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1. Development of a Nomogram for Predicting Mortality Risk in Sepsis Patients During Hospitalization: A Retrospective Study

Author

Lu,Bin, Pan,Xinling, Wang,Bin, Jin,Chenyuan, Liu,Chenxin, Wang,Mengqi, and Shi,Yunzhen

Subjects
Pharmacology, Infectious Diseases, Infection and Drug Resistance, Pharmacology (medical)
Abstract

Bin Lu,1 Xinling Pan,2 Bin Wang,3 Chenyuan Jin,1 Chenxin Liu,1 Mengqi Wang,4 Yunzhen Shi1 1Department of Infectious Diseases, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of China; 2Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, People’s Republic of China; 3Department of Emergency, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of China; 4Department of Neurology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of ChinaCorrespondence: Yunzhen Shi, Department of Infectious Diseases, Affiliated Dongyang Hospital of Wenzhou Medical University, No. 60 Wuningxi Road, Dongyang, People’s Republic of China, Email syzwylwmc@163.comPurpose: We attempted to establish a model for predicting the mortality risk of sepsis patients during hospitalization.Patients and Methods: Data on patients with sepsis were collected from a clinical record mining database, who were hospitalized at the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2013 and August 2022. These included patients were divided into modeling and validation groups. In the modeling group, the independent risk factors of death during hospitalization were determined using univariate and multi-variate regression analyses. After stepwise regression analysis (both directions), a nomogram was drawn. The discrimination ability of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the GiViTI calibration chart assessed the model calibration. The Decline Curve Analysis (DCA) was performed to evaluate the clinical effectiveness of the prediction model. Among the validation group, the logistic regression model was compared to the models established by the SOFA scoring system, random forest method, and stacking method.Results: A total of 1740 subjects were included in this study, 1218 in the modeling population and 522 in the validation population. The results revealed that serum cholinesterase, total bilirubin, respiratory failure, lactic acid, creatinine, and pro-brain natriuretic peptide were the independent risk factors of death. The AUC values in the modeling group and validation group were 0.847 and 0.826. The P values of calibration charts in the two population sets were 0.838 and 0.771. The DCA curves were above the two extreme curves. Moreover, the AUC values of the models established by the SOFA scoring system, random forest method, and stacking method in the validation group were 0.777, 0.827, and 0.832, respectively.Conclusion: The nomogram model established by combining multiple risk factors could effectively predict the mortality risk of sepsis patients during hospitalization.Keywords: severe sepsis, mortality risk prediction, nomogram, SOFA, random forest, stacking

Published
2023

8. Aortic Involvement in Disseminated Tuberculosis – Challenges Beyond the Diagnosis

Author

Shi,Yunzhen, Guo,Wanru, Hu,Wenjuan, Li,Xiaomeng, Shangguan,Yanwan, Feng,Xuewen, Hu,Ming, Xia,Jiafeng, Wang,Shuting, Li,Jingnan, Hong,Li, Lu,Bin, and Xu,Kaijin

Subjects
Pharmacology, Infectious Diseases, Infection and Drug Resistance, cardiovascular system, Pharmacology (medical)
Abstract

Yunzhen Shi,1,* Wanru Guo,2,* Wenjuan Hu,2 Xiaomeng Li,2 Yanwan Shangguan,2 Xuewen Feng,2 Ming Hu,2 Jiafeng Xia,2 Shuting Wang,2 Jingnan Li,1 Li Hong,1 Bin Lu,1 Kaijin Xu2 1Department of Infectious Diseases, Dongyang People’s Hospital, Dongyang, People’s Republic of China; 2State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Kaijin Xu, Email zdyxyxkj@zju.edu.cnAbstract: Tuberculous aortitis (TA) is a rare disease with a high mortality rate. Aortic pseudoaneurysm is the most common vascular pattern of TA, and isolated arterial wall thickening and arterial stenosis can also be seen in TA. We report two cases of disseminated tuberculosis involving the aorta with clinical improvement after treatment. One patient who had an aortic ulcer and intermural hematoma received anti-tuberculosis along with steroids therapy. The other patient, who developed a tubercular abdominal aortic pseudoaneurysm during anti-tuberculosis therapy, successfully received endovascular stent implantation. Clinicians should be aware that TA should be considered in patients with aortitis and active tuberculosis.Keywords: tuberculosis aortitis, aortic pseudoaneurysm, anti-tuberculosis therapy, steroids, endovascular stent implantation

Published
2022

11. The invasion law of drilling fluid along bedding fractures of shale

Author

Hong Li, Qing Wang, Kun Zhang, Qing Hui Zhang, Tao Song, Chuang Zhang, Lu Bin Zhuo, Chen Hao, Fu Ping Feng, He Yuan Wang, and Yin Quan Zhang

Subjects
General Earth and Planetary Sciences
Abstract

In the process of drilling, the drilling fluid will invade into the bedding plane of shale under the action of pressure difference that will cause hydration collapse and wellbore instability. In order to ensure the wellbore stability during shale oil and gas drilling, it is necessary to clarify the invasion law of drilling fluid along bedding fractures during the drilling process. The immersion experiment method is often used to study the invasion law of drilling fluid, which is quite different from the actual invasion process of drilling fluid underground. In this paper, the depth of drilling fluid invasion into shale under different confining pressures and displacement times is intuitively and accurately determined by the displacement experiment and NMR scanning first. Also, then the mathematical relationships between drilling fluid invasion depth and invasion time, invasion pressure difference, confining pressure, bedding angle, and drilling fluid viscosity were established. The errors between the calculated values of the drilling fluid invasion depth and the experimental values were less than 15%, and the calculation accuracy was high. In addition to the influence of invasion time, formation pressure difference and confining pressure on invasion depth were researched through the method of numerical simulation. The results showed that the liquid invasion depth increased logarithmically with the increase of invasion time and formation pressure difference, but it grew slowly in the later period and tended to be stable; the invasion depth decreased exponentially with the increase of confining pressure, bedding plane angle, and drilling fluid viscosity. The results in the paper provide a basis for the subsequent determination of the collapse pressure and collapse period of bedding shale.

Published
2023

12. Additional file 1 of Anti-inflammatory and antioxidative effects of gallic acid on experimental dry eye: in vitro and in vivo studies

Author

Li, Kexin, Gong, Qianwen, Lu, Bin, Huang, Kaiyan, Tong, Yixuan, Mutsvene, Tinashe Emmanuel, Lin, Meng, Xu, Zhiqiang, Lu, Fan, Li, Xingyi, and Hu, Liang

Abstract

Additional file 1: Figure S1. The effect of pre-treatment with gallic acid (GA) on the viability of human corneal epithelial cells (HCECs) and RAW264.7 cells. The effect of GA on the viability of HCECs (a) and RAW264.7 cells (b). Data are presented as mean ± SD (n = 6). **P < 0.01, *P < 0.05 compared to the control group (0 μM gallic acid). Figure S2. Wound healing results. Control (a) and 100 μM gallic acid (b) on wound closure was visualized at 0 h post scratch of human corneal epithelial cell (HCEC) monolayer; Control (c) and 100 μM gallic acid (d) were 24 h after wound healing. e The percentage reduction of the average wound width at 6 h, 18 h and 24 h post scratch of HCEC monolayer. Data are presented as mean ± SD (n = 6). Figure S3. Gallic acid (GA) has good long-term biocompatibility. a Treatment plan and observation plan; b Ocular surface and conjunctiva images, fluorescein sodium staining of the cornea and hematoxylin and eosin (H&E) staining of the cornea after the eyes were treated with phosphate buffered saline (PBS) solution and gallic acid solution (5 mg/mL) at 10 days post instillation; c Draize Test score for ocular surface irritation over 10 days. d Intraocular pressure changes over 10 days. Data are presented as mean ± SD (n = 3). Figure S4. Representative images of the gate plot of intracellular reactive oxygen species (ROS) production in RAW264.7 (a) and human corneal epithelial cells (HCECs) (b). The production of ROS was measured by flow cytometry using the fluorescent probe 2’,7’-Dichlorodihydrofluorescein diacetate (DCFH-DA). SSC-A, side scatter-area; FITC-A, fluorescein isothiocyanate-area; GA, gallic acid; LPS, lipopolysaccharide. Figure S5. 5 mg/mL and 10 mg/mL gallic acid (GA) can significantly reduce corneal fluorescein sodium spotting. Corneal fluorescein representative figures (a) and staining scores (b) show the staining of the NC, EDE, EDE + 10 mg/mL GA, EDE + 5 mg/mL GA, EDE + 1 mg/mL GA groups on the fifth day after desiccant stress. The data are presented as mean ± SD (n = 3). NC, normal control, not received EDE, not given eye drops; EDE, experimental dry eye, received EDE, not given eye drops; EDE + 10 mg/mL GA, received EDE, given 10 mg/mL GA eye drops; EDE + 5 mg/mL GA, received EDE, given 5 mg/mL GA eye drops; EDE + 1 mg/mL GA, received EDE, given 1 mg/mL GA eye drops. ****P < 0.001 compared to the EDE group. Figure S6. Gallic acid (GA) can prevent corneal fluorescein sodium spot staining and cornea epithelial cell apoptosis. Corneal fluorescein representative figures (a) and staining scores (b) show the staining on the fifth day after desiccant stress. Apoptotic corneal epithelial cell count (c) and representative figures (d) show the apoptosis conditions 5 days after desiccant stress. The data are presented as mean ± SD (n = 6). NC, normal control; EDE, experimental dry eye; PBS, phosphate buffered saline; P, preventive effect of the drug; P-NC, not received EDE, not given eye drops; P-EDE: received EDE, not given eye drops; P-EDE + PBS: received EDE, given PBS eye drops; P-EDE + GA: received EDE, given GA eye drops. ****P < 0.001 compared to the P-EDE group. Figure S7. Gallic acid (GA) protects goblet cells. Goblet cells representative figures (a) and goblet cell count (b). The data are presented as mean ± SD (n = 3). NC, normal control; EDE, experimental dry eye; PBS, phosphate buffered saline; P: preventive effect of the drug; P-NC: not received EDE, not given eye drops; P-EDE: received EDE, not given eye drops; P-EDE + PBS: received EDE, given PBS eye drops; P-EDE + GA: received EDE, given GA eye drops. ***P < 0.005 compared to the P-EDE group. Figure S8. Gallic acid (GA) inhibits the elevation of inflammatory factors in the cornea and conjunctiva. a, b respectively show IL-6 and IL-1β levels per mg of the cornea. c, d respectively show IL-6 and IL-1β levels per mg of the conjunctiva. The data are presented as mean ± SD (n = 3). NC, normal control; EDE, experimental dry eye; PBS, phosphate buffered saline; P: preventive effect of the drug; P-NC: not received EDE, not given eye drops; P-EDE: received EDE, not given eye drops; P-EDE + PBS: received EDE, given PBS eye drops; P-EDE + GA: received EDE, given GA eye drops. **P < 0.01, *P < 0.05 compared to the P-EDE group.

Published
2023
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13. Additional file 1 of Neutrophil levels upon admission for the assessment of acute pulmonary embolism with intermediate- and high-risk: an indicator of thrombosis and inflammation

Author

Peng, Rui, Yin, Weihua, Wang, Fang, Cong, Xiangfeng, Lu, Bin, Hua, Lu, and Chen, Xi

Abstract

Additional file1:Table S1. Regression analysis of clinical and hematologic parameters for prediction of intermediate- and high-risk APEa. Table S2. Correlation analysis of D-dimer with PESI score, RV/LV and NT-proBNP in APE patients. Table S3. Receiver operating characteristic (ROC) curve data for admission D-dimer and neutrophils for intermediate- and high-risk APE in the whole APE patient cohort.

Published
2023
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14. Seismic Interferometry by Multi-Dimensional Deconvolution Free from Point-Spread Function

Author

Lu Bin, Wang Ji, Niu Huan, Li Bo, and Ji Guangzhong

Abstract

Seismic interferometry (SI) can be used to reconstruct a pseudo-acquisition from response of a passive source, while the reconstructed data can be used to recover a portion of the model space that is different from that recovered by the inversion of original measurements. The SI by crosscorrelation requiring the seismic wave field to be evenly distributed, which limits the scope of application of this method. SI by multi-dimensional deconvolution (MDD) broken through the limitation that the wave field must be evenly distributed, but there are still some limitations in some special practices. Interferometric point-spread function (PSF), like the correlation function, is a necessary condition of the MDD method, but in some practices it cannot be derived from the field data. A new MDD method is proposed in this paper that free from PSF, and theoretically proved that it is equivalent to usual MDD. We demonstrate the effectiveness of this method with two numerical examples of one-sided illumination, and the source blurring phenomenon in the results of SI by crosscorrelation is effectively eliminated. The first numerical example is far-field one-side illumination, which can also be treated with the usual MDD, and the comparison of the results shows that the two MDD methods are equally effective. The second example is the near-field one-side illumination, and only the MDD method proposed in this paper can be used because the PSF cannot be obtained.

Published
2023

15. Multi-Dimensional Deconvolution for Near-Field Thread Seismic Sources in Tunnels

Author

Lu Bin

Subjects
Geophysics, Environmental Engineering, Geotechnical Engineering and Engineering Geology
Abstract

In underground tunnel constructions, belt conveyors or subway trains can be used as passive sources for time-lapse seismic assessments of surrounding formations. The key technology of passive seismic processes is seismic interferometry (SI), which has the ability to extract the Green's function between any two points in a medium of interest. However, since the process results are typically required to meet certain premise assumptions, the current SI methods cannot be directly used for belt conveyors. In the current study, this type of source is referred to as a near-field thread source and the proposed new SI method is adapted to this type of source. There are mainly two types of SI methods: SI by cross-correlation and SI by multi-dimensional deconvolution. The former requires that the passive source be uniformly distributed in the far field, while the latter relaxes the requirements for uniform distribution yet still requires far field sources. When the first assumptions are violated, the correlation function is proportional to a Green's function with a blurred source. The source blurring is then quantified by what is referred to as an interferometric point-spread function, which can be derived from the observed data. Therefore, using SI by MDD can effectively deblur the source of the Green's function by deconvolving the PSF. In this study, one set of geophones is located in one tunnel as the passive source, and another set is in an adjacent tunnel. However, since the setup almost coincided with the distribution of the passive sources, the spatial (wavenumber) spectrum of the PSF is too broad to be inverted. Therefore, a conventional MDD method is not suitable for this study's experiments. This study found that another PSF could be obtained from the data of the adjacent tunnel. Subsequently, a new MDD formula based on the new PSF is derived and compared with the derivation processes of the conventional MDD formula. Then, the feasibility of the proposed method is verified using numerical simulations.

Published
2021

16. Practical Performance Enhancement of DAS by Using Dense Multichannel Signal Integration

Author

Lu Bin, Ye Lei, Kang Ying, Yifan Liu, Ronghui Qu, Jinfeng Gu, Junqi Yang, Wang Zhaoyong, Qing Ye, and Haiwen Cai

Subjects
Signal-to-noise ratio, Computer science, Electronic engineering, Physics::Physics Education, Spectral density, Fading, Sensitivity (control systems), Intrusion detection system, Acoustic wave, Antenna diversity, Signal, Atomic and Molecular Physics, and Optics
Abstract

Distributed fiber acoustic sensing (DAS) technology can measure acoustic waves along long-distance optical fiber with high spatial resolution. Due to its unique advantages, DAS has been applied in wide fields. However, DAS currently still suffers from limited sensitivity and severe signal fading. Due to the inherent random nature and low signal intensity of Rayleigh backscattering, it is a challenge to essentially eliminate these fundamental limitations. Here a DAS system using its distributed sensing feature is demonstrated with dense multichannel signal integration, complete elimination of signal fading and 100-fold improvement of signal-to-noise ratio (SNR) are experimentally demonstrated. These results provide a simple and effective method to improve DAS performance in practical applications. As far as we know, it is the first time that the distributed sensing characteristic is utilized to tackle fading and sensitivity for DAS, simultaneously. It is believed that this method will promote the process of DAS large-scale applications and help DAS to attract widespread attentions, especially in intrusion detection, acoustic emission flaw detection, near-surface dynamics and characterization, etc.

Published
2021

17. High On-state Current P-type Tunnel Effect Transistor Based on The Doping Modulation

Author

Sun Jiale, Zhang Yuming, Lu Hongliang, Lyu Zhijun, Zhu Yi, Pan Yuche, and Lu Bin

Subjects
General Physics and Astronomy
Abstract

With the goal of solving the problem of the low on-state current in p-TFETs, this paper analyzes the mechanism of adjusting the tunneling current of the TFET device determined by studying the influence of the peak position of ion implantation on the potential of the p-TFET device surface and the width of the tunneling barrier. The doping-regulated silicon-based high on-state p-TFET devices are designed and fabricated, and the test results show that the on-state current of the fabricated devices can be increased by about two orders of magnitude compared with the current of the same structure devices. This method provides a new idea for the realization of high on-state current TFET devices.

Published
2022

20. tension: A Python package for FORCE learning

Author

Lu Bin Liu, Attila Losonczy, and Zhenrui Liao

Subjects
Cellular and Molecular Neuroscience, Computational Theory and Mathematics, Ecology, Modeling and Simulation, Genetics, Molecular Biology, Ecology, Evolution, Behavior and Systematics
Abstract

First-Order, Reduced and Controlled Error (FORCE) learning and its variants are widely used to train chaotic recurrent neural networks (RNNs), and outperform gradient methods on certain tasks. However, there is currently no standard software framework for FORCE learning. We present tension, an object-oriented, open-source Python package that implements a TensorFlow / Keras API for FORCE. We show how rate networks, spiking networks, and networks constrained by biological data can all be trained using a shared, easily extensible high-level API. With the same resources, our implementation outperforms a conventional RNN in loss and published FORCE implementations in runtime. Our work here makes FORCE training chaotic RNNs accessible and simple to iterate, and facilitates modeling of how behaviors of interest emerge from neural dynamics.

Published
2022

21. Association of Serum Pepsinogens With Esophageal Squamous Cell Carcinoma Risk: A Systematic Review and Meta-Analysis

Author

Zhen-Xiao Yang, Lu-Bin Yan, Peng Xie, Peng Hu, Wenjing Zhao, Yi Lu, Xiangbing Xing, and Xudong Liu

Subjects
Cancer Research, Oncology
Abstract

BackgroundSerum pepsinogens are serological biomarkers of gastric atrophy, and the latter is a risk factor for esophageal squamous cell carcinoma (ESCC). However, the association of serum pepsinogens with ESCC risk remains unclear. This systematic review and meta-analysis aimed to assess the relationship between serum pepsinogen I (PGI) and pepsinogen I: pepsinogen II ratio (PGR) and ESCC risk.MethodsPubMed, Embase, and Web of Science were searched for articles on the effect of serum PGI and PGR on ESCC risk, published up to the end of February 2022. Meta-analysis with a random-effect model was used to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs).ResultsFive case–control studies and three prospective studies were included. In comparison with the high categories, the low categories of serum PGI (OR: 1.92, 95% CI: 1.45–2.56) and PGR (OR: 1.70, 95% CI: 1.01–2.85) were associated with an increased risk of ESCC, although a substantial heterogeneity was observed in serum PGR (I2 = 60.2%, P = 0.028) rather than in serum PGI (I2 = 46.4%, P = 0.070). In stratified analysis by study quality, the significant risk effect on ESCC was remained for PGI (OR: 2.05, 95% CI: 1.48–2.84) and PGR (OR: 2.07, 95% CI: 1.17–3.75) when only the studies with high quality were pooled.ConclusionsBased on the available studies, although limited in number, this systematic review along with meta-analysis suggests that low serum PGI and low PGR may be related to an increased risk of ESCC. This present study provides evidence for using serum pepsinogen biomarkers in predicting ESCC. More delicate well-designed cohort studies with high study quality are needed, and dose–response analysis should be performed.

Published
2022

22. A preconditioner based on a splitting-type iteration method for solving complex symmetric indefinite linear systems

Author

Yu-Tao Zheng, Lu-Bin Cui, and Xiao-Qing Zhang

Subjects
Iterative method, Preconditioner, Applied Mathematics, Linear system, General Engineering, 010103 numerical & computational mathematics, Krylov subspace, Computer Science::Numerical Analysis, 01 natural sciences, Generalized minimal residual method, Mathematics::Numerical Analysis, 010101 applied mathematics, Convergence (routing), Computer Science::Mathematical Software, Applied mathematics, 0101 mathematics, Coefficient matrix, Eigenvalues and eigenvectors, Mathematics
Abstract

In this paper, we propose a preconditioned modified positive/negative-stable splitting (PMPNS) iteration method to solve complex symmetric indefinite linear system more efficiently. By analyzing the convergence of the PMPNS iteration method and discussing the spectral properties of the PMPNS iteration method, we construct a new preconditioner to make the eigenvalues of the coefficient matrix more aggregated, which leads to fast convergence of Krylov subspace iteration methods such as GMRES. Numerical example is given to illustrate the efficiency of the PMPNS preconditioner used in GMRES method. In particular, the GMRES method with the PMPNS preconditioner demonstrates meshsize-independent convergence behavior.

