bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cyclic Naphthalene Diimide with a Ferrocene Moiety as a Redox‐Active Tetraplex‐DNA Ligand
Authors: Kaneyoshi Shuma , Zou Tingting , Ozaki Shunsuke , Takeuchi Ryusuke , Udou Ayano , Nakahara Takumi , Fujimoto Kazuhisa , Fujii Satoshi , Sato Shinobu , Takenaka Shigeori 
Published: November 2019

AbstractCyclic naphthalene diimides (cNDIs), with a ferrocene moiety (cFNDs) and different linker lengths between the ferrocene and cNDI moieties, were designed and synthesized as redox‐active, tetraplex‐DNA ligands. Intramolecular stacking was observed between ferrocene and the NDI planes, which could affect the binding properties for G‐quadruplexes. Interestingly, the circular dichroism spectrum of one of these compounds clearly shows new Cotton effects around 320–380 and 240 nm, which can be considered a direct evidence of intramolecular stacking of ferrocene and the NDI. Regarding recognition of hybrid G‐quadruplexes, the less rigid structures (longer linkers) show higher binding affinity (106 m−1 order of magnitude). All new compounds show higher selectivity for G4 during electrochemical detection than noncyclic FND derivatives, which further identifies the redox‐active potentiality of the cFNDs. Two of the three compounds tested even show preferential inhibition of cell growth in cancer cells over normal cells in a low concentration range, highlighting the potential for bioapplications of these cFNDs.
Co(II)‐salen catalyzed stereoselective cyclopropanation of fluorinated styrenes
Authors: Tai Serene , Maskrey Taber S. , Nyalapatla Prasanth R. , Wipf Peter 
Published: November 2019

AbstractThree cis‐selective Co(II)‐salen complexes have been developed for the asymmetric cyclopropanation of para‐fluorinated styrenes with ethyl diazoacetate. Increasing the steric reach of the C2‐symmetric ligand side chains improved the enantiomeric ratio of the reaction from 28:1 to 66:1. The methodology was exemplified by the gram‐scale synthesis of a lead compound for the treatment of castration‐resistant prostate cancer (CRPC), as well as a structurally related analog.
An improved intelligent early warning method based on MWSPCA and its application in complex chemical processes
Authors: Geng Zhiqiang , Chen Ning , Han Yongming , Ma Bo 
Published: November 2019

AbstractWith the development of industrial automation, the requirement of abnormal early warning in the industrial production process is getting higher and higher. Facing complex chemical processes, traditional fault detection and abnormal early warning methods have low detection efficiency and poor real‐time performance. Therefore, this paper analyzes and studies fault detection and abnormal early warning methods, and puts forward an improved intelligent early warning method based on the moving window sparse principal component analysis (MWSPCA) suitable for complex chemical processes. The sparse principal component analysis algorithm is used to establish the initial early warning model, and then the moving window is used to update the early warning model, which makes the early warning model more suitable for the characteristics of time‐varying data. Furthermore, the proposed method reduces the false alarm rate and missed alarm rate of the early warning, and improves the real‐time performance of the early warning model. Finally, the feasibility and the validity of the proposed method are verified by the TE process and the oil drilling process. The experiment results show that the proposed method can reduce the risk of complex chemical processes.
Remaining useful life prediction of lithium‐ion battery based on an improved particle filter algorithm
Authors: Xie Guo , Peng Xi , Li Xin , Hei Xinhong , Hu Shaolin 
Published: November 2019

AbstractBecause lithium‐ion batteries are the main power source of industrial electronic equipment, their degradation process modelling and remaining useful life (RUL) prediction problems have attracted wide attention. The particle filter (PF) method has been successfully applied to suppress the model uncertainty and predict the RUL of the lithium‐ion battery. In order to further enhance the stability of the PF method and realize a more satisfactory prediction result, a RUL prediction method based on the hybrid algorithm, which combines the PF algorithm and extended unbiased finite impulse response (EFIR) filter, is proposed. Firstly, the state space model of capacity degradation for the lithium‐ion battery is established, and the model parameters are estimated by the extended Kalman filter (EKF) algorithm. Secondly, a preliminary battery capacity is predicted by using a regularized particle filter. The preliminary predictions with large deviations are diagnosed and repaired by combining the EFIR filter and diagnostic strategy. Finally, the optimized RUL prediction results of the lithium‐ion battery are extrapolated based on the failure threshold. The experiment results demonstrate that the proposed method has good stability and accuracy in predicting the RUL of a lithium‐ion battery.
Isoxazolone Reactivity Explained by Computed Electronic Structure Analysis
Authors: Bella Giovanni , Santoro Antonio , Cordaro Massimiliano , Nicolò Francesco , Bruno Giuseppe 
Published: November 2019

