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  Subjects -> MATHEMATICS (Total: 889 journals)
    - APPLIED MATHEMATICS (73 journals)
    - GEOMETRY AND TOPOLOGY (20 journals)
    - MATHEMATICS (659 journals)
    - MATHEMATICS (GENERAL) (42 journals)
    - NUMERICAL ANALYSIS (19 journals)
    - PROBABILITIES AND MATH STATISTICS (76 journals)

MATHEMATICS (659 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 4)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 3)
Academic Voices : A Multidisciplinary Journal     Open Access   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 8)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 25)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 11)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 8)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 3)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Decision Sciences     Open Access   (Followers: 5)
Advances in Difference Equations     Open Access   (Followers: 2)
Advances in Fixed Point Theory     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 2)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Mathematical Physics     Open Access   (Followers: 5)
Advances in Mathematics     Full-text available via subscription   (Followers: 10)
Advances in Numerical Analysis     Open Access   (Followers: 4)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 6)
Advances in Pure Mathematics     Open Access   (Followers: 4)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 5)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Afrika Matematika     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 9)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 1)
Al-Jabar : Jurnal Pendidikan Matematika     Open Access  
Algebra and Logic     Hybrid Journal   (Followers: 4)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Full-text available via subscription   (Followers: 5)
Algorithms     Open Access   (Followers: 11)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Biostatistics     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 4)
American Journal of Mathematical Analysis     Open Access  
American Journal of Mathematics     Full-text available via subscription   (Followers: 7)
American Journal of Operations Research     Open Access   (Followers: 5)
American Mathematical Monthly     Full-text available via subscription   (Followers: 6)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 7)
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access   (Followers: 1)
Analysis     Hybrid Journal   (Followers: 2)
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 3)
Analysis Mathematica     Full-text available via subscription  
Annales Mathematicae Silesianae     Open Access  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annales UMCS, Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 6)
Annals of Mathematics     Full-text available via subscription  
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 7)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access  
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access  
Annuaire du Collège de France     Open Access   (Followers: 5)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applications of Mathematics     Hybrid Journal   (Followers: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Mathematics     Open Access   (Followers: 3)
Applied Mathematics     Open Access   (Followers: 4)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 4)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 1)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access   (Followers: 1)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 3)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 1)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites : The Journal of Space Research Centre of Polish Academy of Sciences     Open Access   (Followers: 20)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Journal of Current Engineering & Maths     Open Access  
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 2)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Axioms     Open Access  
Baltic International Yearbook of Cognition, Logic and Communication     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
BIBECHANA     Open Access   (Followers: 1)
BIT Numerical Mathematics     Hybrid Journal  
BoEM - Boletim online de Educação Matemática     Open Access  
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletim de Educação Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription   (Followers: 1)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 21)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 9)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 4)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 1)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Calculus of Variations and Partial Differential Equations     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 20)
Carpathian Mathematical Publications     Open Access   (Followers: 1)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Collectanea Mathematica     Hybrid Journal  
College Mathematics Journal     Full-text available via subscription   (Followers: 3)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 1)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 3)
Complex Analysis and its Synergies     Open Access   (Followers: 2)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Comptes Rendus Mathematique     Full-text available via subscription   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Concrete Operators     Open Access   (Followers: 4)
Confluentes Mathematici     Hybrid Journal  
COSMOS     Hybrid Journal  
Cryptography and Communications     Hybrid Journal   (Followers: 14)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 11)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 28)
Developments in Clay Science     Full-text available via subscription   (Followers: 1)
Developments in Mineral Processing     Full-text available via subscription   (Followers: 3)
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 2)
Discrete Mathematics     Hybrid Journal   (Followers: 8)
Discrete Mathematics & Theoretical Computer Science     Open Access  
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Dnipropetrovsk University Mathematics Bulletin     Open Access  
Doklady Mathematics     Hybrid Journal  
Duke Mathematical Journal     Full-text available via subscription   (Followers: 1)
Eco Matemático     Open Access  
Edited Series on Advances in Nonlinear Science and Complexity     Full-text available via subscription  
Electronic Journal of Graph Theory and Applications     Open Access   (Followers: 2)
Electronic Notes in Discrete Mathematics     Full-text available via subscription   (Followers: 2)
Elemente der Mathematik     Full-text available via subscription   (Followers: 3)
Energy for Sustainable Development     Hybrid Journal   (Followers: 9)
Enseñanza de las Ciencias : Revista de Investigación y Experiencias Didácticas     Open Access  
Ensino da Matemática em Debate     Open Access  
Entropy     Open Access   (Followers: 5)
ESAIM: Control Optimisation and Calculus of Variations     Full-text available via subscription   (Followers: 1)
European Journal of Combinatorics     Full-text available via subscription   (Followers: 5)
European Journal of Mathematics     Hybrid Journal   (Followers: 1)
European Scientific Journal     Open Access   (Followers: 2)
Experimental Mathematics     Hybrid Journal   (Followers: 4)
Expositiones Mathematicae     Hybrid Journal   (Followers: 2)
Facta Universitatis, Series : Mathematics and Informatics     Open Access  
Fasciculi Mathematici     Open Access  
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 4)