Published
2021

23. Impact of COVID-19 on Cardiovascular Testing in the United States Versus the Rest of the World

Author

Hirschfeld, Cole B., Shaw, Leslee J., Williams, Michelle C., Lahey, Ryan, Villines, Todd C., Dorbala, Sharmila, Choi, Andrew D., Shah, Nishant R., Bluemke, David A., Berman, Daniel S., Blankstein, Ron, Ferencik, Maros, Narula, Jagat, Winchester, David, Malkovskiy, Eli, Goebel, Benjamin, Randazzo, Michael J., Lopez-Mattei, Juan, Parwani, Purvi, Vitola, Joao V., Cerci, Rodrigo J., Better, Nathan, Raggi, Paolo, Lu, Bin, Sergienko, Vladimir, Sinitsyn, Valentin, Kudo, Takashi, Nørgaard, Bjarne Linde, Maurovich-Horvat, Pál, Cohen, Yosef A., Pascual, Thomas N. B., Pynda, Yaroslav, Dondi, Maurizio, Paez, Diana, Einstein, Andrew J., Cerci, Rodrigo, Hinterleitner, Gerd, Lu, Yao, Morozova, Olga, Xu, Zhuoran, Cohen, Yosef, Randazzo, Michael, Choi, Andrew, Nasery, Mohammad Nawaz, Goda, Artan, Shirka, Ervina, Benlabgaa, Rabie, Bouyoucef, Salah, Medjahedi, Abdelkader, Nailli, Qais, Agolti, Mariela, Aguero, Roberto Nicolas, Alak, Maria del Carmen, Alberguina, Lucia Graciela, Arroñada, Guillermo, Astesiano, Andrea, Astesiano, Alfredo, Norton, Carolina Bas, Benteo, Pablo, Blanco, Juan, Bonelli, Juan Manuel, Bustos, Jose Javier, Cabrejas, Raul, Cachero, Jorge, Campisi, Roxana, Canderoli, Alejandro, Carames, Silvia, Carrascosa, Patrícia, Castro, Ricardo, Cendoya, Oscar, Cognigni, Luciano Martin, Collaud, Carlos, Cortes, Claudia, Courtis, Javier, Cragnolino, Daniel, Daicz, Mariana, De La Vega, Alejandro, De Maria, Silvia Teresa, Del Riego, Horacio, Dettori, Fernando, Deviggiano, Alejandro, Dragonetti, Laura, Embon, Mario, Enriquez, Ruben Emilio, Ensinas, Jorge, Faccio, Fernando, Facello, Adolfo, Garofalo, Diego, Geronazzo, Ricardo, Gonza, Natalia, Gutierrez, Lucas, Guzzo, Miguel Angel, Hasbani, Victor, Huerin, Melina, Jäger, Victor, Lewkowicz, Julio Manuel, Nieves A López De Munaín, Maria, Lotti, Jose Maria, Marquez, Alejandra, Masoli, Osvaldo, Masoli, Osvaldo Horacio, Mastrovito, Edgardo, Mayoraz, Matias, Melado, Graciela Eva, Mele, Anibal, Merani, Maria Fernanda, Meretta, Alejandro Horacio, Molteni, Susana, Montecinos, Marcos, Noguera, Eduardo, Novoa, Carlos, Sueldo, Claudio Pereyra, Ascani, Sebastian Perez, Pollono, Pablo, Pujol, Maria Paula, Radzinschi, Alejandro, Raimondi, Gustavo, Redruello, Marcela, Rodríguez, Marina, Rodríguez, Matías, Romero, Romina Lorena, Acuña, Arturo Romero, Rovaletti, Federico, San Miguel, Lucas, Solari, Lucrecia, Strada, Bruno, Traverso, Sonia, Traverzo, Sonia Simona, Espeche, Maria del Huerto Velazquez, Weihmuller, Juan Sebastian, Wolcan, Juan, Zeffiro, Susana, Sakanyan, Mari, Beuzeville, Scott, Boktor, Raef, Butler, Patrick, Calcott, Jennifer, Carr, Loretta, Chan, Virgil, Chao, Charles, Chong, Woon, Dobson, Mark, Downie, D'Arne, Dwivedi, Girish, Elison, Barry, Engela, Jean, Francis, Roslyn, Gaikwad, Anand, Basavaraj, Ashok Gangasandra, Goodwin, Bruce, Greenough, Robert, Hamilton-Craig, Christian, Hsieh, Victar, Joshi, Subodh, Lederer, Karin, Lee, Kenneth, Lee, Joseph, Magnussen, John, Mai, Nghi, Mander, Gordon, Murton, Fiona, Nandurkar, Dee, Neill, Johanne, O'Rourke, Edward, O'Sullivan, Patricia, Pandos, George, Pathmaraj, Kunthi, Pitman, Alexander, Poulter, Rohan, Premaratne, Manuja, Prior, David, Ridley, Lloyd, Rutherford, Natalie, Salehi, Hamid, Saunders, Connor, Scarlett, Luke, Seneviratne, Sujith, Shetty, Deepa, Shrestha, Ganesh, Shulman, Jonathan, Solanki, Vijay, Stanton, Tony, Stuart, Murch, Stubbs, Michael, Swainson, Ian, Taubman, Kim, Taylor, Andrew, Thomas, Paul, Unger, Steven, Upton, Anthony, Vamadevan, Shankar, Van Gaal, William, Verjans, Johan, Voutnis, Demetrius, Wayne, Victor, Wilson, Peter, Wong, David, Wong, Kirby, Younger, John, Feuchtner, Gudrun, Mirzaei, Siroos, Weiss, Konrad, Maroz-Vadalazhskaya, Natallia, Gheysens, Olivier, Homans, Filip, Moreno-Reyes, Rodrigo, Pasquet, Agnès, Roelants, Veronique, Van De Heyning, Caroline M., Ríos, Raúl Araujo, Soldat-Stankovic, Valentina, Stankovic, Sinisa, Albernaz Siqueira, Maria Helena, Almeida, Augusto, Alves Togni, Paulo Henrique, Andrade, Jose Henrique, Andrade, Luciana, Anselmi, Carlos, Araújo, Roberta, Azevedo, Guilherme, Bezerra, Sabbrina, Biancardi, Rodrigo, Grossman, Gabriel Blacher, Brandão, Simone, Pianta, Diego Bromfman, Carreira, Lara, Castro, Bruno, Chang, Tien, Cunali, Fernando, Cury, Roberto, Dantas, Roberto, de Amorim Fernandes, Fernando, De Lorenzo, Andrea, De Macedo Filho, Robson, Erthal, Fernanda, Fernandes, Fabio, Fernandes, Juliano, De Souza, Thiago Ferreira, Alves, Wilson Furlan, Ghini, Bruno, Goncalves, Luiz, Gottlieb, Ilan, Hadlich, Marcelo, Kameoka, Vinícius, Lima, Ronaldo, Lima, Adna, Lopes, Rafael Willain, Machado e Silva, Ricardo, Magalhães, Tiago, Silva, Fábio Martins, Mastrocola, Luiz Eduardo, Medeiros, Fábio, Meneghetti, José Claudio, Naue, Vania, Naves, Danilo, Nolasco, Roberto, Nomura, Cesar, Oliveira, Joao Bruno, Paixao, Eduardo, De Carvalho, Filipe Penna, Pinto, Ibraim, Possetti, Priscila, Quinta, Mayra, Nogueira Ramos, Rodrigo Rizzo, Rocha, Ricardo, Rodrigues, Alfredo, Rodrigues, Carlos, Romantini, Leila, Sanches, Adelina, Santana, Sara, Sara da Silva, Leonardo, Schvartzman, Paulo, Matushita, Cristina Sebastião, Senra, Tiago, Shiozaki, Afonso, Menezes de Siqueira, Maria Eduarda, Siqueira, Cristiano, Smanio, Paola, Soares, Carlos Eduardo, Junior, José Soares, Bittencourt, Marcio Sommer, Spiro, Bernardo, Mesquita, Cláudio Tinoco, Torreao, Jorge, Torres, Rafael, Uellendahl, Marly, Monte, Guilherme Urpia, Veríssimo, Otávia, Cabeda, Estevan Vieira, Pedras, Felipe Villela, Waltrick, Roberto, Zapparoli, Marcello, Naseer, Hamid, Garcheva-Tsacheva, Marina, Kostadinova, Irena, Theng, Youdaline, Abikhzer, Gad, Barette, Rene, Chow, Benjamin, Dabreo, Dominique, Friedrich, Matthias, Garg, Ria, Hafez, Mohammed Nassoh, Johnson, Chris, Kiess, Marla, Leipsic, Jonathon, Leung, Eugene, Miller, Robert, Oikonomou, Anastasia, Probst, Stephan, Roifman, Idan, Small, Gary, Tandon, Vikas, Trivedi, Adwait, White, James, Zukotynski, Katherine, Canessa, Jose, Muñoz, Gabriel Castro, Concha, Carmen, Hidalgo, Pablo, Lovera, Cesar, Massardo, Teresa, Vargas, Luis Salazar, Abad, Pedro, Arturo, Harold, Ayala, Sandra, Benitez, Luis, Cadena, Alberto, Caicedo, Carlos, Moncayo, Antonio Calderón, Gomez, Sharon, Gutierrez Villamil, Claudia T., Jaimes, Claudia, Londoño, Juan, Londoño Blair, Juan Luis, Pabon, Luz, Pineda, Mauricio, Rojas, Juan Carlos, Ruiz, Diego, Escobar, Manuel Valencia, Vasquez, Andres, Vergel, Damiana, Zuluaga, Alejandro, Gamboa, Isabel Berrocal, Castro, Gabriel, González, Ulises, Baric, Ana, Batinic, Tonci, Franceschi, Maja, Paar, Maja Hrabak, Jukic, Mladen, Medakovic, Petar, Persic, Viktor, Prpic, Marina, Punda, Ante, Batista, Juan Felipe, Gómez Lauchy, Juan Manuel, Gutierrez, Yamile Marcos, Menéndez, Rayner, Peix, Amalia, Rochela, Luis, Panagidis, Christoforos, Petrou, Ioannis, Engelmann, Vaclav, Kaminek, Milan, Kincl, Vladimír, Lang, Otto, Simanek, Milan, Abdulla, Jawdat, Bøttcher, Morten, Christensen, Mette, Gormsen, Lars Christian, Hasbak, Philip, Hess, Søren, Holdgaard, Paw, Johansen, Allan, Kyhl, Kasper, Norgaard, Bjarne Linde, Øvrehus, Kristian Altern, Rønnow Sand, Niels Peter, Steffensen, Rolf, Thomassen, Anders, Zerahn, Bo, Perez, Alfredo, Escorza Velez, Giovanni Alejandro, Velez, Mayra Sanchez, Abdel Aziz, Islam Shawky, Abougabal, Mahasen, Ahmed, Taghreed, Allam, Adel, Asfour, Ahmed, Hassan, Mona, Hassan, Alia, Ibrahim, Ahmed, Kaffas, Sameh, Kandeel, Ahmed, Ali, Mohamed Mandour, Mansy, Ahmad, Maurice, Hany, Nabil, Sherif, Shaaban, Mahmoud, Flores, Ana Camila, Poksi, Anne, Knuuti, Juhani, Kokkonen, Velipekka, Larikka, Martti, Uusitalo, Valtteri, Bailly, Matthieu, Burg, Samuel, Deux, Jean-François, Habouzit, Vincent, Hyafil, Fabien, Lairez, Olivier, Proffit, Franck, Regaieg, Hamza, Sarda-Mantel, Laure, Tacher, Vania, Schneider, Roman P., Ayetey, Harold, Angelidis, George, Archontaki, Aikaterini, Chatziioannou, Sofia, Datseris, Ioannis, Fragkaki, Christina, Georgoulias, Panagiotis, Koukouraki, Sophia, Koutelou, Maria, Kyrozi, Eleni, Repasos, Evangelos, Stavrou, Petros, Valsamaki, Pipitsa, Gonzalez, Carla, Gutierrez, Goleat, Maldonado, Alejandro, Buga, Klara, Garai, Ildiko, Schmidt, Erzsébet, Szilveszter, Balint, Várady, Edit, Banthia, Nilesh, Bhagat, Jinendra Kumar, Bhargava, Rishi, Bhat, Vivek, Bhatia, Mona, Choudhury, Partha, Chowdekar, Vijay Sai, Irodi, Aparna, Jain, Shashank, Joseph, Elizabeth, Kumar, Sukriti, Girijanandan Mahapatra, Prof Dr, Mitra, Deepanjan, Mittal, Bhagwant Rai, Ozair, Ahmad, Patel, Chetan, Patel, Tapan, Patel, Ravi, Patel, Shivani, Saxena, Sudhir, Sengupta, Shantanu, Singh, Santosh, Singh, Bhanupriya, Sood, Ashwani, Verma, Atul, Affandi, Erwin, Alam Edison, Padma Savenadia, Gunawan, Gani, Hapkido, Habusari, Hidayat, Basuki, Huda, Aulia, Mukti, Anggoro Praja, Prawiro, Djoko, Soeriadi, Erwin Affandi, Syawaluddin, Hilman, Albadr, Amjed, Assadi, Majid, Emami, Farshad, Houshmand, Golnaz, Maleki, Majid, Rostami, Maryam Tajik, Zakavi, Seyed Rasoul, Zaid, Eed Abu, Agranovich, Svetlana, Arnson, Yoav, Bar-Shalom, Rachel, Frenkel, Alex, Knafo, Galit, Lugassi, Rachel, Maor Moalem, Israel Shlomo, Mor, Maya, Muskal, Noam, Ranser, Sara, Shalev, Aryeh, Albano, Domenico, Alongi, Pierpaolo, Arnone, Gaspare, Bagatin, Elisa, Baldari, Sergio, Bauckneht, Matteo, Bertelli, Paolo, Bianco, Francesco, Bonfiglioli, Rachele, Boni, Roberto, Bruno, Andrea, Bruno, Isabella, Busnardo, Elena, Califaretti, Elena, Camoni, Luca, Carnevale, Aldo, Casoni, Roberta, Cavallo, Armando