ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network

With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.Comment: 13 pages, 5 figure

Intrauterine ectopic pregnancy — ultrasound typing and treatment

Objectives: To analyze the correlation between ultrasound typing and treatment modality of patients with an intrauterineectopic pregnancy (cervical and cesarean scar).Material and methods: We retrospectively enrolled 65 patients diagnosed with cesarean scar pregnancy (CSP) or cervicalpregnancy (CP) between February 2014 and May 2018. The cases were divided into two types according to the ultrasoundpresentation with a gestational sac (GS, type I) or a heterogeneous mass (HM, type II). Type I was further divided into typeIa (< 8 weeks) and type Ib (≥ 8 weeks); type II was defined as type IIa (with poor or no vascularity) and type IIb (with richvascularity). Three treatment methods were applied in each group.Results: Of included cases, there were 53 CSP and 12 CP. There was no significant difference between Type I and TypeII groups in any variable. The beta human chorionic gonadotropin (β-hCG) level and gestational age of type IIb weresignificantly higher compared to type IIa (p < 0.05). There was a positive correlation between ultrasound categories andtreatment methods (rs = 0.723, p = 0.000). Analysis of CSP cases of initial treatment failure indicated success rate of initialdilation and curettage (D&C) was dependent upon ultrasonic types, mean sac diameter, gestational age, hCG level, andnumber of cesarean sections.Conclusions: The features of ultrasound imaging might provide an additional reference for the selection of clinical treatmentmethods

Ultra-Sensitive Piezo-Resistive Sensors Constructed with Reduced Graphene Oxide/Polyolefin Elastomer (RGO/POE) Nanofiber Aerogels.

Flexible wearable pressure sensors have received extensive attention in recent years because of the promising application potentials in health management, humanoid robots, and human machine interfaces. Among the many sensory performances, the high sensitivity is an essential requirement for the practical use of flexible sensors. Therefore, numerous research studies are devoted to improving the sensitivity of the flexible pressure sensors. The fiber assemblies are recognized as an ideal substrate for a highly sensitive piezoresistive sensor because its three-dimensional porous structure can be easily compressed and can provide high interconnection possibilities of the conductive component. Moreover, it is expected to achieve high sensitivity by raising the porosity of the fiber assemblies. In this paper, the three-dimensional reduced graphene oxide/polyolefin elastomer (RGO/POE) nanofiber composite aerogels were prepared by chemical reducing the graphene oxide (GO)/POE nanofiber composite aerogels, which were obtained by freeze drying the mixture of the GO aqueous solution and the POE nanofiber suspension. It was found that the volumetric shrinkage of thermoplastic POE nanofibers during the reduction process enhanced the compression mechanical strength of the composite aerogel, while decreasing its sensitivity. Therefore, the composite aerogels with varying POE nanofiber usage were prepared to balance the sensitivity and working pressure range. The results indicated that the composite aerogel with POE nanofiber/RGO proportion of 3:3 was the optimal sample, which exhibits high sensitivity (ca. 223 kPa-1) and working pressure ranging from 0 to 17.7 kPa. In addition, the composite aerogel showed strong stability when it is either compressed with different frequencies or reversibly compressed and released 5000 times

Analysis of Dynamic Characteristics of Pilots Under Different Intentions in Complex Flight Environment

Intention is the main embodiment of human cerebral conscious activities, which has an important influence on guiding the realization of human behaviour. It is a vital prerequisite for analysing the dynamic characteristics of pilots with different intentions. Considering the intention law of the generation, transfer and reduction, this paper analyses dynamic characteristics of pilots with different intentions, starting from the factors of effect on the intention. Taking airfield traffic pattern as an example for simulating flight experiments, the pilot’s multi-source dynamic data of human – aircraft – environment system under different intentions and their psycho-physiological-physical characteristics were recorded. Based on Matlab, one-way analysis of variance was used to extract variables with significant changes, and the variables under different intentions were compared and analysed. The results show that the conventional pilots are more conducive to control the aircraft to keep a stable flight attitude. This study is of great significance for perfecting the warning system of flight safety and improving the pilot’s micro-behaviour assessment system.</p

Research and application of fire air and smoke flow emergency control technology for mine complex ventilation network

In view of the difficult situation of fire prevention, control, disaster resistance and relief in complex ventilation networks, the laws of air flow disturbance, heat transfer and smoke flow diffusion in air flow field, temperature field and smoke flow concentration field are analyzed. According to the evolution law of smoke flow at different fire source locations, an emergency linkage regulation scheme of fire air and smoke flow that can simultaneously meet the requirements of smoke exhaust air volume and stope air volume is proposed. Taking Zhuanlongwan Coal Mine as an application case, the fire smoke emergency linkage control system of Zhuanlongwan Coal Mine was established. In order to analyze the effect of air control and smoke exhaust, combined with the actual tunnel and ventilation parameters, a three-dimensional visualization model of its complex ventilation network was built based on VENTSIM software. The VentFire module is used to simulate the wind and smoke spreading effect of the fire scene in different fire source locations. The smoke exhaust effect and air volume distribution before and after the emergency control scheme is adopted are analyzed. The results show that when a fire occurs in different locations, the emergency smoke exhaust scheme should be started in time. On the basis of meeting the smoke exhaust air volume, the air volume of other key locations can still ensure more than 90% of the normal ventilation period, which can meet the disaster control and relief needs during the disaster of Zhuanlongwan Coal Mine

Modelling domain relationships for transfer learning on retrieval-based question answering systems in e-commerce

In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource- poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding- based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.Comment:

Alzheimer\u27s Biomarkers are Correlated with Brain Connectivity in Older Adults Differentially During Resting and Task States

β-amyloid (Aβ) plaques and tau-related neurodegeneration are pathologic hallmarks of Alzheimer’s disease (AD). The utility of AD biomarkers, including those measured in cerebrospinal fluid (CSF), in predicting future AD risk and cognitive decline is still being refined. Here, we explored potential relationships between functional connectivity (FC) patterns within the default-mode network (DMN), age, CSF biomarkers (Aβ42 and pTau181), and cognitive status in older adults. Multiple measures of FC were explored, including a novel time series-based measure [total interdependence (TI)]. In our sample of 27 cognitively normal older adults, no significant associations were found between levels of Aβ42 or pTau181 and cognitive scores or regional brain volumes. However, we observed several novel relationships between these biomarkers and measures of FC in DMN during both resting-state and a short-term memory task. First, increased connectivity between bilateral anterior middle temporal gyri was associated with higher levels of CSF Aβ42 and Aβ42/pTau181 ratio (reflecting lower AD risk) during both rest and task. Second, increased bilateral parietal connectivity during the short-term memory task, but not during rest, was associated with higher levels of CSF pTau181 (reflecting higher AD risk). Third, increased connectivity between left middle temporal and left parietal cortices during the active task was associated with decreased global cognitive status but not CSF biomarkers. Lastly, we found that our new TI method was more sensitive to the CSF Aβ42-connectivity relationship whereas the traditional cross-correlation method was more sensitive to levels of CSF pTau181 and cognitive status. With further refinement, resting-state connectivity and task-driven connectivity measures hold promise as non-invasive neuroimaging markers of Aβ and pTau burden in cognitively normal older adults

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient’s quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients.Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach.Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis.Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC