Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or ...pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.
In order to fulfill the increasing demand for renewable energy, besides the lithium-ion batteries, other alkali (Na, K)-ion batteries are extensively investigated. However, the difficulty to find ...universal and environmentally benign electrodes for these alkali (Na, K)-ion batteries still severely restricts their development. Promising characteristics, including molecular diversity, low cost, and operation safety, endow the organic electrodes more advantages for applications in alkali-ion batteries. However, organic electrodes usually deliver a reversible capacity smaller than that of their inorganic counterparts due to sluggish ion/electron diffusion and possible dissolution in organic electrolytes. This work introduces fluorine atoms into the covalent triazine frameworks (CTF) to obtain two-dimensional layered fluorinated CTF (FCTF) and its exfoliated few-layered product (E-FCTF) and uses them as anodes of Li, Na, and K organic batteries. Exfoliated E-FCTF electrode delivers high reversible capacities, as well as excellent cycle life for alkali organic batteries (1035 mAh g–1 at 100 mA g–1 after 300 cycles and 581 mAh g–1 at 2 A g–1 after 1000 cycles for lithium organic batteries). In view of the experimental probing and the theoretical calculation, the Li storage mechanism for the E-FCTF can be determined to be an intriguing multielectronic redox reaction originated from lithium storage on the benzene ring and triazine ring units.
Organic electrodes for low-cost potassium ion batteries (PIBs) are attracting more interest by virtue of their molecular diversity, environmental friendliness, and operation safety. But the sluggish ...potassium diffusion kinetics, dissolution in organic electrolyte, poor electronic conductivity, and low reversible capacities are several drawbacks compared with inorganic counterparts. Herein, the boronic ester based covalent organic framework (COF) material is successfully prepared on the exterior surface of carbon nanotubes (CNTs) via rational design of the organic condensation reaction and used as an anode material for PIBs. The few-layered structure of COF-10@CNT can provide more exposed active sites and fast K+ kinetics. It exhibits ultrahigh potassium storage performances (large reversible capacities of 288 mAh g–1 after 500 cycles at 0.1 A g–1 and 161 mAh g–1 after 4000 cycles at 1 A g–1), which is superior to previous organic electrodes and most inorganic electrodes. Moreover, the K-storage mechanism is proposed to be π-cation interaction between K+ and conjugated π-electrons of benzene rings.
Regenerative braking can improve energy usage efficiency and can prolong the driving distance of electric vehicles (EVs). A creative regenerative braking system (RBS) is presented in this paper. The ...RBS is adapted to brushless dc (BLDC) motor, and it emphasizes on the distribution of the braking force, as well as BLDC motor control. In this paper, BLDC motor control utilizes the traditional proportional-integral-derivative (PID) control, and the distribution of braking force adopts fuzzy logic control. Because the fuzzy reasoning is slower than PID control, the braking torque can be real-time controlled by PID control. In comparison to other solutions, the new solution has better performance in regard to realization, robustness, and efficiency. Then, this paper presents the simulation results by analyzing the battery state of charge, braking force, and dc bus current under the environment of MATLAB and Simulink. The simulation results show that the fuzzy logic and PID control can realize the regenerative braking and can prolong the driving distance of EVs under the condition of ensuring braking quality. At last, it is verified that the proposed method is realizable for practical implementation.
Deep TEN: Texture Encoding Network Hang Zhang; Jia Xue; Dana, Kristin
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
2017-July
Conference Proceeding
Open access
We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a ...single model. Current methods build from distinct components, using standard encoders with separate off-the-shelf features such as SIFT descriptors or pre-trained CNN features for material recognition. Our new approach provides an end-to-end learning framework, where the inherent visual vocabularies are learned directly from the loss function. The features, dictionaries, encoding representation and the classifier are all learned simultaneously. The representation is orderless and therefore is particularly useful for material and texture recognition. The Encoding Layer generalizes robust residual encoders such as VLAD and Fisher Vectors, and has the property of discarding domain specific information which makes the learned convolutional features easier to transfer. Additionally, joint training using multiple datasets of varied sizes and class labels is supported resulting in increased recognition performance. The experimental results show superior performance as compared to state-of-the-art methods using gold-standard databases such as MINC-2500, Flickr Material Database, KTH-TIPS-2b, and two recent databases 4D-Light-Field-Material and GTOS. The source code for the complete system are publicly available1.
With the continuous development of sports in China, the tentacles of the sports industry have extended to all walks of life in China. At the same time, with the development of information and ...network, the information exchange between enterprises and between enterprises and between enterprises and customers is also increasing. How to use the existing information technology to provide enterprises and customers with special information about the sports industry has become a focus of the author’s thinking. Combining the development of our city’s sports industry, based on B/S mode, with ASP.NET for the development of technology, designed specifically for sports enterprises and users to provide an information exchange platform, this paper studies the user-based collaborative filtering recommendation algorithm and its application in sports industry information service management system, to design and develop the corresponding sports industry information service management system.