Ugo, Cavenaghi, Giorgio, Chierichetti, Franca, Chiocchi, Marcello, Cittanti, Corrado, Colletta, Mauro, Conti, Umberto, Cossu, Alberto, Cuocolo, Alberto, Cuzzocrea, Marco, De Rimini, Maria Luisa, De Vincentis, Giuseppe, Del Giudice, Eleonora, Del Torto, Alberico, Della Tommasina, Veronica, Durmo, Rexhep, Erba, Paola Anna, Evangelista, Laura, Faletti, Riccardo, Faragasso, Evelina, Farsad, Mohsen, Ferro, Paola, Florimonte, Luigia, Frantellizzi, Viviana, Fringuelli, Fabio Massimo, Gatti, Marco, Gaudiano, Angela, Gimelli, Alessia, Giubbini, Raffaele, Giuffrida, Francesca, Ialuna, Salvatore, Laudicella, Riccardo, Leccisotti, Lucia, Leva, Lucia, Liga, Riccardo, Liguori, Carlo, Longo, Giampiero, Maffione, Margherita, Mancini, Maria Elisabetta, Marcassa, Claudio, Milan, Elisa, Nardi, Barbara, Pacella, Sara, Pepe, Giovanna, Pontone, Gianluca, Pulizzi, Sabina, Quartuccio, Natale, Rampin, Lucia, Ricci, Fabrizio, Rossini, Pierluigi, Rubini, Giuseppe, Russo, Vincenzo, Sacchetti, Gian Mauro, Sambuceti, Gianmario, Scarano, Massimo, Sciagrà, Roberto, Sperandio, Massimiliano, Stefanelli, Antonella, Ventroni, Guido, Zoboli, Stefania, Baugh, Dainia, Chambers, Duane, Madu, Ernest, Nunura, Felix, Asano, Hiroshi, Chimura, Chimura Misato, Fujimoto, Shinichiro, Fujisue, Koichiro, Fukunaga, Tomohisa, Fukushima, Yoshimitsu, Fukuyama, Kae, Hashimoto, Jun, Ichikawa, Yasutaka, Iguchi, Nobuo, Imai, Masamichi, Inaki, Anri, Ishimura, Hayato, Isobe, Satoshi, Kadokami, Toshiaki, Kato, Takao, Kumita, Shinichiro, Maruno, Hirotaka, Mataki, Hiroyuki, Miyagawa, Masao, Morimoto, Ryota, Moroi, Masao, Nagamachi, Shigeki, Nakajima, Kenichi, Nakata, Tomoaki, Nakazato, Ryo, Nanasato, Mamoru, Naya, Masanao, Norikane, Takashi, Ohta, Yasutoshi, Okayama, Satoshi, Okizaki, Atsutaka, Otomi, Yoichi, Otsuka, Hideki, Saito, Masaki, Sakata, Sakata Yasushi, Sarai, Masayoshi, Sato, Daisuke, Shiraishi, Shinya, Suwa, Yoshinobu, Takanami, Kentaro, Takehana, Kazuya, Taki, Junichi, Tamaki, Nagara, Taniguchi, Yasuyo, Teragawa, Hiroki, Tomizawa, Nobuo, Tsujita, Kenichi, Umeji, Kyoko, Wakabayashi, Yasushi, Yamada, Shinichiro, Yamazaki, Shinya, Yoneyama, Tatsuya, Rawashdeh, Mohammad, Batyrkhanov, Daultai, Dautov, Tairkhan, Makhdomi, Khalid, Ombati, Kevin, Alkandari, Faridah, Garashi, Masoud, Coie, Tchoyoson Lim, Rajvong, Sonexay, Kalinin, Artem, Kalnina, Marika, Haidar, Mohamad, Komiagiene, Renata, Kviecinskiene, Giedre, Mataciunas, Mindaugas, Vajauskas, Donatas, Picard, Christian, Karim, Noor Khairiah A., Reichmuth, Luise, Samuel, Anthony, Allarakha, Mohammad Aaftaab, Naojee, Ambedhkar Shantaram, Alexanderson-Rosas, Erick, Barragan, Erika, González-Montecinos, Alejandro Becerril, Cabada, Manuel, Rodriguez, Daniel Calderon, Carvajal-Juarez, Isabel, Cortés, Violeta, Cortés, Filiberto, De La Peña, Erasmo, Gama-Moreno, Manlio, González, Luis, Ramírez, Nelsy Gonzalez, Jiménez-Santos, Moisés, Matos, Luis, Monroy, Edgar, Morelos, Martha, Ornelas, Mario, Ortga Ramirez, Jose Alberto, Preciado-Anaya, Andrés, Preciado-Gutiérrez, Óscar Ulises, Barragan, Adriana Puente, Rosales Uvera, Sandra Graciela, Sandoval, Sigelinda, Tomas, 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Al-Bulushi, Naeema, Jawa, Zabah, Tag, Naima, Afzal, Muhammad Shehzad, Fatima, Shazia, Younis, Muhammad Numair, Riaz, Musab, Saadullah, Mohammad, Herrera, Yariela, Lenturut-Katal, Dora, Vázquez, Manuel Castillo, Ortellado, José, Akhter, Afroza, Cao, Dianbo, Cheung, Stephen, Dai, Xu, Gong, Lianggeng, Han, Dan, Hou, Yang, Li, Caiying, Li, Tao, Li, Dong, Li, Sijin, Liu, Jinkang, Liu, Hui, Ng, Ming Yen, Sun, Kai, Tang, Gongshun, Wang, Jian, Wang, Ximing, Wang, Zhao-Qian, Wang, Yining, Wang, Yifan, Wu, Jiang, Wu, Zhifang, Xia, Liming, Xiao, Jiangxi, Xu, Lei, Yang, Youyou, Yin, Wu, Yu, Jianqun, Yuan, Li, Zhang, Tong, Zhang, Longjiang, Zhang, Yong-Gao, Zhang, Xiaoli, Zhu, Li, Alfaro, Ana, Abrihan, Paz, Barroso, Asela, Cruz, Eric, Gomez, Marie Rhiamar, Magboo, Vincent Peter, Medina, John Michael, Obaldo, Jerry, Pastrana, Davidson, Pawhay, Christian Michael, Quinon, Alvin, Tang, Jeanelle Margareth, Tecson, Bettina, Uson, Kristine Joy, Uy, Mila, Kostkiewicz, Magdalena, Kunikowska, Jolanta, 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Saranovic, Dragana Sobic, Keng, Felix, Jason See, Jia Hao, Sekar, Ramkumar, Yew, Min Sen, Vondrak, Andrej, Bejai, Shereen, Bennie, George, Bester, Ria, Engelbrecht, Gerrit, Evbuomwan, Osayande, Gongxeka, Harlem, Vuuren, Magritha Jv, Kaplan, Mitchell, Khushica, Purbhoo, Lakhi, Hoosen, Louw, Lizette, Malan, Nico, Milos, Katarina, Modiselle, Moshe, More, Stuart, Naidoo, Mathava, Scholtz, Leonie, Vangu, Mboyo, Aguadé-Bruix, Santiago, Blanco, Isabel, Cabrera, Antonio, Camarero, Alicia, Casáns-Tormo, Irene, Cuellar-Calabria, Hug, Flotats, Albert, Fuentes Cañamero, Maria Eugenia, García, María Elia, Jimenez-Heffernan, Amelia, Leta, Rubén, Diaz, Javier Lopez, Lumbreras, Luis, Marquez-Cabeza, Juan Javier, Martin, Francisco, Martinez de Alegria, Anxo, Medina, Francisco, Canal, Maria Pedrera, Peiro, Virginia, Pubul-Nuñez, Virginia, Rayo Madrid, Juan Ignacio, Rey, Cristina Rodríguez, Perez, Ricardo Ruano, Ruiz, Joaquín, Hernández, Gertrudis Sabatel, Sevilla, Ana, Zeidán, Nahla, Nanayakkara, Damayanthi, Udugama, Chandraguptha, Simonsson, Magnus, Alkadhi, Hatem, Buechel, Ronny Ralf, Burger, Peter, Ceriani, Luca, De Boeck, Bart, Gräni, Christoph, Juillet de Saint Lager Lucas, Alix, Kamani, Christel H., Kawel-Boehm, Nadine, Manka, Robert, Prior, John O., Rominger, Axel, Vallée, Jean-Paul, Khiewvan, Benjapa, Premprabha, Teerapon, Thientunyakit, Tanyaluck, Sellem, Ali, Kir, Kemal Metin, Sayman, Haluk, Sebikali, Mugisha Julius, Muyinda, Zerida, Kmetyuk, Yaroslav, Korol, Pavlo, Mykhalchenko, Olena, Pliatsek, Volodymyr, Satyr, Maryna, Albalooshi, Batool, Ahmed Hassan, Mohamed Ismail, Anderson, Jill, Bedi, Punit, Biggans, Thomas, Bularga, Anda, Bull, Russell, Burgul, Rajesh, Carpenter, John-Paul, Coles, Duncan, Cusack, David, Deshpande, Aparna, Dougan, John, Fairbairn, Timothy, Farrugia, Alexia, Gopalan, Deepa, Gummow, Alistair, Ramkumar, Prasad Guntur, Hamilton, Mark, Harbinson, Mark, Hartley, Thomas, Hudson, Benjamin, Joshi, Nikhil, Kay, Michael, Kelion, Andrew, Khokhar, Azhar, Kitt, Jamie, Lee, Ken, Low, Chen, Mak, Sze Mun, Marousa, Ntouskou, Martin, Jon, Mcalindon, Elisa, Menezes, Leon, Morgan-Hughes, Gareth, Moss, Alastair, Murray, Anthony, Nicol, Edward, Patel, Dilip, Peebles, Charles, Pugliese, Francesca, Luis Rodrigues, Jonathan Carl, Rofe, Christopher, Sabharwal, Nikant, Schofield, Rebecca, Semple, Thomas, Sharma, Naveen, Strouhal, Peter, Subedi, Deepak, Topping, William, Tweed, Katharine, Weir-Mccall, Jonathan, Abbara, Suhny, Abbasi, Taimur, Abbott, Brian, Abohashem, Shady, Abramson, Sandra, Al-Abboud, Tarek, Al-Mallah, Mouaz, Almousalli, Omar, Ananthasubramaniam, Karthikeyan, Kumar, Mohan Ashok, Askew, Jeffrey, Attanasio, Lea, Balmer-Swain, Mallory, Bayer, Richard R., Bernheim, Adam, Bhatti, Sabha, Bieging, Erik, Bloom, Stephen, Blue, Sean, Bluemke, David, Borges, Andressa, Branch, Kelley, Bravo, Paco, Brothers, Jessica, Budoff, Matthew, Bullock-Palmer, Renée, Burandt, Angela, Burke, Floyd W., Bush, Kelvin, Candela, Candace, Capasso, Elizabeth, 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George Kinzfogl, Kircher, Jacqueline, Kirkbride, Rachael, Kontos, Michael, Kottam, Anupama, Krepp, Joseph, Layer, Jay, Lee, Steven H., Leppo, Jeffrey, Lesser, John, Leung, Steve, Lewin, Howard, Litmanovich, Diana, Liu, Yiyan, Magurany, Kathleen, Markowitz, Jeremy, Marn, Amanda, Matis, Stephen E., Mckenna, Michael, Mcrae, Tony, Mendoza, Fernando, Merhige, Michael, Min, David, Moffitt, Chanan, Moncher, Karen, Moore, Warren, Morayati, Shamil, Morris, Michael, Mossa-Basha, Mahmud, Mrsic, Zorana, Murthy, Venkatesh, Nagpal, Prashant, Napier, Kyle, Nelson, Katarina, Nijjar, Prabhjot, Osman, Medhat, Passen, Edward, Patel, Amit, Patil, Pravin, Paul, Ryan, Phillips, Lawrence, Polsani, Venkateshwar, Poludasu, Rajaram, Pomerantz, Brian, Porter, Thomas, Prentice, Ryan, Pursnani, Amit, Rabbat, Mark, Ramamurti, Suresh, Rich, Florence, Luna, Hiram Rivera, Robinson, Austin, Robles, Kim, Rodríguez, Cesar, Rorie, Mark, Rumberger, John, Russell, Raymond, Sabra, Philip, Sadler, Diego, Schemmer, Mary, Schoepf, U. Joseph, Shah, Samir, Shah, Nishant, Shanbhag, Sujata, Sharma, Gaurav, Shayani, Steven, Shirani, Jamshid, Shivaram, Pushpa, Sigman, Steven, Simon, Mitch, Slim, Ahmad, Smith, David, Smith, Alexandra, Soman, Prem, Sood, Aditya, Srichai-Parsia, Monvadi Barbara, Streeter, James, T, Albert, Tawakol, Ahmed, Thomas, Dustin, Thompson, Randall, Torbet, Tara, Trinidad, Desiree, Ullery, Shawn, Unzek, Samuel, Uretsky, Seth, Vallurupalli, Srikanth, Verma, Vikas, Waller, Alfonso, Wang, Ellen, Ward, Parker, Weissman, Gaby, Wesbey, George, White, Kelly, Wolinsky, David, Yost, Sandra, Zgaljardic, Michael, Alonso, Omar, Beretta, Mario, Ferrando, Rodolfo, Kapitan, Miguel, Mut, Fernando, Djuraev, Omoa, Rozikhodjaeva, Gulnora, Le Ngoc, Ha, Mai, Son Hong, Nguyen, Xuan Canh, Hirschfeld, Cole B., Shaw, Leslee J., Williams, Michelle C., Lahey, Ryan, Villines, Todd C., Dorbala, Sharmila, Choi, Andrew D., Shah, Nishant R., Bluemke, David A., Berman, Daniel S., Blankstein, Ron, Ferencik, Maro, Narula, Jagat, Winchester, David, Malkovskiy, Eli, Goebel, Benjamin, Randazzo, Michael J., Lopez-Mattei, Juan, Parwani, Purvi, Vitola, Joao V., Cerci, Rodrigo J., Better, Nathan, Raggi, Paolo, Lu, Bin, Sergienko, Vladimir, Sinitsyn, Valentin, Kudo, Takashi, Nørgaard, Bjarne Linde, Maurovich-Horvat, Pál, Cohen, Yosef A., Pascual, Thomas N. 