bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study of complex structural variations of X-linked deafness-2 based on single-molecule sequencing
Authors: Jiang Yi , Wu Lihua , Huang Shasha , Li Pidong , Gao Bo , Yuan Yongyi , Zhang Siwen , Yu Guoliang , Gao Yong , Wu Hao , Dai Pu 
Published: April 2021

Abstract
X-linked deafness-2 (DFNX2) is cochlear incomplete partition type III (IP-III), one of inner ear malformations characterized by an abnormally wide opening in the bone separating the basal turn of the cochlea from the internal auditory canal, fixation of the stapes and cerebrospinal fluid (CSF) gusher upon stapedectomy or cochleostomy. The causative gene of DFNX2 was POU3F4. To investigate the genetic causes of DFNX2 and compare the efficiency of different sequencing methods, 12 unrelated patients were enrolled in the present study. Targeted next-generation sequencing (NGS) and long-read sequencing were used to analyze the genetic etiology of DFNX2. Six variants of POU3F4 were identified in this cohort by NGS. Three patients with a negative diagnosis based on NGS were enrolled in further long-read sequencing. Two of them were all found to carry structural variations (SVs) on chromosome X, consisting of an 870-kb deletion (DEL) at upstream of POU3F4 and an 8-Mb inversion (INV). The 870-kb DEL may have been due to non-homologous end joining (NHEJ), while non-allelic homologous recombination (NAHR) within a single chromatid may have accounted for the 8-Mb INV. Common POU3F4 mutations in DFNX2 included point mutations, small insertions and deletions (INDELs), and exon mutations, which can be detected by Sanger sequencing and NGS. Single-molecule long-read sequencing constitutes an additional and valuable method for accurate detection of pathogenic SVs in IP-III patients with negative NGS results.
More evidence for prediction model of radiosensitivity
Authors: Du Zixuan , Zhang Xinyan , Tang Zaixiang 
Published: April 2021

Abstract
With the development of precision medicine, searching for potential biomarkers plays a major role in personalized medicine. Therefore, how to predict radiosensitivity to improve radiotherapy is a burning question. The definition of radiosensitivity is complex. Radiosensitive gene/biomarker can be useful for predicting which patients would benefit from radiotherapy. The discovery of radiosensitivity biomarkers require multiple pieces of evidence. A prediction model of breast cancer radiosensitivity based on six genes was established. We had put forward some supplements on the basis of the present study. We found that there were no differences between high- and low-risk scores in the non-radiotherapy group. Patients who received radiotherapy had a significantly better overall survival than non-radiotherapy patients in the predicted low-risk score patients. Furthermore, there was no difference between radiotherapy group and non-radiotherapy group in the high-risk score group. Those results firmly supported the prediction model of radiosensitivity. In addition, building a radiosensitivity prediction model was systematically discussed. Genes of model could be screened by different methods, such as Cox regression analysis, Lasso Cox regression method, random forest algorithm and other methods. In the future, precision radiotherapy might depend on the combination of multi-omics data and high dimensional image data.
Gut microbial alterations in neonatal jaundice pre- and post-treatment
Authors: Ding Juan , Ma Xiao , Han Liping , Zhao Xianlan , Li Ang , Xin Qi , Lian Weining , Li Zhen , Ren Hongyan , Ren Zhigang 
Published: April 2021

