bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinicopathological characteristics and prognostic factors of cervical adenocarcinoma
Authors: Wang Min , Yuan Bo , Zhou Zhen-huan , Han Wei-wei 
Published: April 2021

AbstractWe aimed to assess the clinicopathological features and to determine the prognostic factors of cervical adenocarcinoma (AC). Relevant data were extracted from surveillance, epidemiology and end results database from 2004 to 2015. The log-rank test and Cox proportional hazard analysis were subsequently utilized to identify independent prognostic factors. A total of 3102 patients were identified. The enrolled patients were characterized by higher proportion of early FIGO stage (stage I: 65.9%; stage II: 14.1%), low pathological grade (grade I/II: 49.1%) and tumor size ≤ 4 cm (46.8%). The 5- and 10-year cancer-specific survival rates of these patients were 74.47% and 70.00%, respectively. Meanwhile, the 5- and 10-year overall survival (OS) rates were 71.52% and 65.17%, respectively. Multivariate analysis revealed that married status, surgery as well as chemotherapy were independent favorable prognostic indicators. Additionally, aged > 45, tumor grade III/IV, tumor size > 4 cm, advanced FIGO stage and pelvic lymph node metastasis (LNM) were unfavorable prognostic factors (all P < 0.01). Stratified analysis found that patients without surgery could significantly benefit from chemotherapy and radiotherapy. In addition, chemotherapy could significantly improve the survival in stage II–IV patients and radiotherapy could only improve the survival in stage III patients (all P < 0.01). Marital status, age, grade, tumor size, FIGO stage, surgery, pelvic LNM and chemotherapy were significantly associated with the prognosis of cervical AC.
Noble-metal-free hydroxyapatite activated by facile mechanochemical treatment towards highly-efficient catalytic oxidation of volatile organic compound
Authors: Xin Yunzi , Shirai Takashi 
Published: April 2021

AbstractControlling of volatile organic compound (VOC) emitted from industrial processes as most abundant and harmful air pollutant, has become one of the most important global environmental issues due to the rapid urbanization and industrialization. As an alternative and new type catalyst instead of conventional noble-metal nanoparticles widely utilized in oxidative decomposition of VOC, here we report the superior catalytic performance with 100% CO2/CO conversion on hydroxyapatite (HAp, Ca10(PO4)6(OH)2) with structurally well-controlled active surface tailored via facile one-step mechanochemical treatment in ambient air. With detailed characterizations of particle morphology, crystallinity and chemical structure with respects to surface defect/oxygen vacancy formation, acidity/basicity and VOC affinity on HAps activated through different mechanical stresses when altered ball size is utilized in planetary ball-milling assisted mechanochemical process, it was found that the predominant defect/oxygen vacancy generation in PO43− site and enhanced basic site population established by selective mechanochemical activation of c-plane, facilitates the favorable catalytic oxidation route towards highly-efficient CO2/CO conversion of VOC. Regards to the cost-effectiveness and non-toxic nature of HAp, incorporated with the sustainable mechanochemical surface structure tuning process, the results presented in this work opens new strategy in development of novel noble-metal-free catalyst for VOC elimination and environmental cleaning techniques.
The effect of coronary slow flow on left atrial structure and function
Authors: Shui Zhiyuan , Wang Yunzhi , Sun Mingxue , Gao Yiqun , Liang Shunji , Wang Yiran , Wang Xiaomei , Yu Qin , Zhang Shulong , Liu Li 
Published: April 2021