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Butler, Patrick, Calcott, Jennifer, Carr, Loretta, Chan, Virgil, Chao, Charle, Chong, Woon, Dobson, Mark, Downie, D'Arne, Dwivedi, Girish, Elison, Barry, Engela, Jean, Francis, Roslyn, Gaikwad, Anand, Basavaraj, Ashok Gangasandra, Goodwin, Bruce, Greenough, Robert, Hamilton-Craig, Christian, Hsieh, Victar, Joshi, Subodh, Lederer, Karin, Lee, Kenneth, Lee, Joseph, Magnussen, John, Mai, Nghi, Mander, Gordon, Murton, Fiona, Nandurkar, Dee, Neill, Johanne, O'Rourke, Edward, O'Sullivan, Patricia, Pandos, George, Pathmaraj, Kunthi, Pitman, Alexander, Poulter, Rohan, Premaratne, Manuja, Prior, David, Ridley, Lloyd, Rutherford, Natalie, Salehi, Hamid, Saunders, Connor, Scarlett, Luke, Seneviratne, Sujith, Shetty, Deepa, Shrestha, Ganesh, Shulman, Jonathan, Solanki, Vijay, Stanton, Tony, Stuart, Murch, Stubbs, Michael, Swainson, Ian, Taubman, Kim, Taylor, Andrew, Thomas, Paul, Unger, Steven, Upton, Anthony, Vamadevan, Shankar, Van Gaal, William, Verjans, Johan, Voutnis, Demetriu, Wayne, Victor, Wilson, Peter, Wong, David, Wong, Kirby, Younger, John, Feuchtner, Gudrun, Mirzaei, Siroo, Weiss, Konrad, Maroz-Vadalazhskaya, Natallia, Gheysens, Olivier, Homans, Filip, Moreno-Reyes, Rodrigo, Pasquet, Agnè, Roelants, Veronique, Van De Heyning, Caroline M., Ríos, Raúl Araujo, Soldat-Stankovic, Valentina, Stankovic, Sinisa, Albernaz Siqueira, Maria Helena, Almeida, Augusto, Alves Togni, Paulo Henrique, Andrade, Jose Henrique, Andrade, Luciana, Anselmi, Carlo, Araújo, Roberta, Azevedo, Guilherme, Bezerra, Sabbrina, Biancardi, Rodrigo, Grossman, Gabriel Blacher, Brandão, Simone, Pianta, Diego Bromfman, Carreira, Lara, Castro, Bruno, Chang, Tien, Cunali, Fernando, Cury, Roberto, Dantas, Roberto, de Amorim Fernandes, Fernando, De Lorenzo, Andrea, De Macedo Filho, Robson, Erthal, Fernanda, Fernandes, Fabio, Fernandes, Juliano, De Souza, Thiago Ferreira, Alves, Wilson Furlan, Ghini, Bruno, Goncalves, Luiz, Gottlieb, Ilan, Hadlich, Marcelo, Kameoka, Viníciu, Lima, Ronaldo, Lima, Adna, Lopes, Rafael Willain, Machado e Silva, Ricardo, Magalhães, Tiago, Silva, Fábio Martin, Mastrocola, Luiz Eduardo, Medeiros, Fábio, Meneghetti, José Claudio, Naue, Vania, Naves, Danilo, Nolasco, Roberto, Nomura, Cesar, Oliveira, Joao Bruno, Paixao, Eduardo, De Carvalho, Filipe Penna, Pinto, Ibraim, Possetti, Priscila, Quinta, Mayra, Nogueira Ramos, Rodrigo Rizzo, Rocha, Ricardo, Rodrigues, Alfredo, Rodrigues, Carlo, Romantini, Leila, Sanches, Adelina, Santana, Sara, Sara da Silva, Leonardo, Schvartzman, Paulo, Matushita, Cristina Sebastião, Senra, Tiago, Shiozaki, Afonso, Menezes de Siqueira, Maria Eduarda, Siqueira, Cristiano, Smanio, Paola, Soares, Carlos Eduardo, Junior, José Soare, Bittencourt, Marcio Sommer, Spiro, Bernardo, Mesquita, Cláudio Tinoco, Torreao, Jorge, Torres, Rafael, Uellendahl, Marly, Monte, Guilherme Urpia, Veríssimo, Otávia, Cabeda, Estevan Vieira, Pedras, Felipe Villela, Waltrick, Roberto, Zapparoli, Marcello, Naseer, Hamid, Garcheva-Tsacheva, Marina, Kostadinova, Irena, Theng, Youdaline, Abikhzer, Gad, Barette, Rene, Chow, Benjamin, Dabreo, Dominique, Friedrich, Matthia, Garg, Ria, Hafez, Mohammed Nassoh, Johnson, Chri, Kiess, Marla, Leipsic, Jonathon, Leung, Eugene, Miller, Robert, Oikonomou, Anastasia, Probst, Stephan, Roifman, Idan, Small, Gary, Tandon, Vika, Trivedi, Adwait, White, Jame, Zukotynski, Katherine, Canessa, Jose, Muñoz, Gabriel Castro, Concha, Carmen, Hidalgo, Pablo, Lovera, Cesar, Massardo, Teresa, Vargas, Luis Salazar, Abad, Pedro, Arturo, Harold, Ayala, Sandra, Benitez, Lui, Cadena, Alberto, Caicedo, Carlo, Moncayo, Antonio Calderón, Gomez, Sharon, Gutierrez Villamil, Claudia T., Jaimes, Claudia, Londoño, Juan, Londoño Blair, Juan Lui, Pabon, Luz, Pineda, Mauricio, Rojas, Juan Carlo, Ruiz, Diego, Escobar, Manuel Valencia, Vasquez, Andre, Vergel, Damiana, Zuluaga, Alejandro, Gamboa, Isabel Berrocal, Castro, Gabriel, González, Ulise, Baric, Ana, Batinic, Tonci, Franceschi, Maja, Paar, Maja Hrabak, Jukic, Mladen, Medakovic, Petar, Persic, Viktor, Prpic, Marina, Punda, Ante, Batista, Juan Felipe, Gómez Lauchy, Juan Manuel, Gutierrez, Yamile Marco, Menéndez, Rayner, Peix, Amalia, Rochela, Lui, Panagidis, Christoforo, Petrou, Ioanni, Engelmann, Vaclav, Kaminek, Milan, Kincl, Vladimír, Lang, Otto, Simanek, Milan, Abdulla, Jawdat, Bøttcher, Morten, Christensen, Mette, Gormsen, Lars Christian, Hasbak, Philip, Hess, Søren, Holdgaard, Paw, Johansen, Allan, Kyhl, Kasper, Norgaard, Bjarne Linde, Øvrehus, Kristian Altern, Rønnow Sand, Niels Peter, Steffensen, Rolf, Thomassen, Ander, Zerahn, Bo, Perez, Alfredo, Escorza Velez, Giovanni Alejandro, Velez, Mayra Sanchez, Abdel Aziz, Islam Shawky, Abougabal, Mahasen, Ahmed, Taghreed, Allam, Adel, Asfour, Ahmed, Hassan, Mona, Hassan, Alia, Ibrahim, Ahmed, Kaffas, Sameh, Kandeel, Ahmed, Ali, Mohamed Mandour, Mansy, Ahmad, Maurice, Hany, Nabil, Sherif, Shaaban, Mahmoud, Flores, Ana Camila, Poksi, Anne, Knuuti, Juhani, Kokkonen, Velipekka, Larikka, Martti, Uusitalo, Valtteri, Bailly, Matthieu, Burg, Samuel, Deux, Jean-Françoi, Habouzit, Vincent, Hyafil, Fabien, Lairez, Olivier, Proffit, Franck, Regaieg, Hamza, Sarda-Mantel, Laure, Tacher, Vania, Schneider, Roman P., Ayetey, Harold, Angelidis, George, Archontaki, Aikaterini, Chatziioannou, Sofia, Datseris, Ioanni, Fragkaki, Christina, Georgoulias, Panagioti, Koukouraki, Sophia, Koutelou, Maria, Kyrozi, Eleni, Repasos, Evangelo, Stavrou, Petro, Valsamaki, Pipitsa, Gonzalez, Carla, Gutierrez, Goleat, Maldonado, Alejandro, Buga, Klara, Garai, Ildiko, Schmidt, Erzsébet, Szilveszter, Balint, Várady, Edit, Banthia, Nilesh, Bhagat, Jinendra Kumar, Bhargava, Rishi, Bhat, Vivek, Bhatia, Mona, Choudhury, Partha, Chowdekar, Vijay Sai, Irodi, Aparna, Jain, Shashank, Joseph, Elizabeth, Kumar, Sukriti, Girijanandan Mahapatra, Prof Dr, Mitra, Deepanjan, Mittal, Bhagwant Rai, Ozair, Ahmad, Patel, Chetan, Patel, Tapan, Patel, Ravi, Patel, Shivani, Saxena, Sudhir, Sengupta, Shantanu, Singh, Santosh, Singh, Bhanupriya, Sood, Ashwani, Verma, Atul, Affandi, Erwin, Alam, Edison, Padma, Savenadia, Gunawan, Gani, Hapkido, Habusari, Hidayat, Basuki, Huda, Aulia, Mukti, Anggoro Praja, Prawiro, Djoko, Soeriadi, Erwin Affandi, Syawaluddin, Hilman, Albadr, Amjed, Assadi, Majid, Emami, Farshad, Houshmand, Golnaz, Maleki, Majid, Rostami, Maryam Tajik, Zakavi, Seyed Rasoul, Zaid, Eed Abu, Agranovich, Svetlana, Arnson, Yoav, Bar-Shalom, Rachel, Frenkel, Alex, Knafo, Galit, Lugassi, Rachel, Maor Moalem, Israel Shlomo, Mor, Maya, Muskal, Noam, Ranser, Sara, Shalev, Aryeh, Albano, Domenico, Alongi, Pierpaolo, Arnone, Gaspare, Bagatin, Elisa, Baldari, Sergio, Bauckneht, Matteo, Bertelli, Paolo, Bianco, Francesco, Bonfiglioli, Rachele, Boni, Roberto, Bruno, Andrea, Bruno, Isabella, Busnardo, Elena, Califaretti, Elena, Camoni, Luca, Carnevale, Aldo, Casoni, Roberta, Cavallo, Armando Ugo, Cavenaghi, Giorgio, Chierichetti, Franca, Chiocchi, Marcello, Cittanti, Corrado, Colletta, Mauro, Conti, Umberto, Cossu, Alberto, Cuocolo, Alberto, Cuzzocrea, Marco, De Rimini, Maria Luisa, De Vincentis, Giuseppe, Del Giudice, Eleonora, Del Torto, Alberico, Della Tommasina, Veronica, Durmo, Rexhep, Erba, Paola Anna, Evangelista, Laura, Faletti, Riccardo, Faragasso, Evelina, Farsad, Mohsen, Ferro, Paola, Florimonte, Luigia, Frantellizzi, Viviana, Fringuelli, Fabio Massimo, Gatti, Marco, Gaudiano, Angela, Gimelli, Alessia, Giubbini, Raffaele, Giuffrida, Francesca, Ialuna, Salvatore, Laudicella, Riccardo, Leccisotti, Lucia, Leva, Lucia, Liga, Riccardo, Liguori, Carlo, Longo, Giampiero, Maffione, Margherita, Mancini, Maria Elisabetta, Marcassa, Claudio, Milan, Elisa, Nardi, Barbara, Pacella, Sara, Pepe, Giovanna, Pontone, Gianluca, Pulizzi, Sabina, Quartuccio, Natale, Rampin, Lucia, Ricci, Fabrizio, Rossini, Pierluigi, Rubini, Giuseppe, Russo, Vincenzo, Sacchetti, Gian Mauro, Sambuceti, Gianmario, Scarano, Massimo, Sciagrà, Roberto, Sperandio, Massimiliano, Stefanelli, Antonella, Ventroni, Guido, Zoboli, Stefania, Baugh, Dainia, Chambers, Duane, Madu, Ernest, Nunura, Felix, Asano, Hiroshi, Chimura, Chimura Misato, Fujimoto, Shinichiro, Fujisue, Koichiro, Fukunaga, Tomohisa, Fukushima, Yoshimitsu, Fukuyama, Kae, Hashimoto, Jun, Ichikawa, Yasutaka, Iguchi, Nobuo, Imai, Masamichi, Inaki, Anri, Ishimura, Hayato, Isobe, Satoshi, Kadokami, Toshiaki, Kato, Takao, Kumita, Shinichiro, Maruno, Hirotaka, Mataki, Hiroyuki, Miyagawa, Masao, Morimoto, Ryota, Moroi, Masao, Nagamachi, Shigeki, Nakajima, Kenichi, Nakata, Tomoaki, Nakazato, Ryo, Nanasato, Mamoru, Naya, Masanao, Norikane, Takashi, Ohta, Yasutoshi, Okayama, Satoshi, Okizaki, Atsutaka, Otomi, Yoichi, Otsuka, Hideki, Saito, Masaki, Sakata, Sakata Yasushi, Sarai, Masayoshi, Sato, Daisuke, Shiraishi, Shinya, Suwa, Yoshinobu, Takanami, Kentaro, Takehana, Kazuya, Taki, Junichi, Tamaki, Nagara, Taniguchi, Yasuyo, Teragawa, Hiroki, Tomizawa, Nobuo, Tsujita, Kenichi, Umeji, Kyoko, Wakabayashi, Yasushi, Yamada, Shinichiro, Yamazaki, Shinya, Yoneyama, Tatsuya, Rawashdeh, Mohammad, Batyrkhanov, Daultai, Dautov, Tairkhan, Makhdomi, Khalid, Ombati, Kevin, Alkandari, Faridah, Garashi, Masoud, Coie, Tchoyoson Lim, Rajvong, Sonexay, Kalinin, Artem, Kalnina, Marika, Haidar, Mohamad, Komiagiene, Renata, Kviecinskiene, Giedre, Mataciunas, Mindauga, Vajauskas, Donata, Picard, Christian, Karim, Noor Khairiah A., Reichmuth, Luise, Samuel, Anthony, Allarakha, Mohammad Aaftaab, Naojee, Ambedhkar Shantaram, Alexanderson-Rosas, Erick, Barragan, Erika, González-Montecinos, Alejandro Becerril, Cabada, Manuel, Rodriguez, Daniel