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Рис. 7. Варианты преΑсказанной Αоменной структуры моΛекуΛ аΑгезии гемоцитов моΛΛюсков Planorbarius corneus. УсΛовные обозначения и сокращения: 1–3 — β-интегрины, 4–5 — α-интегрины, 6–7 — сеΛектины, 8–11 — моΛекуΛы семейства САМ (сell adhesiom molecues), INB — субъеΑиницы β-интегрина, IntegrinBcyt — цитопΛазматический Αомен β-интегрина, CY — цистатинопоΑобный Αомен, Int alpha — Αомен α-интегрина, FN3 — Αомен фибронектина типа 3, CCP — Αомен контроΛя компΛемента Fig. 7. Variants of the predicted domain structure of adhesion molecules from hemocytes of Planorbarius corneus molluscs. Symbols and abbreviations: 1–3 — β-integrins, 4–5 — α–integrins, 6–7 — selectins, 8–11 — molecules of the СAM family (cell adhesion molecules), INB — β-integrin subunits, IntegrinBcyt — cytoplasmic domain of β-integrin, CY — cystatin-like domain, Int alpha — α-integrin domain, FN3 — fibronectin type 3 domain, CCP — complement control protein domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)

Рис. 7. Варианты преΑсказанной Αоменной структуры моΛекуΛ аΑгезии гемоцитов моΛΛюсков Planorbarius corneus. УсΛовные обозначения и сокращения: 1–3 — β-интегрины, 4–5 — α-интегрины, 6–7 — сеΛектины, 8–11 — моΛекуΛы семейства САМ (сell adhesiom molecues), INB — субъеΑиницы β-интегрина, IntegrinBcyt — цитопΛазматический Αомен β-интегрина, CY — цистатинопоΑобный Αомен, Int alpha — Αомен α-интегрина, FN3 — Αомен фибронектина типа 3, CCP — Αомен контроΛя компΛемента Fig. 7. Variants of the predicted domain structure of adhesion molecules from hemocytes of Planorbarius corneus molluscs. Symbols and abbreviations: 1–3 — β-integrins, 4–5 — α–integrins, 6–7 — selectins, 8–11 — molecules of the СAM family (cell adhesion molecules), INB — β-integrin subunits, IntegrinBcyt — cytoplasmic domain of β-integrin, CY — cystatin-like domain, Int alpha — α-integrin domain, FN3 — fibronectin type 3 domain, CCP — complement control protein domain

opencc-by-4.0Apr 2019View details →

Fig. 3. Predicted relationship between pre-treatment H in Age affects antibody levels and anthelmintic treatment efficacy in a wild rodent

Fig. 3. Predicted relationship between pre-treatment H. polygyrus burden and post-treatment H. polygyrus-specific IgG1 titre for A) juvenile mice, B) young adult mice and C) adult mice. ELW was split into equal thirds to group mice into juvenile (9.2 μg-15.8 μg), young adult (15.8 μg-22.4 μg) or adult mice (22.4 μg- 29.0 μg).

opencc-by-4.0Apr 2019View details →

Fig. 2 in Predicting seasonal infection of eyeworm (Oxyspirura petrowi) and caecal worm (Aulonocephalus pennula) in northern bobwhite quail (Colinus virginianus) of the Rolling Plains Ecoregion of Texas, USA

Fig. 2. Scatterplot of predicted eyeworm reproduction with temperature 60 days prior to collection date with upper and lower 95% confidence intervals.

opencc-by-4.0Apr 2019View details →

Fig. 1 in Predicting seasonal infection of eyeworm (Oxyspirura petrowi) and caecal worm (Aulonocephalus pennula) in northern bobwhite quail (Colinus virginianus) of the Rolling Plains Ecoregion of Texas, USA

Fig. 1. Contour and scatterplot of relationships between temperature and precipitation on parasite worm burdens and egg shedding. a) Predicted caecal worm intensity against temperature and precipitation contour plot. b) Scatterplot of predicted caecal worm reproduction against precipitation. d) Predicted eyeworm reproduction against temperature and precipitation contour plot.