Summary of main observation and conclusionIsoxazol(2H)‐5‐one was chosen as a model molecule to study the structural features (α, β angles and carbonyl bond length) regarding lactone moiety in the isoxazolone species by computational calculation. DFT method with B3LYP 6‐311++G(2df,2p) basis set was used to carry out the optimization on a series (A and B families) of isoxazolones suitably substituted on double bond. The same computational method assisted by default solvent model was performed to evaluate the energies related to the tautomeric equilibrium of isoxazol(2H)‐5‐one and to the keto‐enol process (IRC). NPA (Natural Population Analysis) allowed us to understand how electron density is distributed on the heterocycle ring, highlighting reactive sites in chemical reactions and the possible interactions with biological targets.
Clinical utility of hereditary cancer panel testing: Impact of PALB2, ATM, CHEK2, NBN, BRIP1, RAD51C, and RAD51D results on patient management and adherence to provider recommendations
Authors: Vysotskaia Valentina , Kaseniit K. Eerik , Bucheit Leslie , Ready Kaylene , Price Kristin , Johansen Taber Katherine 
Published: November 2019

BackgroundAlthough management guidelines exist for several genes associated with a 2‐fold to 5‐fold increase in the relative risk for certain cancers, the value of testing for them remains controversial.MethodsDe‐identified personal and family history data for 654 individuals with pathogenic variants (PVs) in PALB2, ATM, CHEK2, NBN, BRIP1, RAD51C, and/or RAD51D were analyzed for pretest and post‐test candidacy for guideline‐recommended management of cancer risk. These individuals were invited to complete a survey about provider recommendations and their adherence.ResultsTwenty‐four percent of CHEK2, ATM, PALB2, or NBN PV carriers were appropriate for consideration of annual breast magnetic resonance imaging screening before genetic testing, with the remaining 76% appropriate only after testing. No BRIP1, RAD51C, or RAD51D PV carriers were appropriate for consideration of risk‐reducing salpingo‐oophorectomy before genetic testing; 100% were appropriate only after testing. Seventeen percent of CHEK2 PV carriers were appropriate for earlier and more frequent colonoscopy before genetic testing, with the remaining 83% appropriate only after testing. Provider recommendations for annual breast magnetic resonance imaging, consideration of risk‐reducing salpingo‐oophorectomy, and earlier and more frequent colonoscopy were reported by 42%, 26%, and 66% of breast, ovarian, and colorectal cancer risk PV carriers, respectively, before genetic testing, versus 82%, 79%, and 81%, respectively, after testing. Nearly all respondents had planned or undertaken provider‐recommended management.ConclusionsTesting for PALB2, ATM, CHEK2, NBN, BRIP1, RAD51C, and RAD51D changed management for those carrying PVs. Provider recommendations were aligned with guidelines, and patients adhered to recommendations, both of which are critical for reducing both long‐term cancer morbidity and mortality.
Multi‐view ensemble learning with empirical kernel for heart failure mortality prediction
Authors: Wang Zhe , Chen Lilong , Zhang Jing , Yin Yichao , Li Dongdong 
Published: November 2019

AbstractHeart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's needs, which has a very serious impact on human health. In recent years, the prevalence of HF has remained high. This paper proposes a multi‐view ensemble learning algorithm based on empirical kernel mapping called MVE‐EK, which predicts the mortality of patient through hospital records. Multi‐view ensemble learning can take advantage of the consistency and complementarity of different views. The MVE‐EK first divides the patient's features into multiple views and then divides the samples of each view to multiple subsets through under sampling, which can reduce the imbalance rate of the original dataset and obtain some relatively balanced subsets. Each subset is mapped into kernel space by empirical kernel mapping, which can map samples from linearly inseparable spaces to linearly separable spaces. Finally, the multi‐view ensemble learning is performed by the designed loss of acquaintance between views. The effectiveness of the algorithm is verified on the three datasets of HF patient in the real world. The performance of the algorithm is better than other comparison algorithms. The datasets are collected from Shanghai Shuguang Hospital and involve 10 203 hospitalization records for 4682 HF patients between March 2009 and April 2016. The prediction information provided by the algorithm can assist the clinician in providing a more personalized treatment plan for patients with HF.
Computational simulations of the helical buckling behavior of blood vessels
Authors: Sharzehee Mohammadali , Fatemifar Fatemeh , Han Hai‐Chao 
Published: November 2019