Calderon, Carvajal-Juarez, Isabel, Cortés, Violeta, Cortés, Filiberto, De La Peña, Erasmo, Gama-Moreno, Manlio, González, Lui, Ramírez, Nelsy Gonzalez, Jiménez-Santos, Moisé, Matos, Lui, Monroy, Edgar, Morelos, Martha, Ornelas, Mario, Ortga Ramirez, Jose Alberto, Preciado-Anaya, André, Preciado-Gutiérrez, Óscar Ulise, Barragan, Adriana Puente, Rosales Uvera, Sandra Graciela, Sandoval, Sigelinda, Tomas, Miguel Santaularia, Sierra-Galan, Lilia M., Siu, Silvia, Vallejo, Enrique, Valles, Mario, Faraggi, Marc, Sereegotov, Erdenechimeg, Ilic, Srdja, Ben-Rais, Nozha, Alaoui, Nadia Ismaili, Taleb, Sara, Myo, Khin Pa, Thu, Phyo Si, Ghimire, Ram Kumar, Rajbanshi, Bijoy, Barneveld, Peter, Glaudemans, Andor, Habets, Jesse, Koopmans, Klaas Pieter, Manders, Jeroen, Pool, Stefan, Scholte, Arthur, Scholtens, Asbjørn, Slart, Riemer, Thimister, Paul, Van Asperen, Erik-Jan, Veltman, Niel, Verschure, Derk, Wagenaar, Nil, Edmond, John, Ellis, Chri, Johnson, Kerryanne, Keenan, Ro, Hua, Shaw, Occleshaw, Christopher, Sasse, Alexander, To, Andrew, Van Pelt, Niel, Young, Calum, Cuadra, Teresa, Roque Vanegas, Hector Bladimir, Soli, Idrissa Adamou, Issoufou, Djibrillou Moussa, Ayodele, Tolulope, Madu, Chibuzo, Onimode, Yetunde, Efros-Monsen, Elen, Forsdahl, Signe Helene, Hildre Dimmen, Jenni-Mari, Jørgensen, Arve, Krohn, Isabel, Løvhaugen, Pål, Bråten, Anders Tjellaug, Al Dhuhli, Humoud, Al Kindi, Faiza, Al-Bulushi, Naeema, Jawa, Zabah, Tag, Naima, Afzal, Muhammad Shehzad, Fatima, Shazia, Younis, Muhammad Numair, Riaz, Musab, Saadullah, Mohammad, Herrera, Yariela, Lenturut-Katal, Dora, Vázquez, Manuel Castillo, Ortellado, José, Akhter, Afroza, Cao, Dianbo, Cheung, Stephen, Dai, Xu, Gong, Lianggeng, Han, Dan, Hou, Yang, Li, Caiying, Li, Tao, Li, Dong, Li, Sijin, Liu, Jinkang, Liu, Hui, Ng, Ming Yen, Sun, Kai, Tang, Gongshun, Wang, Jian, Wang, Ximing, Wang, Zhao-Qian, Wang, Yining, Wang, Yifan, Wu, Jiang, Wu, Zhifang, Xia, Liming, Xiao, Jiangxi, Xu, Lei, Yang, Youyou, Yin, Wu, Yu, Jianqun, Yuan, Li, Zhang, Tong, Zhang, Longjiang, Zhang, Yong-Gao, Zhang, Xiaoli, Zhu, Li, Alfaro, Ana, Abrihan, Paz, Barroso, Asela, Cruz, Eric, Gomez, Marie Rhiamar, Magboo, Vincent Peter, Medina, John Michael, Obaldo, Jerry, Pastrana, Davidson, Pawhay, Christian Michael, Quinon, Alvin, Tang, Jeanelle Margareth, Tecson, Bettina, Uson, Kristine Joy, Uy, Mila, Kostkiewicz, Magdalena, Kunikowska, Jolanta, Bettencourt, Nuno, Cantinho, Guilhermina, Ferreira, Antonio, Syed, Ghulam, Arnous, Samer, Atyani, Said, Byrne, Angela, Gleeson, Tadhg, Kerins, David, Meehan, Conor, Murphy, David, Murphy, Mark, Murray, John, O'Brien, Julie, Bang, Ji-In, Bom, Henry, Cho, Sang-Geon, Hong, Chae Moon, Jang, Su Jin, Jeong, Yong Hyu, Kang, Won Jun, Kim, Ji-Young, Lee, Jaetae, Namgung, Chang Kyeong, So, Young, Won, Kyoung Sook, Majstorov, Venjamin, Vavlukis, Marija, Salobir, Barbara Gužic, Štalc, Monika, Benedek, Theodora, Benedek, Imre, Mititelu, Raluca, Stan, Claudiu Adrian, Ansheles, Alexey, Dariy, Olga, Drozdova, Olga, Gagarina, Nina, Gulyaev, Vsevolod Milyevich, Itskovich, Irina, Karalkin, Anatoly, Kokov, Alexander, Migunova, Ekaterina, Pospelov, Viktor, Ryzhkova, Daria, Saifullina, Guzaliya, Sazonova, Svetlana, Shurupova, Irina, Trifonova, Tatjana, Ussov, Wladimir Yurievich, Vakhromeeva, Margarita, Valiullina, Nailya, Zavadovsky, Konstantin, Zhuravlev, Kirill, Alasnag, Mirvat, Okarvi, Subhani, Saranovic, Dragana Sobic, Keng, Felix, Jason See, Jia Hao, Sekar, Ramkumar, Yew, Min Sen, Vondrak, Andrej, Bejai, Shereen, Bennie, George, Bester, Ria, Engelbrecht, Gerrit, Evbuomwan, Osayande, Gongxeka, Harlem, Vuuren, Magritha Jv, Kaplan, Mitchell, Khushica, Purbhoo, Lakhi, Hoosen, Louw, Lizette, Malan, Nico, Milos, Katarina, Modiselle, Moshe, More, Stuart, Naidoo, Mathava, Scholtz, Leonie, Vangu, Mboyo, Aguadé-Bruix, Santiago, Blanco, Isabel, Cabrera, Antonio, Camarero, Alicia, Casáns-Tormo, Irene, Cuellar-Calabria, Hug, Flotats, Albert, Fuentes Cañamero, Maria Eugenia, García, María Elia, Jimenez-Heffernan, Amelia, Leta, Rubén, Diaz, Javier Lopez, Lumbreras, Lui, Marquez-Cabeza, Juan Javier, Martin, Francisco, Martinez de Alegria, Anxo, Medina, Francisco, Canal, Maria Pedrera, Peiro, Virginia, Pubul-Nuñez, Virginia, Rayo Madrid, Juan Ignacio, Rey, Cristina Rodríguez, Perez, Ricardo Ruano, Ruiz, Joaquín, Hernández, Gertrudis Sabatel, Sevilla, Ana, Zeidán, Nahla, Nanayakkara, Damayanthi, Udugama, Chandraguptha, Simonsson, Magnu, Alkadhi, Hatem, Buechel, Ronny Ralf, Burger, Peter, Ceriani, Luca, De Boeck, Bart, Gräni, Christoph, Juillet de Saint Lager Lucas, Alix, Kamani, Christel H., Kawel-Boehm, Nadine, Manka, Robert, Prior, John O., Rominger, Axel, Vallée, Jean-Paul, Khiewvan, Benjapa, Premprabha, Teerapon, Thientunyakit, Tanyaluck, Sellem, Ali, Kir, Kemal Metin, Sayman, Haluk, Sebikali, Mugisha Juliu, Muyinda, Zerida, Kmetyuk, Yaroslav, Korol, Pavlo, Mykhalchenko, Olena, Pliatsek, Volodymyr, Satyr, Maryna, Albalooshi, Batool, Ahmed Hassan, Mohamed Ismail, Anderson, Jill, Bedi, Punit, Biggans, Thoma, Bularga, Anda, Bull, Russell, Burgul, Rajesh, Carpenter, John-Paul, Coles, Duncan, Cusack, David, Deshpande, Aparna, Dougan, John, Fairbairn, Timothy, Farrugia, Alexia, Gopalan, Deepa, Gummow, Alistair, Ramkumar, Prasad Guntur, Hamilton, Mark, Harbinson, Mark, Hartley, Thoma, Hudson, Benjamin, Joshi, Nikhil, Kay, Michael, Kelion, Andrew, Khokhar, Azhar, Kitt, Jamie, Lee, Ken, Low, Chen, Mak, Sze Mun, Marousa, Ntouskou, Martin, Jon, Mcalindon, Elisa, Menezes, Leon, Morgan-Hughes, Gareth, Moss, Alastair, Murray, Anthony, Nicol, Edward, Patel, Dilip, Peebles, Charle, Pugliese, Francesca, Luis Rodrigues, Jonathan Carl, Rofe, Christopher, Sabharwal, Nikant, Schofield, Rebecca, Semple, Thoma, Sharma, Naveen, Strouhal, Peter, Subedi, Deepak, Topping, William, Tweed, Katharine, Weir-Mccall, Jonathan, Abbara, Suhny, Abbasi, Taimur, Abbott, Brian, Abohashem, Shady, Abramson, Sandra, Al-Abboud, Tarek, Al-Mallah, Mouaz, Almousalli, Omar, Ananthasubramaniam, Karthikeyan, Kumar, Mohan Ashok, Askew, Jeffrey, Attanasio, Lea, Balmer-Swain, Mallory, Bayer, Richard R., Bernheim, Adam, Bhatti, Sabha, Bieging, Erik, Bloom, Stephen, Blue, Sean, Bluemke, David, Borges, Andressa, Branch, Kelley, Bravo, Paco, Brothers, Jessica, Budoff, Matthew, Bullock-Palmer, Renée, Burandt, Angela, Burke, Floyd W., Bush, Kelvin, Candela, Candace, Capasso, Elizabeth, Cavalcante, Joao, Chang, Donald, Chatterjee, Saurav, Chatzizisis, Yianni, Cheezum, Michael, Chen, Tiffany, Chen, Jennifer, Chen, Marcu, Clarcq, Jame, Cordero, Ayreen, Crim, Matthew, Danciu, Sorin, Decter, Bruce, Dhruva, Nimish, Doherty, Neil, Doukky, Rami, Dunbar, Anjori, Duvall, William, Edwards, Rachael, Esquitin, Kerry, Farah, Husam, Fentanes, Emilio, Fisher, Daniel, Fitzpatrick, Daniel, Foster, Cameron, Fuisz, Tony, Gannon, Michael, Gastner, Lori, Gerson, Myron, Ghoshhajra, Brian, Goldberg, Alan, Goldner, Brian, Gonzalez, Jorge, Gore, Rosco, Gracia-López, Sandra, Hage, Fadi, Haider, Agha, Haider, Sofia, Hamirani, Yasmin, Hassen, Karen, Hatfield, Mallory, Hawkins, Carolyn, Hawthorne, Katie, Heath, Nichola, Hendel, Robert, Hernandez, Phillip, Hill, Gregory, Horgan, Stephen, Huffman, Jeff, Hurwitz, Lynne, Iskandrian, Ami, Janardhanan, Rajesh, Jellis, Christine, Jerome, Scott, Kalra, Dinesh, Kaviratne, Summanther, Kay, Fernando, Kelly, Faith, Khalique, Omar, Kinkhabwala, Mona, Iii, George Kinzfogl, Kircher, Jacqueline, Kirkbride, Rachael, Kontos, Michael, Kottam, Anupama, Krepp, Joseph, Layer, Jay, Lee, Steven H., Leppo, Jeffrey, Lesser, John, Leung, Steve, Lewin, Howard, Litmanovich, Diana, Liu, Yiyan, Magurany, Kathleen, Markowitz, Jeremy, Marn, Amanda, Matis, Stephen E., Mckenna, Michael, Mcrae, Tony, Mendoza, Fernando, Merhige, Michael, Min, David, Moffitt, Chanan, Moncher, Karen, Moore, Warren, Morayati, Shamil, Morris, Michael, Mossa-Basha, Mahmud, Mrsic, Zorana, Murthy, Venkatesh, Nagpal, Prashant, Napier, Kyle, Nelson, Katarina, Nijjar, Prabhjot, Osman, Medhat, Passen, Edward, Patel, Amit, Patil, Pravin, Paul, Ryan, Phillips, Lawrence, Polsani, Venkateshwar, Poludasu, Rajaram, Pomerantz, Brian, Porter, Thoma, Prentice, Ryan, Pursnani, Amit, Rabbat, Mark, Ramamurti, Suresh, Rich, Florence, Luna, Hiram Rivera, Robinson, Austin, Robles, Kim, Rodríguez, Cesar, Rorie, Mark, Rumberger, John, Russell, Raymond, Sabra, Philip, Sadler, Diego, Schemmer, Mary, Schoepf, U. Joseph, Shah, Samir, Shah, Nishant, Shanbhag, Sujata, Sharma, Gaurav, Shayani, Steven, Shirani, Jamshid, Shivaram, Pushpa, Sigman, Steven, Simon, Mitch, Slim, Ahmad, Smith, David, Smith, Alexandra, Soman, Prem, Sood, Aditya, Srichai-Parsia, Monvadi Barbara, Streeter, Jame, T, Albert, Tawakol, Ahmed, Thomas, Dustin, Thompson, Randall, Torbet, Tara, Trinidad, Desiree, Ullery, Shawn, Unzek, Samuel, Uretsky, Seth, Vallurupalli, Srikanth, Verma, Vika, Waller, Alfonso, Wang, Ellen, Ward, Parker, Weissman, Gaby, Wesbey, George, White, Kelly, Wolinsky, David, Yost, Sandra, Zgaljardic, Michael, Alonso, Omar, Beretta, Mario, Ferrando, Rodolfo, Kapitan, Miguel, Mut, Fernando, Djuraev, Omoa, Rozikhodjaeva, Gulnora, Le Ngoc, Ha, Mai, Son Hong, Nguyen, Xuan Canh, Hirschfeld, C, Shaw, L, Williams, M, Lahey, R, Villines, T, Dorbala, S, Choi, A, Shah, N, Bluemke, D, Berman, D, Blankstein, R, Ferencik, M, Narula, J, Winchester, D, Malkovskiy, E, Goebel, B, Randazzo, M, Lopez-Mattei, J, Parwani, P, Vitola, J, Cerci, R, Better, N, Raggi, P, Lu, B, Sergienko, V, Sinitsyn, V, Kudo, T, Nørgaard, B, Maurovich-Horvat, P, Cohen, Y, Pascual, T, Pynda, Y, Dondi, M, Paez, D, Einstein, A, and Erba, P

