DeeProPre: A promoter predictor based on deep learning.

The promoter is a DNA sequence recognized, bound and transcribed by RNA polymerase. It is usually located at the upstream or 5'end of the transcription start site (TSS). Studies have shown that the structure of the promoter affects its affinity for RNA polymerase, thus affecting the level of gene expression. Therefore, the correct identification of core promoter and common structural gene is of great significance in the field of biomedicine. At present, many methods have been proposed to improve the accuracy of promoter recognition, but the performances still need to be further improved. In this study, a deep learning algorithm (DeeProPre) based on bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) was proposed. Firstly, the supervised embedding layer was applied to map the sequence to a high-dimensional space. Secondly, two 1D convolutional layers, BiLSTM and attentional mechanism layer were used for extracting features. Finally, the full connection layer activated by Sigmoid function was used to obtain the probability of classification into target categories. This model can identify the promoter region of eukaryotes with high accuracy, providing an analytical basis for further understanding of promoter physiological functions and studies of gene transcription mechanisms. The source code of DeeProPre is freely available at https://github.com/zzwwmmm/DeeProPre/tree/master.

[Effects of enhanced-efficiency nitrogen fertilizers on nitrous oxide emissions from cotton field under plastic mulched drip irrigation in Xinjiang,China]. / 高效氮肥对新疆膜下滴灌棉田土壤氧化亚氮排放的影响.

The effect of enhanced-efficiency nitrogen (N) fertilizers on emissions of nitrous oxide (N2O) from the grey desert agricultural soils of Xinjiang is uncertain. In this study, the enhanced-efficiency fertilizers, polymer-coated urea (ESN), and stabilized urea with urease and nitrification inhibitors (U+I) were compared to conventional urea (U) for N2O emissions from cotton under plastic mulch drip irrigation near Urumqi, Xinjiang. ESN was added once at planting but the other treatments were added multiple times with drip irrigation during the growing season. Gas samples were collected and analyzed twice per week during the growing season, using the static chamber-chromatography methodology. The results showed that generally, ESN significantly increased soil cumulative N2O emissions during the growing season by 47%-73% compared to other treatments. In the first four months after fertilization, soil ammonium (NH4+-N) and nitrate (NO3--N) concentrations under ESN treatment were generally higher than under other treatments. Thereafter, NH4+-N and NO3--N concentrations under all treatments gradually decreased to similar levels. ESN all added at planting was likely responsible for high NH4+-N and NO3--N concentrations and highest N2O emissions. The U+I treatment reduced soil N2O emission by 9.9% in comparison with U, whereas the difference was not statistically significant. In addition, soil NO3--N contents of the U+I treatments were generally lower than those of the ESN and the U treatments. The cumulative N2O emissionsover the growing season ranged from 300 to 500 g N2O-N·hm-2, generally lower than emissions reported for other agricultural ecosystems. Drip irrigation successfully kept moisture conditions below levels for appreciable N2O emissions. Multiple applications of N via drip irrigation seemed to be effective to lower emissions than all N applied at planting. Therefore, for cotton field under plastic mulch drip irrigation in arid land of Northwest China, the benefit of enhanced efficiency N ferti-lizers on N2O mitigation is limited.

[Value of multi-slice computed tomography in diagnosis of coronary plaque characterization].

OBJECTIVE: To investigate the value of multi-slice computed tomography in diagnosis of different types of coronary atherosclerotic plaques. METHODS: Twenty-eight patients undergoing CT angiography (CTA) with normal coronary arteries were randomly selected to measure the CT values of different sections of the 4 main branches of coronary artery. Twenty-five specimens of human heart from the bodies of the patients who died of non-cardiogenic diseases were scanned by 16-slice CT scanner and 64-slice CT scanner: a mixture of CT contrast media and normal saline was injected into the coronary arteries to achieve in-vivo-like contrast enhancement within the coronary artery lumen to detect atherosclerotic plaques. The CT values of plaques were measured in several regions of interest (ROI) selected in each plaque. The CT images thus obtained were evaluated by 2 experienced radiologists. There are nine specimens with coronary atherosclerotic plaques among them. Then the atherosclerotic lesions in the coronary were made into tissue specimens to undergo pathological examination. RESULTS: 7560 CT values were obtained from the 28 patients. Thirty-eight atherosclerotic plaques were found by CAT in 9 heart specimens and confirmed by pathology. When the CT value of coronary lumen was 370 HU, the predominant lipid-rich plaque showed a mean CT value of 53 +/- 12 HU; the fibrous-rich plaque showed a mean CT value of 106 +/- 17 HU; and the calcified plaque showed a mean CT value of 429 +/- 94 HU measured by 16-slice CT; and the predominant lipid-rich plaque showed a mean CT value of 51 +/- 13 HU; the fibrous-rich plaque showed a mean CT value of 110 +/- 19 HU; and the calcified plaque showed a mean CT value of 435 +/- 87 HU measured by 64-slice CT. The CT value of the fibrous-rich plaque was significantly higher than that of the lipid-rich plaque (P = 0.008), and lower than that of the calcified plaque (P < 0.01). There was no significant difference between the results obtained by the two kinds of CT scanners. CONCLUSION: CTA can non-invasively assess the atherosclerotic plaques.