YOLO-CIANNA: Galaxy detection with deep learning, predicted SDC1 source catalogs

Set of detected source catalogs from the SKAO SDC1 560MHz - 1000h challenge image using the YOLO-CIANNA method. This upload is made to accompany the publication of <a title="Cornu et al. (2024)" href="https://ui.adsabs.harvard.edu/abs/2024arXiv240205925C/abstract" target="_blank" rel="noopener">Cornu et al. (2024)</a> and contains catalogs that were produced with the <a title="CIANNA release" href="https://doi.org/10.5281/zenodo.12806325" target="_blank" rel="noopener">CIANNA</a> V-1.0 framework using trained models available at <a title="Network models" href="https://doi.org/10.5281/zenodo.12801421" target="_blank" rel="noopener">10.5281/zenodo.12801421</a>

opencc-by-4.0Dec 2021View details →

Fig. 3 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea

Fig. 3. The Jackknife test for evaluating the relative importance of environmental variables for Betula ermanii in South Korea.

Data - AlphaFold2 Predicts Alternative Conformation Populations in Green Fluorescent Protein Variants

MSAs.zip Multiple sequences alignments generated by AlphaFold2 structure prediction of  7 engineered GFPs. AF2_models_column_masking.zip AlphaFold2 models of the alternative conformations of 7 engineered GFPs. MD_trajectories_PyMOL.zip Molecular dynamics trajectories (PyMOL sessions) of the alternative conformations of 7 engineered GFPs. MD_analysis.zip Root mean square deviation and per-residue root mean square fluctuations along molecular dynamics simulations of 7 engineered GFPs. rmsd_values.zip Root mean square deviation relative to crystallographic GFP structure for AlphaFold2 models and molecular dynamics frames (global and central alpha-helix)

Supplementary material for publication "Multi-Echelon Inventory Optimization in Supply Chain Networks: Exploring Network Structures and Predictive Modeling"

<div> <div> <div> This dataset collects different supply chain network structures generated artificially. We present four types of networks: Serial, Convergent, Divergent, and General, each type consisting of 20,000 individual instances. All 80,000 network instances generated are available to researchers and practitioners in Excel. The repository consists of separate files for each network instance consisting of each network inventory data, node connections, and a visual representation. </div> </div> </div>

Figure 5. Sensory score and period of storage for processed cheese-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

R2 was found to be 96.5 percent of the total variation as explained by sensory scores. Period of storage (days) for which the processed cheese has been in the shelf can be determined based on sensory score (Fig. 5).

Figure 4. Comparison of ASS and PSS for multilayer model R-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

TDNN models with single and multi layers were developed taking soluble nitrogen, pH, standard plate count, yeast & mould count, spore count as input parameters, and sensory score as output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used in order to compare the prediction ability of the developed TDNN models. Regression equations were developed for predicting the shelf life of processed cheese, which came out as 28.25 days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence from the study it can be concluded that TDNN artificial neural network models are quite efficient in predicting shelf life of processed cheese.

Figure 2. Training pattern of TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The Neural Network Toolbox under MATLAB software was used for developing the TDNN models. Training pattern of TDNN models is presented in Fig.2.

Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate count, yeast & mould count, and spore count were taken as input parameters, and sensory score as output parameter for developing TDNN single and multilayer models (Fig.1).

Figure 6. Performance plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 5 we can see that the mse curve reaches the In performance goal but it does not decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these results one can say that RNN is better choice than Feedforward MLP in prediction purpose.

Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

Figure 5. Performance plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 3. Regression plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 3 depicts the regression plot for the feedforward MLP network, analyzing it we can say that Y=T regression is not so good.

Figure 4. Regression plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 4,depicts the regression plot for the Timedelay RNN network, analyzing it we can say that Y=T regression is totally fit. This paper also comprises of comparative study of performance(mse) plot of both network.

Figure 2.Flow Chart for Data preprocessing & Training-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Methodology This paper develops an ANN based comparative predictive model for NASDAQ stock prediction. The first ANN model is developed with Multi-Layer Feed forward Network Architecture & the second model is developed with Recurrent Neural Network Architecture. In this paper gradient descent based back propagation learning algorithm is used for the supervised learning of the predictive network.

opencc-by-4.0Aug 2015View details →

Figure 3. Distribution of Q3 values-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm

The estimated accuracy for the α- helices (QH), β- strands (QE), C-coil states (QC), and three state together (Q3) for the system is shown in Figure 3.