AbstractTortuous vessels are often observed in vivo and could hinder or even disrupt blood flow to distal organs. Besides genetic and biological factors, the in vivo mechanical loading seems to play a role in the formation of tortuous vessels, but the mechanism for formation of helical vessel shape remains unclear. Accordingly, the aim of this study was to investigate the biomechanical loads that trigger the occurrence of helical buckling in blood vessels using finite element analysis. Porcine carotid arteries were modeled as thick‐walled cylindrical tubes using generalized Fung and Holzapfel‐Gasser‐Ogden constitutive models. Physiological loadings, including axial tension, lumen pressure, and axial torque, were applied. Simulations of various geometric dimensions, different constitutive models and at various levels of axial stretch ratios, lumen pressures, and twist angles were performed to identify the mechanical factors that determine the helical stability. Our results demonstrated that axial torsion can cause wringing (twist buckling) that leads to kinking or helical coiling and even looping and winding. The specific buckling patterns depend on the combination of lumen pressure, axial torque, axial tension, and the dimensions of the vessels. This study elucidates the mechanism of how blood vessels buckle under various mechanical loads and how complex mechanical loads yield helical buckling.
Quantization noise upconversion effects in mixer‐first direct delta‐sigma receivers
Authors: Ul Haq Faizan , Englund Mikko , Östman Kim B. , Stadius Kari , Kosunen Marko , Koli Kimmo , Ryynänen Jussi 
Published: November 2019

SummaryIn this paper, we investigate the application of the direct ΔΣ receiver (DDSR) concept in a mixer‐first architecture. Specifically, we analyze the degrading effects of quantization noise (Qn) upconversion on DDSR sensitivity, which is a major concern in mixer‐first DDSR architecture. We demonstrate that with the chosen approach, the mixer‐first architecture is suitable for the DDSR despite the potential challenges arising from Qn upconversion. A systematic modeling and understanding of Qn upconversion effects is presented, which lead to simple design guidelines. The results demonstrate that a first‐order low‐pass Qn filtering is sufficient in most cases for mixer‐first DDSR implementations. Based on analytical results, we design a transistor‐level mixer‐first DDSR by merging the functionality of N‐path capacitors both as channel select and Qn filters. Simulations performed in a 28‐nm complementary metal‐oxideŰsemiconductor (CMOS) process show a mere 1.5‐dB degradation from maximum signal‐to‐noise and distortion ratio (SNDR) for the worst‐case scenarios arising from Qn upconversion effects, validating the chosen approach.
Secrecy sum rate optimization in MISO nonorthogonal multiple access systems with orthogonal space‐time block coding
Authors: Yan Jianfei , Li Quanzhong , Zhang Qi , Yang Liang , Qin Jiayin 
Published: November 2019

SummaryIn this paper, we investigate the secrecy sum rate optimization problem for a multiple‐input single‐output (MISO) nonorthogonal multiple access (NOMA) system with orthogonal space‐time block codes (OSTBC). This system consists of a transmitter, two users, and a potential eavesdropper. The transmitter sends information by orthogonal space‐time block codes. The transmitter's precoder and the power allocation scheme are designed to maximize achievable secrecy sum rate subject to the power constraint at the transmitter and the minimum transmission rate requirement of the weak user. We consider two cases of the eavesdropper's channel condition to obtain positive secrecy sum rate. The first case is the eavesdropper's equivalent channel is the weakest, and the other is the eavesdropper's equivalent channel between the strong user and weak user. For the former case, we employ the constrained concave convex procedure (CCCP)‐based iterative algorithm with one‐dimensional search. While for the latter, we adopt the method of alternating optimization (AO) between precoder and power allocation. We solve a semidefinite programming to optimize the precoder and drive a closed‐form expression of power allocation. The simulation results obtained by our method demonstrate the superiority of our proposed scheme.
The risk of higenamine adverse analytical findings following oral administration of plumula nelumbinis capsules
Authors: Yan Kuan , Wang Xinzhai , Wang Zhanliang , Wang Yunfei , Luan Zhaoqian , Gao Xi , Wang Ruiqi 
Published: November 2019