AbstractThe coronary slow flow phenomenon (CSFP) is common in coronary angiography, however its impact on left atrial (LA) function is still controversial. This study aims to evaluate the LA structure and function of patients with CSFP using two-dimensional speckle tracking echocardiography (2D-STE). Consecutive patients scheduled for coronary angiography from January 2016 to September 2017 were enrolled in this study. Patients’ demographic data, clinical histories, laboratory and angiographic findings were collected and recorded. Diagnostic criteria for CSFP is based on Beltrame et al. proposed in 2012. Meanwhile 139 patients who have no significant stenosis (≤ 40%) and normal blood flow were selected as control. All patients received an echocardiographic examination 24 h before coronary angiography. LA structure and function were measured with echocardiography and 2D-STE. Our results showed that among the 1,954 patients who had received coronary angiography, 512 patients were included in the analysis after the exclusion criteria was implemented. Of those, 101 patients met the CSFP criteria (5.5%). CSFP is mainly seen in LAD (~ 70%). There was no statistical difference in baseline characteristics between the CSFP group and control group, except for a higher proportion of smokers in the CSFP group (P = 0.001). The percentage of monocytes is an independent risk factor for the occurrence of CSFP (P = 0.036) after binary logistic regression analysis. The LA global longitudinal strain (LA-GLS, represents reservoir functions) decreased and LA strain rate at late diastole (LA-SRa, represents booster function) increased in patients with CSFP compared to the control group (P < 0.05). Correlation test of continuous variables by Pearson test suggested that LA-GLS was negatively correlated with TIMI frame count (TFC). We concluded that the percentage of monocytes is an independent risk factor for the CSFP; the LA reservoir and booster functions were impaired in patients with CSFP; LA-GLS is negatively correlated with TFC.
Protecting artificial intelligence IPs: a survey of watermarking and fingerprinting for machine learning
Authors: Regazzoni Francesco , Palmieri Paolo , Smailbegovic Fethulah , Cammarota Rosario , Polian Ilia 
Published: April 2021

AbstractArtificial intelligence (AI) algorithms achieve outstanding results in many application domains such as computer vision and natural language processing. The performance of AI models is the outcome of complex and costly model architecture design and training processes. Hence, it is paramount for model owners to protect their AI models from piracy – model cloning, illegitimate distribution and use. IP protection mechanisms have been applied to AI models, and in particular to deep neural networks, to verify the model ownership. State‐of‐the‐art AI model ownership protection techniques have been surveyed. The pros and cons of AI model ownership protection have been reported. The majority of previous works are focused on watermarking, while more advanced methods such fingerprinting and attestation are promising but not yet explored in depth. This study has been concluded by discussing possible research directions in the area.
Bloody Mahjong playing strategy based on the integration of deep learning and XGBoost
Authors: Gao Shijing , Li Shuqin 
Published: April 2021

AbstractBloody Mahjong is a kind of mahjong. It is very popular in China in recent years. It not only has the characteristics of mahjong's conventional state space, huge hidden information, complicated rules, and large randomness of hand cards but also has special rules such as Change three, Hu must lack at least one suit, and Continue playing after Hu. These rules increase the difficulty of research. These special rules are used as the input of the deep learning DenseNet model. DenseNet is used to extract the Mahjong situation features. The learned features are used as the input of the classification algorithm XGBoost, and then the XGBoost algorithm is used to derive the card strategy. Experiments show that the fusion model of deep learning and XGBoost proposed in this paper has higher accuracy than the single model using only one of them in the case of high‐dimensional sparse features. In the case of fewer training rounds, accuracy of the model can still reach 83%. In the games against real people, it plays like human.
Resource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms
Authors: Karthik Priyadarsini , Sekhar Karthik 
Published: April 2021

AbstractMany organizations around the world use cloud computing Testing as Service (Taas) for their services. Cloud computing is principally based on the idea of on‐demand delivery of computation, storage, applications, and additional resources. It depends on delivering user services through Internet connectivity. In addition, it uses a pay‐as‐you‐go business design to deliver user services. It offers some essential characteristics including on‐demand service, resource pooling, rapid elasticity, virtualization, and measured services. There are various types of virtualization, such as full virtualization, para‐virtualization, emulation, OS virtualization, and application virtualization. Resource scheduling in Taas is among the most challenging jobs in resource allocation to mandatory tasks/jobs based on the required quality of applications and projects. Because of the cloud environment, uncertainty, and perhaps heterogeneity, resource allocation cannot be addressed with prevailing policies. This situation remains a significant concern for the majority of cloud providers, as they face challenges in selecting the correct resource scheduling algorithm for a particular workload. The authors use the emergent artificial intelligence algorithms deep RM2, deep reinforcement learning, and deep reinforcement learning for Taas cloud scheduling to resolve the issue of resource scheduling in cloud Taas.
Performance analysis of hybrid MPAPM technique for deep‐space optical communications
Authors: Idris Sadiq , Selmy Hossam , Lopes Waslon Terllizzie A. 
Published: April 2021

