Universal annotation of the human genome through integration of over a thousand epigenomic datasets

Background Genome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative “stacked modeling” approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges. Results Using a version of ChromHMM enhanced for large-scale applications, we apply the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we use in characterizing each state. Compared to per-cell-type annotations, the full-stack annotations directly differentiate constitutive from cell type-specific activity and is more predictive of locations of external genomic annotations. Conclusions The full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome.

In Vitro Wound Dressing Stack Model as a First Step to Evaluate the Behavior of Dressing Materials in Wound Bed—An Assessment of Mass Transport Phenomena in Hydrogel Wound Dressings

Wound dressings when applied are in contact with wound exudates in vivo or with acceptor fluid when testing drug release from wound dressing in vitro. Therefore, the assessment of bidirectional mass transport phenomena in dressing after application on the substrate is important but has never been addressed in this context. For this reason, an in vitro wound dressing stack model was developed and implemented in the 3D printed holder. The stack was imaged using magnetic resonance imaging, i.e., relaxometric imaging was performed by means of T2 relaxation time and signal amplitude 1D profiles across the wound stack. As a substrate, fetal bovine serum or propylene glycol were used to simulate in vivo or in vitro cases. Multi-exponential analysis of the spatially resolved magnetic resonance signal enabled to distinguish components originating from water and propylene glycol in various environments. The spatiotemporal evolution of these components was assessed. The components were related to mass transport (water, propylene glycol) in the dressing/substrate system and subsequent changes of physicochemical properties of the dressing and adjacent substrate. Sharp changes in spatial profiles were detected and identified as moving fronts. It can be concluded that: (1) An attempt to assess mass transport phenomena was carried out revealing the spatial structure of the wound dressing in terms of moving fronts and corresponding layers; (2) Moving fronts, layers and their temporal evolution originated from bidirectional mass transport between wound dressing and substrate. The setup can be further applied to dressings containing drugs.

Investigation of the Ambient Temperature Influence on the PEMFC Characteristics: Modeling from a Single Cell to a Stack

Power supply systems based on air-cooled proton exchange membrane fuel cell (PEMFC) stacks are becoming more popular as power sources for mobile applications. We try to create a PEMFC model that allows for predicting the PEMFC operation in various climatic conditions. A total of two models were developed and used: the membrane electrode assemble (MEA) model and the PEMFC stack model. The developed MEA model allows to determine the influence of external factors (temperature) on the PEMFC power density. The data obtained using the developed model correlate with experimental data at low ambient temperatures (10–30 °C). The difference between the simulation and experimental data is less than 10%. However, the accuracy of the model during PEMFC operation at high (>30 °C) and negative ambient temperatures remains in doubt and requires improvement. The obtained data were integrated into the air-cooled PEMFC stack model. Data of the temperature fields distribution will help to manage the processes in the PEMFC stack. The maximum temperature is slightly above 60 °C, which corresponds to the optimal conditions for the operation of the stack. The temperature gradient across the longitudinal section is very low (<20 °C), which is a positive factor for the chemical reaction. However, the temperature gradient observed across the cross section of the PEMFC stack is 30 °C. The data obtained will help to optimize the mass-dimensional characteristics of air-cooled proton exchange membrane fuel cell and increase their performance. The synergetic effect between the MEA model and the PEMFC stack model can be successfully used in the selection of materials and the development of a thermoregulation system in the PEMFC stack.

Improvement of Temperature and Humidity Control of Proton Exchange Membrane Fuel Cells

Temperature and humidity are two important interconnected factors in the performance of PEMFCs (Proton Exchange Membrane Fuel Cells). The fuel and oxidant humidity and stack temperature in a fuel cell were analyzed in this study. There are many factors that affect the temperature and humidity of the stack. We adopt the fuzzy control method of multi-input and multi-output to control the temperature and humidity of the stack. A model including a driver, vehicle, transmission motor, air feeding, electrical network, stack, hydrogen supply and cooling system was established to study the fuel cell performance. A fuzzy controller is proven to be better in improving the output power of fuel cells. The three control objectives are the fan speed control for regulating temperature, the solenoid valve on/off control of the bubble humidifier for humidity variation and the speed of the pump for regulating temperature difference. In addition, the results from the PID controller stack model and the fuzzy controller stack model are compared in this research. The fuel cell bench test has been built to validate the effectiveness of the proposed fuzzy control. The maximum temperature of the stack can be reduced by 5 °C with the fuzzy control in this paper, so the fuel cell output voltage (power) increases by an average of approximately 5.8%.

An Experience in Enhancing Machine Learning Classifier Against Low-Entropy Packed Malwares

Both benign applications and malwares would take packing for their different purposes to conceal the real part of the program processes. According to recent research reports, existing machine learning (ML) approach-based malware detection engines are difficult to effectively classify the packed malwares, especially when they are in low entropy packed. Recently, we counted and found that the ratio of low-entropy packed ransomware is extremely high. This would cause a high error rate of the result on currently used ML approaches. Thus, we propose a new method to extract entropy-related features and use a stack model to build up an ML malware engine to effectively detect low-entropy packed malwares. We evaluate our method by using over 15,000 malware samples collected from VirusTotal and compare the result to related researches. This experience reports our adopted model and features can significantly lower the error rate of low-entropy packed detection from 11% to 1%.

Optimization of SOFC stack gas distribution structure based on BP Neural network and CFD

The flow field distribution of solid oxide fuel cells significantly affects the performance of the stack. The flow uniformity can be improved and the power generation efficiency can be improved by optimizing the gas distribution structure of the stack. Based on the simplified 6kW stack model, the stack gas distribution structure with two-stage buffer cavity was designed, and the stack model was numerically simulated by ANSYS Fluent software. The BP neural network model, which can predict the uniformity of the outlet of the integrated stack, is established successfully. The parameters of the gas distribution structure are analyzed and optimized by using the orthogonal test and BP neural network. The results show that at the same time considering pile distribution structure under the condition of surface area and uniformity, when the first stage inlet buffer chamber depth is 40 mm, the channel width is 40 mm, the secondary inlet buffer chamber depth is 80 mm, can effectively reduce the electric pile distribution structure, surface area, to reduce heat loss, at the same time guarantee the integrated electric reactor outlet flow uniformity of more than 96%, greatly improves the efficiency of power generation.

Universal annotation of the human genome through integration of over a thousand epigenomic datasets

Genome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative ‘stacked modeling’ approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges. In this paper, using a version of ChromHMM enhanced for large-scale applications, we applied the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, denoted the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we used in characterizing each state. Compared to cell-type-specific annotations, the full-stack annotation directly differentiates constitutive from cell-type-specific activity and is more predictive of locations of external genomic annotations. Overall, the full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing cell-type-specific annotations for studying the non-coding human genome.