AbstractNeonatal jaundice is a common disease that affects up to 60% of newborns. Herein, we performed a comparative analysis of the gut microbiome in neonatal jaundice and non-neonatal jaundice infants (NJIs) and identified gut microbial alterations in neonatal jaundice pre- and post-treatment. We prospectively collected 232 fecal samples from 51 infants at five time points (0, 1, 3, 6, and 12 months). Finally, 114 samples from 6 NJIs and 19 non-NJI completed MiSeq sequencing and analysis. We characterized the gut microbiome and identified microbial differences and gene functions. Meconium microbial diversity from NJI was decreased compared with that from non-NJI. The genus Gemella was decreased in NJI versus non-NJI. Eleven predicted microbial functions, including fructose 1,6-bisphosphatase III and pyruvate carboxylase subunit B, decreased, while three functions, including acetyl-CoA acyltransferase, increased in NJI. After treatments, the microbial community presented significant alteration-based β diversity. The phyla Firmicutes and Actinobacteria were increased, while Proteobacteria and Fusobacteria were decreased. Microbial alterations were also analyzed between 6 recovered NJI and 19 non-NJI. The gut microbiota was unique in the meconium microbiome from NJI, implying that early gut microbiome intervention could be promising for the management of neonatal jaundice. Alterations of gut microbiota from NJI can be of great value to bolster evidence-based prevention against ‘bacterial dysbiosis’.
Clonal haematopoiesis of indeterminate potential: intersections between inflammation, vascular disease and heart failure
Authors: Mooney Leanne , Goodyear Carl S. , Chandra Tamir , Kirschner Kristina , Copland Mhairi , Petrie Mark C. , Lang Ninian N. 
Published: April 2021

Abstract
Ageing is a major risk factor for the development of cardiovascular disease (CVD) and cancer. Whilst the cumulative effect of exposure to conventional cardiovascular risk factors is important, recent evidence highlights clonal haematopoiesis of indeterminant potential (CHIP) as a further key risk factor. CHIP reflects the accumulation of somatic, potentially pro-leukaemic gene mutations within haematopoietic stem cells over time. The most common mutations associated with CHIP and CVD occur in genes that also play central roles in the regulation of inflammation. While CHIP carriers have a low risk of haematological malignant transformation (&lt;1% per year), their relative risk of mortality is increased by 40% and this reflects an excess of cardiovascular events. Evidence linking CHIP, inflammation and atherosclerotic disease has recently become better defined. However, there is a paucity of information about the role of CHIP in the development and progression of heart failure, particularly heart failure with preserved ejection fraction (HFpEF). While systemic inflammation plays a role in the pathophysiology of both heart failure with reduced and preserved ejection fraction (EF), it may be of greater relevance in the pathophysiology of HFpEF, which is also strongly associated with ageing. This review describes CHIP and its pathogenetic links with ageing, inflammation and CVD, while providing insight into its putative role in HFpEF.
Non-coding RNA in cancer
Authors: Yan Huiwen , Bu Pengcheng 
Published: April 2021

Abstract
Majority of the human genome is transcribed to RNAs that do not encode proteins. These non-coding RNAs (ncRNAs) play crucial roles in regulating the initiation and progression of various cancers. Given the importance of the ncRNAs, the roles of ncRNAs in cancers have been reviewed elsewhere. Thus, in this review, we mainly focus on the recent studies of the function, regulatory mechanism and therapeutic potential of the ncRNAs including microRNA (miRNA), long ncRNA (lncRNA), circular RNA (circRNA) and PIWI interacting RNA (piRNA), in different type of cancers.
Review of 3D-printing technologies for wearable and implantable bio-integrated sensors
Authors: Rachim Vega Pradana , Park Sung-Min 
Published: April 2021

Abstract
Thin-film microfabrication-based bio-integrated sensors are widely used for a broad range of applications that require continuous measurements of biophysical and biochemical signals from the human body. Typically, they are fabricated using standard photolithography and etching techniques. This traditional method is capable of producing a precise, thin, and flexible bio-integrated sensor system. However, it has several drawbacks, such as the fact that it can only be used to fabricate sensors on a planar surface, it is highly complex requiring specialized high-end facilities and equipment, and it mostly allows only 2D features to be fabricated. Therefore, developing bio-integrated sensors via 3D-printing technology has attracted particular interest. 3D-printing technology offers the possibility to develop sensors on nonplanar substrates, which is beneficial for noninvasive bio-signal sensing, and to directly print on complex 3D nonplanar organ structures. Moreover, this technology introduces a highly flexible and precisely controlled printing process to realize patient-specific sensor systems for ultimate personalized medicine, with the potential of rapid prototyping and mass customization. This review summarizes the latest advancements in 3D-printed bio-integrated systems, including 3D-printing methods and employed printing materials. Furthermore, two widely used 3D-printing techniques are discussed, namely, ex-situ and in-situ fabrication techniques, which can be utilized in different types of applications, including wearable and smart-implantable biosensor systems.
Perspectives for the application of Ustilaginaceae as biotech cell factories
Authors: Wierckx Nick , Miebach Katharina , Ihling Nina , Hussnaetter Kai P. , Büchs Jochen , Schipper Kerstin 
Published: April 2021