AbstractA hybrid multiple pulse amplitude and position modulation (H‐MPAPM) scheme based on gradual multi‐pulse pulse‐position modulation (G‐MPPM) and multi‐level pulse amplitude modulation (M‐PAM) schemes is proposed for deep‐space optical communication. In this scheme, transmitted information is conveyed by different combinations of the positions and amplitudes of multiple optical pulses. The performance of the proposed scheme with multi‐pulse pulse‐position modulation (MPPM), G‐MPPM and multiple pulse amplitude and position modulation (M‐PAPM) schemes in terms of the number of transmitted symbols, data rate, symbol error rate (SER), bit error rate (BER), bandwidth utilization efficiency and power requirements are studied in this work. Our results reveal that, at the same frame size, the proposed scheme allows much more symbols per frame transmission, achieves higher transmission energy and further enhance the bandwidth utilization efficiency of MPPM, G‐MPPM and M‐PAPM schemes. Moreover, compared to the MPPM and G‐MPPM, the proposed scheme achieves lower levels of SERs but at the expense of peak power levels. Furthermore, the proposed scheme outperforms G‐MPPM and MPPM schemes by approximately 1 and 2 dB at a BER of 10−6, respectively. In addition, the analytical expressions for the BER and SER of the proposed scheme are validated using Monte Carlo (MC) simulations.
Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence‐based methods
Authors: Hua Haochen , Wei Zhiqian , Qin Yuchao , Wang Tonghe , Li Liuying , Cao Junwei 
Published: April 2021

AbstractEnergy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI‐based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems.
Energy management in a cloud‐based cyber‐physical system
Authors: Orumwense Efe F. , Abo‐Al‐Ez Khaled M. 
Published: April 2021

AbstractCyber‐physical systems (CPSs) are embodied systems of highly unified computational, control and communicational elements tightly fused with the physical world. Normally, CPSs are seen to have limited storage and computational abilities due to the fact that they are implemented across several platforms and also embedded into larger systems. The fusion of Cloud computing and CPS gives rise to Cloud‐based CPS; Cloud computing will no doubt provide numerous opportunities for CPSs to increase their capabilities by taking advantage of the resources (applications, servers, storage and network capabilities). With this new addition, there will definitely be an increase in energy consumption in the system and this becomes a huge and daunting task that needs to be overcome in actualising this goal. Here, the energy consumption in the network system is being evaluated to ensure an effective data transmission amongst sensor nodes. A simulation environment is considered where a particle swarm optimization algorithm is introduced to optimise and balance the consumption of energy in the system network. To ensure an energy‐efficient Cloud data control center, an energy consumption model is developed based on resource utilisation. This, however, assists in managing the amount of resources to be utilised for a specific amount of workload.
DC‐presaturated fault current limiter for high voltage direct current transmission systems
Authors: Eladawy Mohamed , Metwally Ibrahim A. , Tarhuni Naser G. 
Published: April 2021

