scholarly journals TLCrys: Transfer Learning Based Method for Protein Crystallization Prediction

2022 ◽  
Vol 23 (2) ◽  
pp. 972
Author(s):  
Chen Jin ◽  
Zhuangwei Shi ◽  
Chuanze Kang ◽  
Ken Lin ◽  
Han Zhang

X-ray diffraction technique is one of the most common methods of ascertaining protein structures, yet only 2–10% of proteins can produce diffraction-quality crystals. Several computational methods have been proposed so far to predict protein crystallization. Nevertheless, the current state-of-the-art computational methods are limited by the scarcity of experimental data. Thus, the prediction accuracy of existing models hasn’t reached the ideal level. To address the problems above, we propose a novel transfer-learning-based framework for protein crystallization prediction, named TLCrys. The framework proceeds in two steps: pre-training and fine-tuning. The pre-training step adopts attention mechanism to extract both global and local information of the protein sequences. The representation learned from the pre-training step is regarded as knowledge to be transferred and fine-tuned to enhance the performance of crystalization prediction. During pre-training, TLCrys adopts a multi-task learning method, which not only improves the learning ability of protein encoding, but also enhances the robustness and generalization of protein representation. The multi-head self-attention layer guarantees that different levels of the protein representation can be extracted by the fine-tuned step. During transfer learning, the fine-tuning strategy used by TLCrys improves the task-specialized learning ability of the network. Our method outperforms all previous predictors significantly in five crystallization stages of prediction. Furthermore, the proposed methodology can be well generalized to other protein sequence classification tasks.

2010 ◽  
Vol 43 (5) ◽  
pp. 1078-1083 ◽  
Author(s):  
Cory J. Gerdts ◽  
Glenn L. Stahl ◽  
Alberto Napuli ◽  
Bart Staker ◽  
Jan Abendroth ◽  
...  

The Microcapillary Protein Crystallization System (MPCS) is a microfluidic, plug-based crystallization technology that generates X-ray diffraction-ready protein crystals in nanolitre volumes. In this study, 28 out of 29 (93%) proteins crystallized by traditional vapor diffusion experiments were successfully crystallized by chemical gradient optimization experiments using the MPCS technology. In total, 90 out of 120 (75%) protein/precipitant combinations leading to initial crystal hits from vapor diffusion experiments were successfully crystallized using MPCS technology. Many of the resulting crystals produced high-quality X-ray diffraction data, and six novel protein structures that were derived from crystals harvested from MPCS CrystalCards are reported.


Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4850 ◽  
Author(s):  
Carlos S. Pereira ◽  
Raul Morais ◽  
Manuel J. C. S. Reis

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.


2021 ◽  
Vol 11 (7) ◽  
pp. 671
Author(s):  
Oihane Pikatza-Menoio ◽  
Amaia Elicegui ◽  
Xabier Bengoetxea ◽  
Neia Naldaiz-Gastesi ◽  
Adolfo López de Munain ◽  
...  

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder that leads to progressive degeneration of motor neurons (MNs) and severe muscle atrophy without effective treatment. Most research on ALS has been focused on the study of MNs and supporting cells of the central nervous system. Strikingly, the recent observations of pathological changes in muscle occurring before disease onset and independent from MN degeneration have bolstered the interest for the study of muscle tissue as a potential target for delivery of therapies for ALS. Skeletal muscle has just been described as a tissue with an important secretory function that is toxic to MNs in the context of ALS. Moreover, a fine-tuning balance between biosynthetic and atrophic pathways is necessary to induce myogenesis for muscle tissue repair. Compromising this response due to primary metabolic abnormalities in the muscle could trigger defective muscle regeneration and neuromuscular junction restoration, with deleterious consequences for MNs and thereby hastening the development of ALS. However, it remains puzzling how backward signaling from the muscle could impinge on MN death. This review provides a comprehensive analysis on the current state-of-the-art of the role of the skeletal muscle in ALS, highlighting its contribution to the neurodegeneration in ALS through backward-signaling processes as a newly uncovered mechanism for a peripheral etiopathogenesis of the disease.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young Jae Kim ◽  
Jang Pyo Bae ◽  
Jun-Won Chung ◽  
Dong Kyun Park ◽  
Kwang Gi Kim ◽  
...  

