracy and time performance

Microbial denitrification characteristics of typical decentralized wastewater treatment processes based on 16S rRNA sequencing

Despite the widespread application of decentralized wastewater treatment (WWT) facilities in China, relatively few research has used the multi-media biological filter (MMBF) facilities to investigate the microorganism characteristics. This study utilizes 16S rRNA high-throughput sequencing (HTS) technology to examine the microbial biodiversity of a representative wastewater treatment (WWT) system in an expressway service area. The pathways of nitrogen removal along the treatment route were analyzed in conjunction with water quality monitoring. The distribution and composition of microbial flora in the samples were examined, and the dominant flora were identified using LEfSe analysis. The FAPROTAX methodology was employed to investigate the relative abundance of genes associated with the nitrogen cycle and to discern the presence of functional genes involved in nitrogen metabolism. On average, the method has a high level of efficiency in removing COD, TN, NH3-N, and TP from the effluent. The analysis of the microbial community identified a total of 40 phyla, 111 classes, 143 orders, 263 families, and 419 genera. The phyla that were predominantly observed include Proteobacteria, Acidobacteria, Chloroflexi, Actinobacteria, Nitrospirae, Bacteroidetes. The results show that the system has achieved high performance in nitrogen removal, the abundance of nitrification genes is significantly higher than that of other nitrogen cycle genes such as denitrification, and there are six nitrogen metabolism pathways, primarily nitrification, among which Nitrospirae and Nitrospira are the core differentiated flora that can adapt to low temperature conditions and participate in nitrification, and are the dominant nitrogen removal flora in cold regions. This work aims to comprehensively investigate the diversity and functional properties of the bacterial community in decentralized WWT processes

Cattle grazing mitigates the negative impacts of nitrogen addition on soil nematode communities

Livestock grazing and atmospheric nitrogen (N) deposition have been reported as important factors affecting soil communities. However, how different large herbivore grazing and N addition may interact to affect soil biota in grassland ecosystems is unclear. Nematodes are the most abundant metazoan in soil ecosystems, play critical roles in regulating carbon and nutrient dynamics, and are valuable bioindicators. We examined the independent and interactive effects of grazing regimes (no grazing; sheep grazing; cattle grazing; mixed grazing of sheep and cattle) and N addition (ambient N; N addition) on soil nematodes in a meadow steppe. We found that grazing and N addition interacted to influence total nematode abundance, trophic group abundance, generic richness, diversity and several nematode-based indices (maturity index, channel ratio, enrichment index). In cattle grazing treatment, N addition significantly increased total nematode abundance, and the abundance of bacterial feeders, plant feeders, and omnivore-predators, and generic richness. By contrast, in the sheep and mixed grazing treatments, N addition had a negative effect on the same variables. Moreover, N addition reduced nematode maturity, enrichment and structure indices, and enhanced nematode channel ratio, in most grazing treatments, except mixed grazing where N addition had no effect on these variables. Structural equation modeling (SEM) revealed that N addition indirectly reduced nematode abundance and richness through increased soil NO3−-N content, whereas the effects of grazing were associated with increased relative biomass of grasses. Our results suggested that the response of soil nematodes to N addition strongly depended on herbivore assemblages. Nitrogen addition enhanced soil nematode diversity and maintained a relatively complex and mature soil food web in the presence of cattle rather than sheep grazing. Furthermore, our study highlighted that under N deposition, cattle grazing could benefit the soil nematode community

Gene function prediction from congruent synthetic lethal interactions in yeast

We predicted gene function using synthetic lethal genetic interactions between null alleles in Saccharomyces cerevisiae. Phenotypic and protein interaction data indicate that synthetic lethal gene pairs function in parallel or compensating pathways. Congruent gene pairs, defined as sharing synthetic lethal partners, are in single pathway branches. We predicted benomyl sensitivity and nuclear migration defects using congruence; these phenotypes were uncorrelated with direct synthetic lethality. We also predicted YLL049W as a new member of the dynein–dynactin pathway and provided new supporting experimental evidence. We performed synthetic lethal screens of the parallel mitotic exit network (MEN) and Cdc14 early anaphase release pathways required for late cell cycle. Synthetic lethal interactions bridged genes in these pathways, and high congruence linked genes within each pathway. Synthetic lethal interactions between MEN and all components of the Sin3/Rpd3 histone deacetylase revealed a novel function for Sin3/Rpd3 in promoting mitotic exit in parallel to MEN. These in silico methods can predict phenotypes and gene functions and are applicable to genomic synthetic lethality screens in yeast and analogous RNA interference screens in metazoans