Abstract
Basidiomycetes fungi of the family Ustilaginaceae are mainly known as plant pathogens causing smut disease on crops and grasses. However, they are also natural producers of value-added substances like glycolipids, organic acids, polyols, and harbor secretory enzymes with promising hydrolytic activities. These attributes recently evoked increasing interest in their biotechnological exploitation. The corn smut fungus Ustilago maydis is the best characterized member of the Ustilaginaceae. After decades of research in the fields of genetics and plant pathology, a broad method portfolio and detailed knowledge on its biology and biochemistry are available. As a consequence, U. maydis has developed into a versatile model organism not only for fundamental research but also for applied biotechnology. Novel genetic, synthetic biology, and process development approaches have been implemented to engineer yields and product specificity as well as for the expansion of the repertoire of produced substances. Furthermore, research on U. maydis also substantially promoted the interest in other members of the Ustilaginaceae, for which the available tools can be adapted. Here, we review the latest developments in applied research on Ustilaginaceae towards their establishment as future biotech cell factories.
Research on visual‐tactile cross‐modality based on generative adversarial network
Authors: Li Yaoyao , Zhao Huailin , Liu Huaping , Lu Shan , Hou Yueyang 
Published: April 2021

AbstractAiming at the research of assisted blind technology, a generative adversarial network model was proposed to complete the transformation of the mode from vision to touch. Firstly, two key representations of visual to tactile sense are identified: the texture image of the object and the audio frequency that generates vibrotactile. It is essentially a matter of generating audio from images. The authors propose a cross‐modal network framework that generates corresponding vibrotactile signals based on texture images. More importantly, the network structure is an end‐to‐end, which eliminates the traditional intermediate form of converting texture image to spectrum image, and can directly carry out the transformation from visual to tactile. A quantitative evaluation system is proposed in this study, which can evaluate the performance of the network model. The experimental results show that the network can complete the conversion of visual information to tactile signals. The proposed method is proved to be superior to the existing method of indirectly generating vibrotactile signals, and the applicability of the model is verified.
Intelligent flow control algorithm for microservice system
Authors: Li Yudong , Zhang Yuqing , Zhou Zhangbing , Shen LinLin 
Published: April 2021

AbstractIn microservice systems, availability can be ensured through a variety of measures, such as fault tolerance and flow limiting, which are collectively called the flow control. In the current mainstream system design, the flow control rules are usually fixed and set manually, which cannot be dynamically adjusted according to the flow shape. The performance of the system is thus not fully explored. To mitigate this problem, an adaptive dynamic flow control algorithm is proposed. Based on the system's monitoring data and current flow, the algorithm calculates the flow‐limiting threshold in real time, and then it implements fine‐grained service adaptive flow control to improve the resource utilization. Experimental results show that the performance of the adaptive automatic flow control is better than that of the traditional static method on resource utilization.
Research and sustainable design of wearable sensor for clothing based on body area network
Authors: Xiangfang Ren , Lei Shen , Miaomiao Liu , Xiying Zhang , Han Chen 
Published: April 2021