AbstractHigenamine is a β2‐agonist that has been included in the Prohibited List of the World Anti‐Doping Agency (WADA) since 2017. Meanwhile, it exists in plumula nelumbinis, a part of the lotus seed, and is commonly used as an ingredient in cuisines, herbal medicines, and nutritional supplements in China and other countries in East Asia. Therefore, an evaluation of the risk of an adverse analytical finding (AAF) of higenamine caused by plumula nelumbinis products is necessary in doping control. In this study, 14 volunteers took plumula nelumbinis capsules orally (0.34 g/caplet, 6 caplets/day, 7 days), and another 11 volunteers ingested higenamine tablets (three 5 mg tablets/day for 7 days). Urine samples were collected over a period of 14 days. All urine samples were subjected to quantitative dilute‐and‐shoot analysis using liquid chromatography–tandem mass spectrometry (LC–MS/MS). The analytical results showed that urinary higenamine concentrations exceeded the WADA reporting limit (10 ng/mL) during the drug period in most sample groups. The maximum higenamine concentration observed in the plumula nelumbinis capsule group was 500 ng/mL. Based on confidence interval theory, appropriate data were used to establish mathematical models. The models reflected that the higenamine concentration in urine can exceed the WADA reporting limit with a high probability after taking plumula nelumbinis capsules. In conclusion, oral administration of plumula nelumbinis capsules showed a high risk of an AAF due to higenamine.
Combining environmental DNA and species distribution modeling to evaluate reintroduction success of a freshwater fish
Authors: Riaz Maria , Kuemmerlen Mathias , Wittwer Claudia , Cocchiararo Berardino , Khaliq Imran , Pfenninger Markus , Nowak Carsten 
Published: November 2019

AbstractActive species reintroduction is an important conservation tool when aiming for the restoration of biological communities and ecosystems. The effective monitoring of reintroduction success is a crucial factor in this process. Here, we used a combination of environmental DNA (eDNA) techniques and species distribution models (SDMs) to evaluate the success of recent reintroductions of the freshwater fish Alburnoides bipunctatus in central Germany. We built SDMs without and with eDNA presence data to locate further suitable reintroduction sites and potentially overlooked populations of the species. We successfully detected eDNA of A. bipunctatus at all reintroduction sites, as well as several adjacent sites mostly in downstream direction, which supports the success of reintroduction efforts. eDNA‐based species detection considerably improved SDMs for A. bipunctatus, which allowed to identify species presence in previously unknown localities. Our results confirm the usefulness of eDNA techniques as standard tool to monitor reintroduced fish populations. We propose that combining eDNA with SDMs is a highly effective approach for long‐term monitoring of reintroduction success in aquatic species.
Analysis of bicarbonate, phosphate and other anions in saliva by capillary electrophoresis with capacitively coupled contactless conductivity detection in diagnostics of gastroesophageal reflux disease
Authors: Dosedělová Věra , Ďurč Pavol , Dolina Jiří , Konečný Štefan , Foret František , Kubáň Petr 
Published: November 2019

AbstractBicarbonate and phosphate constitute major salivary buffering components, and their importance consists in the neutralization of acidic gastric contents during reflux episodes. In this work, capillary electrophoresis with capacitively coupled contactless conductivity detector was applied for the analysis of bicarbonate, phosphate, and another inorganic (chloride, nitrite, nitrate, sulfate, thiocyanate) and organic anions (acetate, butyrate) to evaluate their levels in saliva. The background electrolytes of different composition and pH between 6.02–9.41 were assessed for the bicarbonate and phosphate determination by comparison of the real analyses of a model solution with the simulation by PeakMaster software. The optimized background electrolyte was composed of 10 mM 2‐(N‐morpholino)ethanesulfonic acid, 20 mM arginine, and 30 µM cetyltrimethylammonium bromide, pH 8.95. Using this BGE, the anion levels were compared in saliva from 20 patients suffering from gastroesophageal reflux disease (GERD) and saliva from 12 healthy subjects. Bicarbonate levels were significantly elevated in saliva from GERD patients suggesting the possible applicability of bicarbonate as a biomarker in non‐invasive diagnostics of GERD by CE‐C4D.
Nonlinear reaction–diffusion process models improve inference for population dynamics
Authors: Lu Xinyi , Williams Perry J. , Hooten Mevin B. , Powell James A. , Womble Jamie N. , Bower Michael R. 
Published: November 2019