AbstractThe authors present a 500 kV real‐dimension DC‐presaturated fault current limiter (PFCL) with a steady‐state rated current of 2 kA for limiting large fault current of 8 kA with high rate of rise. These characteristics of the fault current can be a threat to high voltage direct current (HVDC) transmission systems, and hence PFCL design and performance improvement are investigated through three‐dimensional, time‐domain, magnetic‐field and electric‐circuit coupled model using finite element simulation of COMSOL Multiphysics package. The nonlinear magnetic characteristics of the soft magnet ensure variable inductance depending on demagnetisation magnetomotive force generated by the line current. This DC‐biased PFCL can replace the traditional smoothing reactor during the normal operation of the system by controlling the level of presaturation. In addition, PFCL is a self‐triggered device and therefore it can automatically limit the fault current and reduce its value below the interruption rating of the used HVDC circuit breakers (CBs). Moreover, the rate of rise of the fault current can be controlled to ensure the compatibility with the available type of HVDC CBs. The dynamic performance of PFCL is investigated during fault condition through the fault current clipping ratio and rate of rise of the fault current, and during steady‐state operation through the voltage drop across PFCL and its power losses. It is found that the proposed PFCL presents adequate capability in limiting large fault currents with extremely low values of voltage drop and power losses during the steady‐state condition. However, the switching transient overvoltages that appear at terminals of PFCL coils during the fault duration have been successfully suppressed by carefully selected ratings of zinc‐oxide surge arresters.
Polypropylene‐based nanocomposites for HVDC cable insulation
Authors: Adnan Muhammad , Abdul‐Malek Zulkurnain , Lau Kwan Yiew , Tahir Muhammad 
Published: April 2021

AbstractCross‐linked polyethylene (XLPE) is commonly used as an insulation material in power cables. Due to the recent advancements in the field of high voltage power transmission and distribution, there is a need for novel cable insulation materials that have high performance, recyclability and high working temperature as alternatives for the conventional XPLE‐based insulation materials. Polypropylene (PP) shows excellent properties and has drawn considerable attention as a potential high voltage direct current (HVDC) insulation material. Therefore, the development of PP‐based HVDC cable insulation with improved electrical, thermal and mechanical properties is important in discovering a potentially recyclable cable insulation material. Due to the remarkable development in the field of nanodielectrics, nanotechnology can be a promising solution for enhancing the overall dielectric properties of PP‐based insulation materials. This review presents the important aspects of PP‐based nanocomposites for HVDC cable insulation with a special focus on understanding the effects of various parameters of nanofillers on the dielectric properties of PP‐based HVDC cable insulation. Based on the gathered information, future perspectives for improving the dielectric properties of PP‐based nanocomposites for HVDC cable are provided.
Self-integrating current sensor for fast pulsed current monitor in transmission line
Authors: Wang Jiachen , Ding Weidong , Su Fangfei , Zhang Xiwen , Wan Zhenbo , Qiu Aici 
Published: April 2021

The fast linear transformer driver (FLTD) utilizes a water-insulated transmission line as its secondary. To monitor the fast pulsed current and locate the fault, a compact self-integrating current sensor is developed. Print circuit board (PCB) coils and PCB integrating resistors are used to form the current sensor. By soldering a large number of chip resistors, PCB integrating resistors with various resistance and low inductance can be obtained. The current sensor is designed in a coaxial structure to reduce its inductance and size and can be installed on the inner conductor of the FLTD’s secondary water-insulated transmission line with a small opening. The principle and matching schemes for the current sensor are theoretically analyzed with the circuit principle. Both simple matching and two-stage division can be used to obtain signals without oscillation. The time constant of the two schemes is the same. However, the sensitivity of the current sensor with two-stage division is lower than that with simple matching. A 100 kV step pulse generator is used as the pulsed high current generator to verify the properties of the current sensor. The designed current sensor could respond to the step current pulse with the rise time of 4 ns. The matching scheme is verified to be effective with step response experiments. Finally, the influence of the parameters, which are the coil type, the angle between the PCB coil and the magnetic flux, the resistance of the integrating resistor, and the length of the measuring cable, on the output signal of the current sensor is studied.
The pentatricopeptide repeat protein Rmd9 recognizes the dodecameric element in the 3′-UTRs of yeast mitochondrial mRNAs
Authors: Hillen Hauke S. , Markov Dmitriy A. , Wojtas Ireneusz D. , Hofmann Katharina B. , Lidschreiber Michael , Cowan Andrew T. , Jones Julia L. , Temiakov Dmitry , Cramer Patrick , Anikin Michael 
Published: April 2021

