Business Process Text Sketch Automation Generation Using Large Language Model

Business Process Management (BPM) is gaining increasing attention as it has the potential to cut costs while boosting output and quality. Business process document generation is a crucial stage in BPM. However, due to a shortage of datasets, data-driven deep learning techniques struggle to deliver the expected results. We propose an approach to transform Conditional Process Trees (CPTs) into Business Process Text Sketches (BPTSs) using Large Language Models (LLMs). The traditional prompting approach (Few-shot In-Context Learning) tries to get the correct answer in one go, and it can find the pattern of transforming simple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy, the traditional prompts perform weakly and with low correctness. We suggest using this technique to break down a difficult CPT into a number of basic CPTs and then solve each one in turn, drawing inspiration from the divide-and-conquer strategy. We chose 100 process trees with depths ranging from 2 to 5 at random, as well as CPTs with many nodes, many degrees of selection, and cyclic nesting. Experiments show that our method can achieve a correct rate of 93.42%, which is 45.17% better than traditional prompting methods. Our proposed method provides a solution for business process document generation in the absence of datasets, and secondly, it becomes potentially possible to provide a large number of datasets for the process model extraction (PME) domain.Comment: 10 pages, 7 figure

Gas-Solids Hydrodynamics in a CFB with 6 Cyclones and a Pang Leg

Solids volume fraction and particle velocity profiles were measured with a fiber optical probe in a cold circulating fluidized bed test rig with 6 parallel cyclones and a pant leg. Results in the pant leg zone, main bed zone and exit zone of the furnace are reported. The work also includes the influences of superficial gas velocity, secondary air rate and static bed height on the gas-solids hydrodynamics

A universal lesion detection method based on partially supervised learning

Partially supervised learning (PSL) is urgently necessary to explore to construct an efficient universal lesion detection (ULD) segmentation model. An annotated dataset is crucial but hard to acquire because of too many Computed tomography (CT) images and the lack of professionals in computer-aided detection/diagnosis (CADe/CADx). To address this problem, we propose a novel loss function to reduce the proportion of negative anchors which is extremely likely to classify the lesion area (positive samples) as a negative bounding box, further leading to an unexpected performance. Before calculating loss, we generate a mask to intentionally choose fewer negative anchors which will backward wrongful loss to the network. During the process of loss calculation, we set a parameter to reduce the proportion of negative samples, and it significantly reduces the adverse effect of misclassification on the model. Our experiments are implemented in a 3D framework by feeding a partially annotated dataset named DeepLesion, a large-scale public dataset for universal lesion detection from CT. We implement a lot of experiments to choose the most suitable parameter, and the result shows that the proposed method has greatly improved the performance of a ULD detector. Our code can be obtained at https://github.com/PLuld0/PLuldl

Integrated analysis identifies microRNA-195 as a suppressor of Hippo-YAP pathway in colorectal cancer

Figure S2. KEGG cell signaling pathway was shown for HIPPO pathway. The most significantly enriched by the predicted targets of miR-195 (P = 6.47E-05). Red frame shows the predicted miR-195 targets. (TIF 83 kb

SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning

Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI’s deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and R2 (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and R2 = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet

SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning

Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI’s deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and R2 (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and R2 = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet

Flagella-Associated WDR-Containing Protein CrFAP89 Regulates Growth and Lipid Accumulation in Chlamydomonas reinhardtii

WD40-repeat (WDR) domain-containing proteins are subunits of multi-protein E3 ligase complexes regulating various cellular and developmental activities in eukaryotes. Chlamydomonas reinhardtii serves as a model organism to study lipid metabolism in microalgae. Under nutrition deficient conditions, C. reinhardtii accumulates lipids for survival. The proteins in C. reinhardtii flagella have diverse functions, such as controlling the motility and cell cycle, and environment sensing. Here, we characterized the function of CrFAP89, a flagella-associated WDR-containing protein, which was identified from C. reinhardtii nitrogen deficiency transcriptome analysis. Quantitative real time-PCR showed that the transcription levels of CrFAP89 were significantly enhanced upon nutrient deprivation, including nitrogen, sulfur, or iron starvation, which is considered an effective condition to promote triacylglycerol (TAG) accumulation in microalgae. Under sulfur starvation, the expression of CrFAP89 was 32.2-fold higher than the control. Furthermore, two lines of RNAi mutants of CrFAP89 were generated by transformation, with gene silencing of 24.9 and 16.4%, respectively. Inhibiting the expression of the CrFAP89 gene drastically increased cell density by 112–125% and resulted in larger cells, that more tolerant to nutrition starvation. However, the content of neutral lipids declined by 12.8–19.6%. The fatty acid content in the transgenic algae decreased by 12.4 and 13.3%, mostly decreasing the content of C16:0, C16:4, C18, and C20:1 fatty acids, while the C16:1 fatty acid in the CrFAP89 RNAi lines increased by 238.5 to 318.5%. Suppressed expression of TAG biosynthesis-related genes, such as CrDGAT1 and CrDGTTs, were detected in CrFAP89 gene silencing cells, with a reduction of 16–78%. Overall our results suggest that down-regulating of the expression of CrFAP89 in C. reinhardtii, resulting in an increase of cell growth and a decrease of fatty acid synthesis with the most significant decrease occurring in C16:0, C16:4, C18, and C20:1 fatty acid. CrFAP89 might be a regulator for lipid accumulation in C. reinhardtii