Owing to the development of electronic devices moving toward high power density, miniaturization, and multifunction, research on thermal interface materials (TIMs) is become increasingly significant. ...Graphene is regarded as the most promising thermal management material owing to its ultrahigh in‐plane thermal conductivity. However, the fabrication of high‐quality vertical graphene (VG) arrays and their utilization in TIMs still remains a big challenge. In this study, a rational approach is developed for growing VG arrays using an alcohol‐based electric‐field‐assisted plasma enhanced chemical vapor deposition. Alcohol‐based carbon sources are used to produce hydroxyl radicals to increase the growth rate and reduce the formation of defects. A vertical electric field is used to align the graphene sheets. Using this method, high‐quality and vertically aligned graphene with a height of 18.7 µm is obtained under an electric field of 30 V cm−1. TIMs constructed with the VG arrays exhibit a high vertical thermal conductivity of 53.5 W m−1 K−1 and a low contact thermal resistance of 11.8 K mm2 W−1, indicating their significant potential for applications in heat dissipation technologies.
An alcohol‐based electric‐field‐assisted plasma enhanced chemical vapor deposition method is developed to grow vertical graphene (VG) arrays with high thermal conductivity. Using this method, high‐quality and vertically aligned graphene sheets at a height of 18.7 µm are obtained. Thermal interface materials constructed with these VG arrays exhibit excellent thermal properties for the heat dissipation of electrical devices.
Colorectal cancer is the third most common cancer in males and second in females. This disease can be caused by genetic and acquired/environmental factors. Microsatellite instability (MSI) is one of ...the major mechanisms in colorectal cancer. This mechanism is a specific condition of genetic hyper mutability that results from incompetent DNA mismatch repair. MSI has been applied to classify different colorectal cancer subtypes. However, the effects of MSI status on gene expression are largely unknown. In our study, we integrated the gene expression profile and MSI status of all CRC samples from the TCGA database, and then categorized the CRC samples into three subgroups, namely, MSI‐stable, MSI‐low, and MSI‐high, according to the MSI status. We applied a novel computational method based on machine learning and screened the genes specifically expressed for the different colorectal cancer subtypes. The results showed the distinct mechanisms of the different colorectal cancer subtypes with MSI status and provided the genes that may be the optimal standards to further classify the various molecular subtypes of colorectal cancer with distinct MSI status.
What's new?
Microsatellite instability (MSI), a key genetic mechanism implicated in colorectal cancer (CRC), is linked to drug reactivity and sensitivity in CRC patients and is useful for CRC subtype classification. Yet, little is known about the identity of MSI‐associated genes or their role in CRC. Here, combined analysis of datasets on gene‐expression profile and MSI status enabled the investigation of a number of differentially expressed genes from CRC samples. Genes optimal for the classification of CRC subtypes with different MSI statuses were identified. The gene panel could facilitate the discovery of biomarkers specific for CRCs with known MSI status.
Many biological tissues offer J-shaped stress-strain responses, since their microstructures exhibit a three-dimensional (3D) network construction of curvy filamentary structures that lead to a ...bending-to-stretching transition of the deformation mode under an external tension. The development of artificial 3D soft materials and device systems that can reproduce the nonlinear, anisotropic mechanical properties of biological tissues remains challenging. Here we report a class of soft 3D network materials that can offer defect-insensitive, nonlinear mechanical responses closely matched with those of biological tissues. This material system exploits a lattice configuration with different 3D topologies, where 3D helical microstructures that connect the lattice nodes serve as building blocks of the network. By tailoring geometries of helical microstructures or lattice topologies, a wide range of desired anisotropic J-shaped stress-strain curves can be achieved. Demonstrative applications of the developed conducting 3D network materials with bio-mimetic mechanical properties suggest potential uses in flexible bio-integrated devices.
While the complementary metal‐oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of ...spiking oscillators called “thermal neuristors”, which operate and interact solely via thermal processes. Utilizing the insulator‐to‐metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. This study establishes the foundation for scalable and energy‐efficient thermal neural networks, fostering progress in brain‐inspired computing.
Targeting a scalable and energy‐efficient thermal neural network, a novel class of spiking oscillators termed “thermal neuristors” is engineered based on the insulator‐to‐metal transition (IMT) in vanadium dioxide. Solely through thermal interactions, a wide variety of reconfigurable functionalities mirroring biological neurons are demonstrated, including cascaded information flow, as well as excitatory and inhibitory interactions, without relying on traditional CMOS‐based circuits.
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