AbstractThe body area network (BAN) is composed of every wearable device network on the body to share information and data, which is applied in medical and health, especially in the direction of intelligent clothing. A wearable device is an integrated body of multi‐sensor fusion. At the same time, the multi‐dimensional needs of users and the unique problems of sensors appear. How to solve the problems of wearable sensors and sustainable design is the research focus. Based on the wearable sensor in the critical factor of wearable device fusion, this paper analyses the classification, technology, and current situation of a wearable sensor, discusses the problems of a wearable sensor for BAN from the aspects of human–computer interaction experience, data accuracy, multiple interaction modes, and battery power supply, and summarizes the direction of multi‐sensor fusion, compatible biosensor materials, and low power consumption and high sensitivity. The sustainable design direction of visibility design, identification of use scenarios, short‐term human–computer interaction, interaction process reduction, and integration invisibility are introduced. The integration research of wearable sensors is the future trend, and it has been widely used in medical and health, intelligent clothing, wireless communication, military, automobile, and other fields.
A novel channel prediction method for MIMO‐OFDM in high‐speed environment
Authors: Zhang Ruiqi , Ai Bo 
Published: April 2021

AbstractThe OFDM system with multiple transceiver antennas is attractive to increase the spectrum or energy efficiency. Depending on the channel state information (CSI), precoding techniques offer array and multiplexing gain in such systems. However, if terminal equipment is fast moving, the CSI feedback would be outdated when it is applied to data transmission. This paper proposes a novel channel prediction method applicable for high‐speed environment. Benefiting from the proposed structure, this method greatly improves the prediction accuracy, and meanwhile retains the low feedback overhead. Numerical results show that the performance of the proposed method outperforms conventional precoding methods in high‐speed scenarios.
Optimising spatial distance for alleviating the compound interference of in‐band full‐duplex D2D communications
Authors: Al‐Doori Bilal Rabah 
Published: April 2021

AbstractThis paper introduces a new methodology that mitigates the impact of interference produced by full‐duplexing transmission in the device‐to‐device (D2D) environment. One of the most significant issues is the approach with which the D2Ds' receivers recognise and decode the outcomes detected from the multi‐codewords sent by the peer D2Ds' transmitters. To address this issue, a novel proposed framework exchanged by the D2D users is applied and guided by two policies. Initially, all the D2Ds' transmitters expressly characterise and interchange the transmission policy regarding the modulation index, number of streams, and the order of the transmitted codewords. Specifically, this work introduces a novel 3GPP‐compliant binary integer optimisation model subject to several practical constraints for defining the transmission policy at the transmitters. Second, a new classification method is proposed, where the Bayesian decision theory is utilised at the D2Ds' receivers for acquiring the decision parameters from the exchanged transmission policy. Hence, this classification offers a determination for each codeword through enabling symbols' estimation and recognition in terms of posterior probability statements. A series of numerical and comprehensive simulations shows that using the proposed approach leads to several improvements on the conventional full‐duplex D2D schemes.
Direction‐based jamming detection and suppression in mmWave massive MIMO networks
Authors: Bagherinejad Saeed , Razavizadeh S. Mohammad 
Published: April 2021

AbstractIn this paper, the problem of physical layer security in the uplink of millimeter‐wave massive multiple‐input multiple‐output (MIMO) networks is studied and a jamming detection and suppression method is proposed. The proposed method is based on directional information of the received signals at the base station antenna array. The proposed jamming detection method can accurately detect both the existence and direction of the jammer using the received pilot signals in the training phase. The obtained information is then exploited to develop a channel estimator that excludes the jammer's angular subspace from received training signals. The estimated channel information is then used for designing a combiner at the base station that is able to effectively cancel out the deliberate interference of the jammer. By numerical simulations, the performance of the proposed jamming detection method is evaluated in terms of correct detection probability and false alarm probability, and its effectiveness when the jammer's power is substantially lower than the user's power is shown. Also, the results show that the proposed jamming suppression method can achieve a very close spectral efficiency as the case of no jamming in the network.
DMT analysis and optimal scheduling for FSO relaying communications
Authors: Khayatian Hassan , Parvaresh Farzad , Abouei Jamshid , Saberali S. Mohammad 
Published: April 2021