        1 2 3 4 | Last

Journal Cover Computational and Mathematical Methods in Medicine
  [SJR: 0.415]   [H-I: 22]   [2 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1748-670X - ISSN (Online) 1748-6718
   Published by Hindawi Homepage  [284 journals]
  • Sequential Probability Ratio Testing with Power Projective Base Method
           Improves Decision-Making for BCI

    • Abstract: Obtaining a fast and reliable decision is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this study, the EEG signals were firstly analyzed with a power projective base method. Then we were applied a decision-making model, the sequential probability ratio testing (SPRT), for single-trial classification of motor imagery movement events. The unique strength of this proposed classification method lies in its accumulative process, which increases the discriminative power as more and more evidence is observed over time. The properties of the method were illustrated on thirteen subjects’ recordings from three datasets. Results showed that our proposed power projective method outperformed two benchmark methods for every subject. Moreover, with sequential classifier, the accuracies across subjects were significantly higher than that with nonsequential ones. The average maximum accuracy of the SPRT method was 84.1%, as compared with 82.3% accuracy for the sequential Bayesian (SB) method. The proposed SPRT method provides an explicit relationship between stopping time, thresholds, and error, which is important for balancing the time-accuracy trade-off. These results suggest SPRT would be useful in speeding up decision-making while trading off errors in BCI.
      PubDate: Tue, 14 Nov 2017 00:00:00 +000
       
  • Predicting Outcomes in Emergency Medical Admissions Using a Laboratory
           Only Nomogram

    • Abstract: Background. We describe a nomogram to explain an Acute Illness Severity model, derived from emergency room triage and admission laboratory data, to predict 30-day in-hospital survival following an emergency medical admission. Methods. For emergency medical admissions (96,305 episodes in 50,612 patients) between 2002 and 2016, the relationship between 30-day in-hospital mortality and admission laboratory data was determined using logistic regression. The previously validated Acute Illness Severity model was then transposed to a Kattan-style nomogram with a Stata user-written program. Results. The Acute Illness Severity was based on the admission Manchester triage category and biochemical laboratory score; these latter were based on the serum albumin, sodium, potassium, urea, red cell distribution width, and troponin status. The laboratory admission data was predictive with an AUROC of 0.85 (95% CI: 0.85, 0.86). The sensitivity was 94.4%, with a specificity of 62.7%. The positive predictive value was 21.2%, with a negative predictive value of 99.1%. For the Kattan-style nomogram, the regression coefficients are converted to a 100-point scale with the predictor parameters mapped to a probability axis. The nomogram would be an easy-to-use tool at the bedside and for educational purposes, illustrating the relative importance of the contribution of each predictor to the overall score. Conclusion. A nomogram to illustrate and explain the prognostic factors underlying an Acute Illness Severity Score system is described.
      PubDate: Tue, 14 Nov 2017 00:00:00 +000
       
  • Research on Gastroesophageal Reflux Disease Based on Dynamic Features of
           Ambulatory 24-Hour Esophageal pH Monitoring

    • Abstract: Ambulatory 24-hour esophageal pH monitoring has been considered as the gold standard for diagnosing gastroesophageal reflux disease (GERD), and in clinical application, static parameters are widely used, such as DeMeester score. However, a shortcoming of these static variables is their relatively high false negative rate and long recording time required. They may be falsely labeled as nonrefluxers and not appropriately treated. Therefore, it is necessary to seek more accurate and objective parameters to detect and quantify GERD. This paper first describes a new effort that investigated the feasibility of dynamic features of 24-hour pH recording. Wavelet energy, information entropy, and wavelet entropy were estimated for three groups (severe, mild-to-moderate, and normal). The results suggest that wavelet energy and entropy are physiologically meaningful since they differentiated patients with varying degrees of GERD. -means clustering algorithm was employed to obtain the sensitivity and specificity of new parameters. It is obvious that information entropy goes with the highest sensitivity of 87.3% and wavelet energy has the highest specificity of 97.1%. This would allow a more accurate definition of the best indicators to detect and quantify GERD as well as provide an alternative insight into the early diagnosis of GERD.
      PubDate: Tue, 14 Nov 2017 00:00:00 +000
       