Significance
Respiration-coupled ATP production yields most of the energy in eukaryotic cells and relies on the function of mitochondrial oxidative phosphorylation complexes. Biogenesis of these complexes involves both nuclear and mitochondrial genes, which must be coordinately expressed to assure stoichiometric assembly. The regulatory system that provides this coordination remains poorly understood. Here, we report on the function of a nuclear gene product Rmd9, a feasible component of such a system from yeast. We show that Rmd9 is the factor that binds to the dodecamer element present in mitochondrial mRNAs and provide structural insights into Rmd9-RNA interaction. Combined with previous genetic analyses, our results indicate that Rmd9 controls yeast mitochondrial gene expression by stabilizing and enabling the processing of the mitochondrial mRNAs.
Primate phageomes are structured by superhost phylogeny and environment
Authors: Gogarten Jan F. , Rühlemann Malte , Archie Elizabeth , Tung Jenny , Akoua-Koffi Chantal , Bang Corinna , Deschner Tobias , Muyembe-Tamfun Jean-Jacques , Robbins Martha M. , Schubert Grit , Surbeck Martin , Wittig Roman M. , Zuberbühler Klaus , Baines John F. , Franke Andre , Leendertz Fabian H. , Calvignac-Spencer Sébastien 
Published: April 2021

Significance
Mammals harbor diverse communities of gut microbes. The assembly and evolution of the bacterial components of these communities are influenced by host evolutionary histories and social behavior. Little is known about the ecological and evolutionary origins of the phages infecting these bacteria. We explore drivers of phage community assembly and phage lineage evolution in primates. Many phages codiverged with their superhosts. Furthermore, neighboring social groups harbor compositionally and evolutionary distinct phageomes, structured by superhost social behavior. Captive primate phageome composition is intermediate to humans and their wild primate counterparts, with phage phylogenies revealing replacement of wild-associated phages by human-associated lineages. This plasticity makes the long-term associations of phages with their superhosts observed across ecosystems and continents all the more striking.
Alternative splicing redefines landscape of commonly mutated genes in acute myeloid leukemia
Authors: Rivera Osvaldo D. , Mallory Michael J. , Quesnel-Vallières Mathieu , Chatrikhi Rakesh , Schultz David C. , Carroll Martin , Barash Yoseph , Cherry Sara , Lynch Kristen W. 
Published: April 2021

Significance
Acute myeloid leukemia (AML) is an aggressive hematologic cancer in which malignant myeloid precursor cells impair hematopoiesis and induce bone marrow failure. Most genes associated with AML are mutated in less than 10% of patients, suggesting that alternative mechanisms of gene disruption contribute to this disease. Here, we demonstrate that alternative splicing provides an additional layer of gene dysregulation and mechanism by which gene function is disrupted in AML. These results demonstrate that classical mutation analysis underestimates the burden of functional gene disruption in AML and highlight the importance of assessing the contribution of alternative splicing to gene dysregulation in human disease.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Authors: Rives Alexander , Meier Joshua , Sercu Tom , Goyal Siddharth , Lin Zeming , Liu Jason , Guo Demi , Ott Myle , Zitnick C. Lawrence , Ma Jerry , Fergus Rob 
Published: April 2021

Significance
Learning biological properties from sequence data is a logical step toward generative and predictive artificial intelligence for biology. Here, we propose scaling a deep contextual language model with unsupervised learning to sequences spanning evolutionary diversity. We find that without prior knowledge, information emerges in the learned representations on fundamental properties of proteins such as secondary structure, contacts, and biological activity. We show the learned representations are useful across benchmarks for remote homology detection, prediction of secondary structure, long-range residue–residue contacts, and mutational effect. Unsupervised representation learning enables state-of-the-art supervised prediction of mutational effect and secondary structure and improves state-of-the-art features for long-range contact prediction.