AbstractIn this paper, two‐hop parallel N‐relay networks are considered and the diversity‐multiplexing tradeoff (DMT) is derived over Gamma‐Gamma free‐space optical (FSO) channels with identical average received signal to noise rations in all links. In the derivations, both local and global channel state information (CSI) are investigated. In the local CSI case, the node only knows its incoming link conditions, while in the global CSI case, the nodes are aware of all the CSIs in the network. The listening and transmitting times at the relays as variables in the DMT derivation are further considered which are later used to optimize the performance of the network. It is demonstrated that the optimal DMT is obtained with the static quantize map and forward (SQMF) and dynamic quantize map and forward (DQMF) strategies for different ranges of the multiplexing gain. In addition, the optimal schedule of relays in the DQMF strategy is determined as a function of the local channel conditions in the relays.
Fractional order sliding mode control based on single parameter adaptive law for nano‐positioning of piezoelectric actuators
Authors: Zhang Sai , Li Zicheng , Wang Hou‐Neng , Xiong Tao 
Published: April 2021

AbstractA fractional order sliding mode control (FOSMC) based on single parameter adaptive law for nano‐positioning of Piezoelectric Actuators (PEAs) is proposed. First, the Bouc–Wen (B–W) model is used to describe the hysteresis of the nano‐position platform based on PEAs, which provides a mathematical model for the subsequent controller design. Then, theoretical support is provided to design the FOSMC based on adaptive law of different parameters, which are proposed for the displacement tracking problem of PEAs, and the position error convergence is also proved. Moreover, the core parameters of FOSMC based on single parameter adaptive law are identified by hybrid differential evolution (HDE) and adaptive differential evolution (ADE), which require considering the relationship between the scaling factor and the cross‐probability factor. Finally, experiments have been conducted with the displacement signals mixed with multiple frequencies and multiple amplitudes and the results obtained from them show that the proposed control scheme can produce a faster response and smaller tracking errors in PEAs system as compared to traditional control algorithms.
Distance‐based formation control for multi‐lane autonomous vehicle platoons
Authors: Zheng Yajun , Wang Qingling , Cao Dongpu , Fidan Baris , Sun Changyin 
Published: April 2021

AbstractThis paper investigates the formation control of connected autonomous vehicle (CAV) platoons moving in multi lanes using distance‐based formation control techniques based on rigid graphs and V2V communication. A hierarchical architecture is proposed to decompose the cooperative control into velocity planning and vehicle dynamic control. A new velocity planning method is first developed via a distributed distance‐based formation controller so that each vehicle can keep platoon and change lane. Then, for the vehicle dynamics with nonlinearities and bounded disturbances, an adaptive controller is designed for regulating driving/braking torque to achieve the longitudinal velocity output of the velocity planner. The steering controller is designed to adjust the yaw angle of each vehicle to track and change lanes. Furthermore, stability analysis is conducted based on the Lyapunov theory. Finally, the applications of the proposed control designs to various of automated highway system (AHS) scenarios including lane‐change, curve lane and platoon overtaking, are simulated and numerically analysed to validate the effectiveness of theoretical results.
Data‐driven urban traffic model‐free adaptive iterative learning control with traffic data dropout compensation
Authors: Li Dai , Hou Zhongsheng 
Published: April 2021

AbstractIn this paper, to fully utilize the urban traffic flow characteristics of similarity and repeatability without using a mathematical traffic model, a data‐driven urban traffic control strategy based on model‐free adaptive iterative learning control (MFAILC) scheme is put forward. Firstly, by dynamically linearizing the urban traffic dynamics along the iteration axis, the traffic network system is transformed into a MFAILC data model with the help of repetitive pattern of urban traffic flow. Then, the traffic controller is designed based on the derived MFAILC data model only using the I/O data of the traffic network. Finally, a traffic data compensation method is proposed to deal with data dropout problem. Simulation study verifies the feasibility and effectiveness of the proposed control method.