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Periodical: Are usually our little ones getting a reasonable package?
Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps.

Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention.

Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.
Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.
Fatty acid desaturases (FADs) introduce a double bond into the fatty acids acyl chain resulting in unsaturated fatty acids that have essential roles in plant development and response to biotic and abiotic stresses. Wheat germ oil, one of the important by-products of wheat, can be a good alternative for edible oils with clinical advantages due to the high amount of unsaturated fatty acids. Therefore, we performed a genome-wide analysis of the wheat FAD gene family (TaFADs).

68 FAD genes were identified from the wheat genome. Based on the phylogenetic analysis, wheat FADs clustered into five subfamilies, including FAB2, FAD2/FAD6, FAD4, DES/SLD, and FAD3/FAD7/FAD8. The TaFADs were distributed on chromosomes 2A-7B with 0 to 10 introns. The Ka/Ks ratio was less than one for most of the duplicated pair genes revealed that the function of the genes had been maintained during the evolution. Several cis-acting elements related to hormones and stresses in the TaFADs promoters indicated the role of these genes in pe detection of candidate genes for wheat genetic modification.
Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation.

Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score.

The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.
The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.
Acupuncture treatment possesses the neuroprotection potential to attenuate cerebral ischemia-reperfusion (I/R) injury. Endoplasmic reticulum (ER) stress has been suggested to be involved in the pathogenic mechanism of cerebral I/R injury. Whether acupuncture protects against cerebral I/R injury via regulating ER stress remains unclear. This study aimed to evaluate the role of ER stress in the neuroprotection of acupuncture against cerebral I/R injury and its underlying mechanisms.

Cerebral I/R injury was induced by middle cerebral artery occlusion (MCAO) in rats. Acupuncture was carried out at Baihui (GV 20), and Qubin (GB7) acupoints in rats immediately after reperfusion. The infarct volumes, neurological score, ER stress, autophagy and apoptosis were determined.

Acupuncture treatment decreased infarct volume, neurological score and suppressed ER stress via inactivation of ATF-6, PERK, and IRE1 pathways in MCAO rats. Attributing to ER stress suppression, 4-PBA (ER stress inhibitor) promoted the beneficial effect of acupuncture against cerebral I/R injury. RBPJ Inhibitor-1 Notch inhibitor Whereas, ER stress activator tunicamycin significantly counteracted the neuroprotective effects of acupuncture. In addition, acupuncture restrained autophagy via regulating ER stress in MCAO rats. Finally, ER stress took part in the neuroprotective effect of acupuncture against apoptosis in cerebral I/R injury.

Our findings suggest that acupuncture offers neuroprotection against cerebral I/R injury, which is attributed to repressing ER stress-mediated autophagy and apoptosis.
Our findings suggest that acupuncture offers neuroprotection against cerebral I/R injury, which is attributed to repressing ER stress-mediated autophagy and apoptosis.
Imputation accuracy among other things depends on the size of the reference panel, the marker's minor allele frequency (MAF), and the correct placement of single nucleotide polymorphism (SNP) on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50 K to 777 K SNP density using Minimac3, when map positions were determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to the empirical imputation accuracy.

The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R
dose) was almost identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R
dose increased from 0.
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