  • Image Fusion of CT and MR with Sparse Representation in NSST Domain

    • Abstract: Multimodal image fusion techniques can integrate the information from different medical images to get an informative image that is more suitable for joint diagnosis, preoperative planning, intraoperative guidance, and interventional treatment. Fusing images of CT and different MR modalities are studied in this paper. Firstly, the CT and MR images are both transformed to nonsubsampled shearlet transform (NSST) domain. So the low-frequency components and high-frequency components are obtained. Then the high-frequency components are merged using the absolute-maximum rule, while the low-frequency components are merged by a sparse representation- (SR-) based approach. And the dynamic group sparsity recovery (DGSR) algorithm is proposed to improve the performance of the SR-based approach. Finally, the fused image is obtained by performing the inverse NSST on the merged components. The proposed fusion method is tested on a number of clinical CT and MR images and compared with several popular image fusion methods. The experimental results demonstrate that the proposed fusion method can provide better fusion results in terms of subjective quality and objective evaluation.
      PubDate: Thu, 09 Nov 2017 10:07:33 +000
       
  • Periodically Pulsed Immunotherapy in a Mathematical Model of Tumor, CD4+ T
           Cells, and Antitumor Cytokine Interactions

    • Abstract: Immunotherapy is one of the most recent approaches for controlling and curing malignant tumors. In this paper, we consider a mathematical model of periodically pulsed immunotherapy using T cells and an antitumor cytokine. Mathematical analyses are performed to determine the threshold of a successful treatment. The interindividual variability is explored by one-, two-, and three-parameter bifurcation diagrams for a nontreatment case. Numerical simulation conducted in this paper shows that (i) the tumor can be regulated by administering T cells alone in a patient with a strong immune system or who has been diagnosed at an early stage, (ii) immunotherapy with a large amount of an antitumor cytokine can boost the immune system to remit or even to suppress tumor cells completely, and (iii) through polytherapy the tumor can be kept at a smaller size with reduced dosages.
      PubDate: Thu, 09 Nov 2017 09:05:09 +000
       
  • Computing Individual Risks Based on Family History in Genetic Disease in
           the Presence of Competing Risks

    • Abstract: When considering a genetic disease with variable age at onset (e.g., familial amyloid neuropathy, cancers), computing the individual risk of the disease based on family history (FH) is of critical interest for both clinicians and patients. Such a risk is very challenging to compute because the genotype of the individual of interest is in general unknown, the posterior distribution changes with ( is the age at disease onset for the targeted individual), and the competing risk of death is not negligible. In this work, we present modeling of this problem using a Bayesian network mixed with (right-censored) survival outcomes where hazard rates only depend on the genotype of each individual. We explain how belief propagation can be used to obtain posterior distribution of genotypes given the FH and how to obtain a time-dependent posterior hazard rate for any individual in the pedigree. Finally, we use this posterior hazard rate to compute individual risk, with or without the competing risk of death. Our method is illustrated using the Claus-Easton model for breast cancer. The competing risk of death is derived from the national French registry.
      PubDate: Thu, 09 Nov 2017 00:00:00 +000
       
  • A Hybrid Wavelet-Based Method for the Peak Detection of
           Photoplethysmography Signals

    • Abstract: The noninvasive peripheral oxygen saturation (SpO2) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects’ PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.
      PubDate: Tue, 07 Nov 2017 07:16:49 +000
       
  • Posterior Estimates of Dynamic Constants in HIV Transmission Modeling

    • Abstract: In this paper, we construct a linear differential system in both continuous time and discrete time to model HIV transmission on the population level. The main question is the determination of parameters based on the posterior information obtained from statistical analysis of the HIV population. We call these parameters dynamic constants in the sense that these constants determine the behavior of the system in various models. There is a long history of using linear or nonlinear dynamic systems to study the HIV population dynamics or other infectious diseases. Nevertheless, the question of determining the dynamic constants in the system has not received much attention. In this paper, we take some initial steps to bridge such a gap. We study the dynamic constants that appear in the linear differential system model in both continuous and discrete time. Our computations are mostly carried out in Matlab.
      PubDate: Tue, 07 Nov 2017 00:00:00 +000
       