Subjects
Aging, Coronavirus disease 2019 (COVID-19), Clinical Sciences, coronavirus, Disease, 030204 cardiovascular system & hematology, Cardiorespiratory Medicine and Haematology, Cardiovascular, 030218 nuclear medicine & medical imaging, NO, 03 medical and health sciences, 0302 clinical medicine, COVID-19 Testing, Cardiovascular procedures, Predictive Value of Tests, cardiovascular disease, Clinical Research, Environmental health, INCAPS-COVID Investigators Group, Pandemic, Volume reduction, Medicine, Humans, Radiology, Nuclear Medicine and imaging, cardiovascular imaging, diagnostic cardiovascular procedure, Pandemics, business.industry, SARS-CoV-2, Prevention, COVID-19, Health Services, United States/epidemiology, United States, Invasive coronary angiography, coronaviru, Heart Disease, Good Health and Well Being, MYOCARDIAL-INFARCTION, Cardiovascular System & Hematology, Predictive value of tests, HOSPITAL ADMISSIONS, Cardiology and Cardiovascular Medicine, Early phase, business
Abstract

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-U.S. institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection.

Published
2021

25. Research on Competitive Enzymatic Hydrolysis-Assisted Liquid Crystal-based Acetylcholine Sensor

Author

Qi Kang, Yi-Ping Pang, Li Yu, Rong-Hua Zhang, Qiong-Zheng Hu, and Lu-Bin Qi

Subjects
Detection limit, Aqueous solution, integumentary system, 010401 analytical chemistry, Substrate (chemistry), myr, 02 engineering and technology, 021001 nanoscience & nanotechnology, 01 natural sciences, Acetylcholinesterase, 0104 chemical sciences, Analytical Chemistry, chemistry.chemical_compound, chemistry, Liquid crystal, Enzymatic hydrolysis, Monolayer, Biophysics, 0210 nano-technology
Abstract

A simple, convenient and label-free approach was developed for detection of acetylcholine (ACh) based on competitive enzymatic hydrolysis by use of liquid crystals (LCs) as a sensing medium. Self-assembled monolayers of myristoylcholine (Myr) were obtained at the aqueous/LC interface when the surfactants were in contact with LCs, resulting in a perpendicular orientation of LCs at the interface. Thus, LCs displayed a dark optical image. However, enzymatic hydrolysis of Myr by acetylcholinesterase (AChE) disturbed the Myr monolayers, which led to a horizontal orientation of LCs at the interface, thereby inducing a dark-to-bright shift of the LCs optical appearance. Interestingly, as ACh was also a substrate of AChE, the bright appearance of LCs was prevented when incubating the mixture of AChE and ACh with Myr decorated aqueous/LC interface due to the competitive enzymatic hydrolysis between Myr and ACh by AChE. By using this strategy, ACh could be detected selectively and sensitively, with a broad linear range from 0.01 μmol/L to 1 mmol/L and a low detection limit of 14.5 nmol/L. The results showed that the competitive enzymatic hydrolysis-assisted LC-based sensor had potential applications in ACh detection.

Published
2021

26. Rational design of pore structures for carbon aerogels to significantly increase adsorption of tetracycline from water using batch and fixed-bed operation

Author

Zai-Dong Shao, Liang Yu, Lu-Bin Zhong, Shuai Dou, Xuan Cheng, Wen-Cui Xu, and Yu-Ming Zheng

Subjects
Materials science, Hydrogen bond, Graphene, Materials Science (miscellaneous), chemistry.chemical_element, law.invention, chemistry.chemical_compound, Adsorption, Volume (thermodynamics), Chemical engineering, chemistry, law, Nanofiber, Particle, Methyl methacrylate, Carbon, General Environmental Science
Abstract

Three-dimensional (3D) adsorption materials have attracted ever-increasing interest as a cost-effective tool to remove antibiotics from water. However, how to effectively enhance the adsorption capacity of the adsorbents is still a great challenge. In this study, a novel method for fabricating 3D porous carbon nanofiber/graphene oxide composite aerogels (PCNF/GOAs) with an adjustable pore structure was developed for antibiotic removal from water using electrospinning-liquid assisted collection technology. By adding poly(methyl methacrylate) (PMMA) as a pore-forming agent, the volume of pores corresponding to the tetracycline (TC) size (0.8–2 nm) increased twelve-fold, and the adsorption capacity increased nine-fold. To evaluate the feasibility of PCNF/GOAs as a potential adsorbent for antibiotic removal, batch and fixed-bed experiments were conducted. The results showed that PCNF/GOAs effectively removed TC from water over a wide pH range (4–10), and the maximum adsorption capacity was more than 900 mg g−1 at pH 7. The experimental data for the adsorption of TC onto PCNF/GOAs better fitted the pseudo-second-order kinetic and Temkin models. Due to its self-supporting characteristic, 3D PCNF/GOAs generated lower inlet pressure compared to traditional particle adsorbents in fixed-bed column adsorption. π–π electro-donor–acceptor (EDA) interaction, pore filling and hydrogen bonding are the main possible mechanisms for the adsorption of TC on PCNF/GOAs.

Published
2021

27. On the extreme points of slice-stochastic tensors

Author

Lu-Bin Cui, Yan Liang, Rihuan Ke, and Wen Li

Subjects
Pure mathematics, Algebra and Number Theory, 010103 numerical & computational mathematics, 0101 mathematics, Extreme point, 01 natural sciences, Mathematics
Abstract

In this paper, we study the properties of extreme points of slice-stochastic tensors, and present an analogue of the Birkhoff–von Neumann theorem for slice-stochastic tensors. Some equivalent chara...