AbstractPartial differential equations (PDEs) are a useful tool for modeling spatiotemporal dynamics of ecological processes. However, as an ecological process evolves, we need statistical models that can adapt to changing dynamics as new data are collected. We developed a model that combines an ecological diffusion equation and logistic growth to characterize colonization processes of a population that establishes long‐term equilibrium over a heterogeneous environment. We also developed a homogenization strategy to statistically upscale the PDE for faster computation and adopted a hierarchical framework to accommodate multiple data sources collected at different spatial scales. We highlighted the advantages of using a logistic reaction component instead of a Malthusian component when population growth demonstrates asymptotic behavior. As a case study, we demonstrated that our model improves spatiotemporal abundance forecasts of sea otters in Glacier Bay, Alaska. Furthermore, we predicted spatially varying local equilibrium abundances as a result of environmentally driven diffusion and density‐regulated growth. Integrating equilibrium abundances over the study area in our application enabled us to infer the overall carrying capacity of sea otters in Glacier Bay, Alaska.
Can inpainting improve digital terrain analysis? Comparing techniques for void filling, surface reconstruction and geomorphometric analyses
Authors: Crema Stefano , Llena Manel , Calsamiglia Aleix , Estrany Joan , Marchi Lorenzo , Vericat Damià , Cavalli Marco 
Published: November 2019

AbstractThe investigation of form and processes in geomorphology and ecology is highly dependent on topographic data: a reliable digital terrain representation is in fact a key issue across environmental and earth sciences. In many cases, the processing of high‐resolution topographic data (e.g., light detection and ranging (LiDAR), structure from motion) has to face issues such as void filling, vegetation/feature removal and interpolation accuracy that are usually related to (i) intrinsic limitations of the adopted technology, (ii) local conditions affecting the survey or (iii) specific design scenario. In this paper, we develop a methodology to test the accuracy of an image inpainting algorithm to fill data voids in complex mountain areas. The devised experiment exploits the availability of a high‐resolution, LiDAR‐derived digital terrain model and the inpainting approach accuracy is checked against some widely used interpolation techniques (natural neighbor, spline, inverse distance weighting, kriging). In order to better mimic the actual surface texture, a methodology to introduce local topographic variability to the interpolated surface is also presented. The results show a better performance of the inpainting algorithm especially in the case of complex and rugged topography. Two examples showing an effective usage and accuracy of the proposed technique are reported, highlighting the drawbacks that a poor surface representation can introduce. The whole procedure is made freely available within a Matlab® script with the addition of sample files. ©2019 John Wiley & Sons, Ltd. ©2020 John Wiley & Sons, Ltd.
Modeling and Forecasting of Energy Demands for Household Applications
Authors: Salam Md. Abdus , Yazdani Md. Gholam , Wen Fushuan , Rahman Quazi Mehbubar , Malik Owais Ahmed , Hasan Syeed 
Published: November 2019

AbstractEnergy use is on the rise due to an increase in the number of households and general consumptions. It is important to estimate and forecast the number of houses and the resultant energy consumptions to address the effective and efficient use of energy in future planning. In this paper, the number of houses in Brunei Darussalam is estimated by using Spline interpolation and forecasted by using two methods, namely an autoregressive integrated moving average (ARIMA) model and nonlinear autoregressive (NAR) neural network. The NAR model is more accurate in forecasting the number of houses as compared to the ARIMA model. The energy required for water heating and other appliances is investigated and are found to be 21.74% and 78.26% of the total energy used, respectively. Through analysis, it is demonstrated that 9 m2 solar heater and 90 m2 of solar panel can meet these energy requirements.