  • A Structure Design Method for Reduction of MRI Acoustic Noise

    • Abstract: The acoustic problem of the split gradient coil is one challenge in a Magnetic Resonance Imaging and Linear Accelerator (MRI-LINAC) system. In this paper, we aimed to develop a scheme to reduce the acoustic noise of the split gradient coil. First, a split gradient assembly with an asymmetric configuration was designed to avoid vibration in same resonant modes for the two assembly cylinders. Next, the outer ends of the split main magnet were constructed using horn structures, which can distribute the acoustic field away from patient region. Finally, a finite element method (FEM) was used to quantitatively evaluate the effectiveness of the above acoustic noise reduction scheme. Simulation results found that the noise could be maximally reduced by 6.9 dB and 5.6 dB inside and outside the central gap of the split MRI system, respectively, by increasing the length of one gradient assembly cylinder by 20 cm. The optimized horn length was observed to be 55 cm, which could reduce noise by up to 7.4 dB and 5.4 dB inside and outside the central gap, respectively. The proposed design could effectively reduce the acoustic noise without any influence on the application of other noise reduction methods.
      PubDate: Mon, 06 Nov 2017 00:00:00 +000
       
  • A Cross-Lingual Mobile Medical Communication System Prototype for
           Foreigners and Subjects with Speech, Hearing, and Mental Disabilities
           Based on Pictograms

    • Abstract: People with speech, hearing, or mental impairment require special communication assistance, especially for medical purposes. Automatic solutions for speech recognition and voice synthesis from text are poor fits for communication in the medical domain because they are dependent on error-prone statistical models. Systems dependent on manual text input are insufficient. Recently introduced systems for automatic sign language recognition are dependent on statistical models as well as on image and gesture quality. Such systems remain in early development and are based mostly on minimal hand gestures unsuitable for medical purposes. Furthermore, solutions that rely on the Internet cannot be used after disasters that require humanitarian aid. We propose a high-speed, intuitive, Internet-free, voice-free, and text-free tool suited for emergency medical communication. Our solution is a pictogram-based application that provides easy communication for individuals who have speech or hearing impairment or mental health issues that impair communication, as well as foreigners who do not speak the local language. It provides support and clarification in communication by using intuitive icons and interactive symbols that are easy to use on a mobile device. Such pictogram-based communication can be quite effective and ultimately make people’s lives happier, easier, and safer.
      PubDate: Thu, 02 Nov 2017 00:00:00 +000
       
  • Advanced Image Enhancement Method for Distant Vessels and Structures in
           Capsule Endoscopy

    • Abstract: This paper proposes an advanced method for contrast enhancement of capsule endoscopic images, with the main objective to obtain sufficient information about the vessels and structures in more distant (or darker) parts of capsule endoscopic images. The proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively. The half-unit weighted-bilinear algorithm (HWB) proposed in our previous work is used to enhance darker areas according to the darker map content of its HSV’s component . Enhancement of brighter areas is achieved thanks to the novel threshold weighted-bilinear algorithm (TWB) developed to avoid overexposure and enlargement of specular highlight spots while preserving the hue, in such areas. The TWB performs enhancement operations following a gradual increment of the brightness of the brighter map content of its HSV’s component . In other words, the TWB decreases its averaged weights as the intensity content of the component increases. Extensive experimental demonstrations were conducted, and, based on evaluation of the reference and PM enhanced images, a gastroenterologist (Ø.H.) concluded that the PM enhanced images were the best ones based on the information about the vessels, contrast in the images, and the view or visibility of the structures in more distant parts of the capsule endoscopy images.
      PubDate: Tue, 31 Oct 2017 07:05:03 +000
       
  • Dynamics of Immune Checkpoints, Immune System, and BCG in the Treatment of
           Superficial Bladder Cancer