Published
2020

28. High SNR Φ-OTDR Based on Frequency and Wavelength Diversity With Differential Vector Aggregation Method

Author

Haiwen Cai, Ronghui Qu, Ye Lei, Lu Bin, Jinfeng Gu, Junqi Yang, Wang Zhaoyong, and Qing Ye

Subjects
Physics, Acoustics, Time-domain reflectometer, 02 engineering and technology, Optical time-domain reflectometer, 01 natural sciences, Signal, Noise floor, Atomic and Molecular Physics, and Optics, 010309 optics, Wavelength, symbols.namesake, 020210 optoelectronics & photonics, Amplitude, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, symbols, Fading, Electrical and Electronic Engineering, Rayleigh scattering, Computer Science::Information Theory
Abstract

Phase-demodulated phase-sensitive time domain reflectometer (Φ-OTDR) has attracted much attention because of its linear relationship between the phase variation and the amplitude of the vibration. However, it suffers from limited signal-to-noise ratio (SNR) and signal fading, due to the inherent low Rayleigh scattering coefficient and randomly spatial non-uniform distribution of the refractive index. Here, a Φ-OTDR system based on wavelength diversity is demonstrated, which can enhance the SNR and reduce signal fading simultaneously by using differential vector aggregation method. A theoretical model is built, and the linear relationship between SNR improvement and diversity scale is analyzed. The verification experiment is conducted, and in the further experiments, the frequency and wavelength diversity technology are cascaded to achieve a diversity scale of 20. After differential vector aggregation method, the external disturbance signal is well reconstructed and the noise floor is reduced by 13.4 dB, which is basically consistent with the theoretical analysis.

Published
2020

29. Hysteroscopic management of a 'hernia-like' uterine myoma within the uterine cavity

Author

Chengzhi Zhao, Shen-tao Lu, Ying Wang, Pan Hu, Li Yan, Ming-bo Liu, Lu-bin Liu, and Li Lei

Subjects
Adult, Hernia, Leiomyoma, Cesarean Section, Obstetrics and Gynecology, Hysteroscopy, Reproductive Medicine, Pregnancy, Uterine Neoplasms, Uterine Myomectomy, Humans, Female, Child, Menorrhagia
Abstract

To introduce a case of a uterus "hernia-like" myoma within the uterine cavity that was successfully treated with hysteroscopy.Step-by-step explanation of the case and surgical procedure using a video and slides. The Medical Ethics Committee of Women and Children's Hospital of Chongqing Medical University approved the study and decided for this video to be exempt from formal approval, and informed consent was obtained from the patient.Hospital.A 37-year-old woman, gravida 1, para 1, with a history of secondary infertility for 2 years and heavy menstrual bleeding for 7 months, which eventually resulted in anemia, was diagnosed with a type 1 (International Federation of Gynecology and Obstetrics leiomyoma subclassification system) myoma approximately 4 cm in diameter using ultrasonography. Her hemoglobin level was 8.6 g/dL (11.5-15.0 g/dL), red blood cell count was 3.6 ×10-12/L (3.8-5.1 ×10-12/L), and hematocrit was 26.7% (35%-45%).Hysteroscopic myomectomy with bipolar resectoscope and transabdominal ultrasound guidance was used to resect the hernia-like myoma. Blunt dissection combined with oxytocin (20 U I.V.) and uterine dilation pressure reduction was used to induce the hernial content (myoma) protrusion into the uterine cavity.The hernia-like myoma was completely resected by hysteroscopy, and all symptoms disappeared.The hernia-like myoma was successfully and completely resected by hysteroscopy. The operative time was 37 minutes. No surgical-related or anesthesia-related complications occurred. During the follow-up period, the patient had regular menstrual cycles without hypermenorrhea, and her anemia had improved. The 5-month follow-up ultrasound examination and hysteroscopy revealed that the uterine cavity had recovered well. The patient conceived 7 months after the procedure and delivered at term (39 weeks 1 day) via cesarean section.A hernia-like myoma covered with endometrium is a degenerative myoma that herniates into the myometrium and forms a hernia sac-like defect. Hysteroscopy is an effective and feasible method for treating hernia-like myoma.

Published
2022

30. The Preventive Effect of IL-1beta Antagonist on Diabetic Peripheral Neuropathy

Author

Liu Xiao-xia, Li Qingchun, Li Yiming, Zhu Xiaoming, Xiong Qian, Ji Lijin, Zhang Qi, Zheng Hangping, Zhang Shuo, Zhao Wenting, Han Ling, Hu Ji, and Lu Bin

Subjects
Male, medicine.medical_specialty, Diabetic neuropathy, medicine.drug_class, Endocrinology, Diabetes and Metabolism, medicine.medical_treatment, Interleukin-1beta, Intraperitoneal injection, 030209 endocrinology & metabolism, Diabetes Mellitus, Experimental, Rats, Sprague-Dawley, 03 medical and health sciences, 0302 clinical medicine, Diabetic Neuropathies, Internal medicine, medicine, Animals, Immunology and Allergy, Saline, business.industry, Antagonist, medicine.disease, Receptor antagonist, Streptozotocin, Recombinant Proteins, Rats, Interleukin 1 Receptor Antagonist Protein, Treatment Outcome, Peripheral neuropathy, Endocrinology, Sciatic nerve, business, 030217 neurology & neurosurgery, medicine.drug
Abstract

Objective: To investigate the relationship between Interleukin-1beta (IL-1beta) and diabetic peripheral neuropathy (DPN) using animal models. Materials: The rat model of diabetic neuropathy was induced by intraperitoneal injection of a single dose of streptozotocin (STZ) at 65mg/kg. Diabetic rats were randomly divided into two groups (10 each), one treated with 0.9% saline (DMS group) and the other with interleukin-1 receptor antagonist (IL-1RA) at 50mg/kg (DMI group) twice a day for 5 weeks. Ten normal rats matched for weight, age and sex served as normal controls (Con group) and were treated with saline. Morphologic studies of sciatic nerves were achieved using light and transmission electron microscopy. Results: Transmission electron microscopy of the sciatic nerve showed the ultrastructure of myelin and the axon in the IL-1RA group was highly protected compared to diabetic controls. Conclusions: High levels of circulating IL-1beta may be associated with the risk of DPN and anti-IL-1 treatment may provide a potential strategy for the prevention of diabetic neuropathy.

Published
2020

31. Raw frozen Antarctic krill (Euphausia superba) as an alternative feed source for cuttlefishSepiella japonicain artificial breeding systems

Author

Yu Fangping, Ping Hongling, Jianyu He, Zhang Tao, Jun Liang, Wenbin Zhu, Lu Bin, and Shi Huilai

Subjects
Cuttlefish, 0303 health sciences, education.field_of_study, biology, Euphausia, Zoology, 04 agricultural and veterinary sciences, Aquatic Science, biology.organism_classification, Shrimp, 03 medical and health sciences, Ink sac, Antarctic krill, Sepiella japonica, Cuttlebone, 040102 fisheries, 0401 agriculture, forestry, and fisheries, Palaemon gravieri, education, 030304 developmental biology
Abstract

The study aims to evaluate the feasibility of completely replacing raw frozen shrimp Palaemon gravieri diets with raw frozen Antarctic krill (Euphausia superba) in the diets of cuttlefish Sepiella japonica. To address the knowledge gap, we conducted a 60‐day feeding trial. At the end of the experiment (day 60), the cuttlefish Sepiella japonica eating Palaemon gravieri (SJP) grew significantly faster than those eating Euphausia superba (SJE), with the specific growth rate (SGR)SJP (7.92%) > (SGR)SJE (7.09%). Approximately 33.3% and 20.0% mortality was observed in the SJE and SJP, during the course of the experiment respectively. Some important fatty acids (i.e. n‐3 and n‐6 PUFAs) were elevated in SJE with respect to SJP. Replacement of Antarctic krill increased the diversity of the gut microbiome composition in the SJE group. Fluoride accumulated in the ink sac and cuttlebone of cuttlefish in SJE. Overall, these findings imply that PUFA‐rich Antarctic krill could replace P. gravieri shrimp for feeding cuttlefish S. japonica.

Published
2020

32. Convergent and divergent gray matter volume abnormalities in unaffected first-degree relatives and ultra-high risk individuals of schizophrenia

Author

Bei Lin, Xian-Bin Li, Sen Ruan, Yu-Xin Wu, Chao-Yue Zhang, Chuan-Yue Wang, and Lu-Bin Wang

Abstract

High-risk populations of schizophrenia can be mainly identified as genetic high-risk based on putative endophenotypes or ultra-high-risk (UHR) based on clinically manifested symptoms. Previous studies have consistently shown brain structural abnormalities in both genetic high-risk and UHR individuals. In this study, we aimed to disentangle the convergent and divergent pattern of gray matter alterations between UHR and unaffected first-degree relatives from genetic high-risk individuals. We used structural MRI scans and voxel-based morphometry method to examine gray matter volume (GMV) differences among 23 UHR subjects meeting the Structured Interview for Prodromal Syndromes (SIPS) criteria, 18 unaffected first-degree relatives (UFDR), 26 first-episode schizophrenia patients (FES) and 54 healthy controls (CN). We found that a number of brain regions exhibited a monotonically decreasing trend of GMV from CN to UFDR to UHR to FES. Compared with CN, the UHR subjects showed significant decreases of GMV similar to the patients in the inferior temporal gyrus, fusiform gyrus, middle occipital gyrus, insula, and limbic regions. Moreover, the UHR transformed subgroup had significantly lower GMV than UHR non-transformed subgroup in the right inferior temporal/fusiform gyrus. On the other hand, the UFDR subjects only showed significant GMV decreases in the inferior temporal gyrus and fusiform. Moreover, we found GMV in the occipital lobe was negatively correlated with the UHR subjects’ composite positive symptom of SIPS, and GMV in the cerebellum was positively correlated with FES subjects’ symptom severity. Our results suggest that GMV deficits and regional dysfunction are evident prior to the onset of psychosis and are more prominent in the UHR than the UFDR individuals.

Published
2022

49. A new treatment of an intravesical eroded mesh after TVM: 3 mm trocar-assisted cystoscopic approach

Author

Shen-Tao Lu, Ming-Bo Liu, and Lu-Bin Liu

Subjects
Urology, Urinary Bladder, Obstetrics and Gynecology, Humans, Cystoscopy, Prostheses and Implants, Surgical Mesh, Surgical Instruments
Abstract

To present a novel technique to remove intravesical eroded mesh through a 3-mm trocar-assisted cystoscopy.First, a 3-mm trocar was inserted into the bladder under ultrasound guidance after the bladder had been infused with 600 ml normal saline. Second, we inserted the forceps through the trocar into the bladder and pulled the mesh through the 3-mm trocar. Last, cystoscopic scissors were used to remove the eroded mesh completely.The patient was managed adequately in the inpatient department. The urethral catheter was left in situ for 3 days, and the patient was discharged within 5 days.Surgery under 3-mm trocar-assisted cystoscopy offers the advantage of lower risk of morbidity and complications compared to other surgical techniques. It is an effective and feasible procedure for treatment of synthetic mesh erosion into the bladder after TVM surgery.

Published
2021

50. The Role of the Important Agricultural Heritage Systems in the Construction of China's National Park System and the Optimisation of the Protected Area System

Author

Lu-bin Ding, HE Siyuan, and Qingwen Min

Subjects
Environmental Engineering, Ecology, business.industry, National park, Livelihood, Adaptive management, Agriculture, Resource management, business, Protected area, Community development, Environmental planning, Management by objectives, Ecology, Evolution, Behavior and Systematics
Abstract

Conservation-compatible development of rural communities is an important part of nature conservation objectives. Understanding the role of agriculture, which is often practiced in or bordering the protected areas in rural China, is critical for managing conservation networks considering that limited spatial areas are available for enclosed protected areas. Important Agricultural Heritage Systems stand out for their multi-functionality, and some of their values are compatible with nature conservation. This paper examined the concept, management objectives and resource management characteristics of the Important Agricultural Heritage Systems (IAHS) by analysing their interactions with national parks in terms of community development. The results reveal that management strategies of dynamic conservation, integrated protection and adaptive management of the IAHS can contribute to those national park management objectives concerning conservation-compatible livelihood. However, the typology of the protected area system, including the traditional agricultural system as a new type, needs further consideration.

Published
2021

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