AbstractWhile colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.


2021 ◽  
Vol 29 (1) ◽  
pp. 19-36
Author(s):  
Çağín Polat ◽  
Onur Karaman ◽  
Ceren Karaman ◽  
Güney Korkmaz ◽  
Mehmet Can Balcı ◽  
...  

BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Tam ◽  
Mounir Boukadoum ◽  
Alexandre Campeau-Lecours ◽  
Benoit Gosselin

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.


2014 ◽  
Vol 70 (a1) ◽  
pp. C613-C613
Author(s):  
Jan Stránský ◽  
Tomáš Kovaľ ◽  
Lars Østergaard ◽  
Jarmila Dušková ◽  
Tereza Skálová ◽  
...  

Development of X-ray diffraction technologies have made de novo phasing of protein structures by single-wavelength anomalous dispersion by sulphur (S-SAD) more common. As anomalous differences in the sulphur atomic factors are in the order of errors of measurement, careful intensity reading and data processing are crucial. S-SAD was used for de novo phasing of a small 12 kDa protein with 4 sulphur atoms per molecule at 2.3 Å, where the data did not enable a straightforward structure solution. Data processing was performed using XDS [1] and scaling using XSCALE. The sulphur substructure was determined by SHELXD [2] and phases were obtained from SHELXE [2]. Both algorithms strongly depend on input parameters and default values did not lead to the correct phases. Therefore a systematic search of optimal values of several parameters was used to find a solution. This method helped to confirm sulphur substructure and to differentiate the handedness of the solutions. Moreover, a script for comfortable conversion of SHELX outputs to MTZ format was developed, using programmes included in the CCP4 package [3]. The previously unsolvable protein structure was successfully resolved with the described procedure. This work was supported by the Grant Agency of the Czech Technical University in Prague, (SGS13/219/OHK4/3T/14), the Czech Science Foundation (P302/11/0855), project BIOCEV CZ.1.05/1.1.00/02.0109 from the ERDF.


2020 ◽  
Vol 10 (11) ◽  
pp. 3833 ◽  
Author(s):  
Haidar Almubarak ◽  
Yakoub Bazi ◽  
Naif Alajlan

In this paper, we propose a method for localizing the optic nerve head and segmenting the optic disc/cup in retinal fundus images. The approach is based on a simple two-stage Mask-RCNN compared to sophisticated methods that represent the state-of-the-art in the literature. In the first stage, we detect and crop around the optic nerve head then feed the cropped image as input for the second stage. The second stage network is trained using a weighted loss to produce the final segmentation. To further improve the detection in the first stage, we propose a new fine-tuning strategy by combining the cropping output of the first stage with the original training image to train a new detection network using different scales for the region proposal network anchors. We evaluate the method on Retinal Fundus Images for Glaucoma Analysis (REFUGE), Magrabi, and MESSIDOR datasets. We used the REFUGE training subset to train the models in the proposed method. Our method achieved 0.0430 mean absolute error in the vertical cup-to-disc ratio (MAE vCDR) on the REFUGE test set compared to 0.0414 obtained using complex and multiple ensemble networks methods. The models trained with the proposed method transfer well to datasets outside REFUGE, achieving a MAE vCDR of 0.0785 and 0.077 on MESSIDOR and Magrabi datasets, respectively, without being retrained. In terms of detection accuracy, the proposed new fine-tuning strategy improved the detection rate from 96.7% to 98.04% on MESSIDOR and from 93.6% to 100% on Magrabi datasets compared to the reported detection rates in the literature.


2013 ◽  
Vol 6 (1) ◽  
pp. 308 ◽  
Author(s):  
Mikael Elias ◽  
Dorothee Liebschner ◽  
Jurgen Koepke ◽  
Claude Lecomte ◽  
Benoit Guillot ◽  
...  

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