    • Abstract: This paper aims to study the dynamics of immune suppressors/checkpoints, immune system, and BCG in the treatment of superficial bladder cancer. Programmed cell death protein-1 (PD-1), cytotoxic T-lymphocyte-associated antigen 4 (CTLA4), and transforming growth factor-beta (TGF-β) are some of the examples of immune suppressors/checkpoints. They are responsible for deactivating the immune system and enhancing immunological tolerance. Moreover, they categorically downregulate and suppress the immune system by preventing and blocking the activation of T-cells, which in turn decreases autoimmunity and enhances self-tolerance. In cancer immunotherapy, the immune checkpoints/suppressors prevent and block the immune cells from attacking, spreading, and killing the cancer cells, which leads to cancer growth and development. We formulate a mathematical model that studies three possible dynamics of the treatment and establish the effects of the immune checkpoints on the immune system and the treatment at large. Although the effect cannot be seen explicitly in the analysis of the model, we show it by numerical simulations.
      PubDate: Thu, 26 Oct 2017 00:00:00 +000
       
  • Interactive -Means Clustering Method Based on User Behavior for Different
           Analysis Target in Medicine

    • Abstract: Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor’s knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive -means clustering method to improve the user’s satisfactions towards the result. The core of this method is to get the user’s feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user’s business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user’s requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.
      PubDate: Thu, 26 Oct 2017 00:00:00 +000
       
  • The Dynamical Behaviors for a Class of Immunogenic Tumor Model with Delay

    • Abstract: This paper aims at studying the model proposed by Kuznetsov and Taylor in 1994. Inspired by Mayer et al., time delay is introduced in the general model. The dynamic behaviors of this model are studied, which include the existence and stability of the equilibria and Hopf bifurcation of the model with discrete delays. The properties of the bifurcated periodic solutions are studied by using the normal form on the center manifold. Numerical examples and simulations are given to illustrate the bifurcation analysis and the obtained results.
      PubDate: Wed, 25 Oct 2017 09:11:23 +000
       
  • Measuring Coupling of Rhythmical Time Series Using Cross Sample Entropy
           and Cross Recurrence Quantification Analysis

    • Abstract: The aim of this investigation was to compare and contrast the use of cross sample entropy (xSE) and cross recurrence quantification analysis (cRQA) measures for the assessment of coupling of rhythmical patterns. Measures were assessed using simulated signals with regular, chaotic, and random fluctuations in frequency, amplitude, and a combination of both. Biological data were studied as models of normal and abnormal locomotor-respiratory coupling. Nine signal types were generated for seven frequency ratios. Fifteen patients with COPD (abnormal coupling) and twenty-one healthy controls (normal coupling) walked on a treadmill at three speeds while breathing and walking were recorded. xSE and the cRQA measures of percent determinism, maximum line, mean line, and entropy were quantified for both the simulated and experimental data. In the simulated data, xSE, percent determinism, and entropy were influenced by the frequency manipulation. The 1 : 1 frequency ratio was different than other frequency ratios for almost all measures and/or manipulations. The patients with COPD used a 2 : 3 ratio more often and xSE, percent determinism, maximum line, mean line, and cRQA entropy were able to discriminate between the groups. Analysis of the effects of walking speed indicated that all measures were able to discriminate between speeds.
      PubDate: Sun, 22 Oct 2017 00:00:00 +000
       
  • Deep Convolutional Neural Networks for Classifying Body Constitution Based
           on Face Image

    • Abstract: Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.
      PubDate: Wed, 18 Oct 2017 00:00:00 +000
       
  • Pairwise Multiple Comparison Adjustment Procedure for Survival Functions
           with Right-Censored Data

    • Abstract: The aim of this study is to propose a new pairwise multiple comparison adjustment procedure based on Genz’s numerical computation of probabilities from a multivariate normal distribution. This method is applied to the results of two-sample log-rank and weighted log-rank statistics where the survival data contained right-censored observations. We conducted Monte Carlo simulation studies not only to evaluate the familywise error rate and power of the proposed procedure but also to compare the procedure with conventional methods. The proposed method is also applied to the data set consisting of 815 patients on a liver transplant waiting list from 1990 to 1999. It was found that the proposed method can control the type I error rate, and it yielded similar power as Tukey’s and high power with respect to the other adjustment procedures. In addition to having a straightforward formula, it is easy to implement.
      PubDate: Thu, 12 Oct 2017 00:00:00 +000
       
  • Uncertainty Analysis in the Calibration of an Emission Tomography System
           for Quantitative Imaging

    • Abstract: It is generally acknowledged that calibration of the imaging system (be it a SPECT or a PET scanner) is one of the critical components associated with in vivo activity quantification in nuclear medicine. The system calibration is generally performed through the acquisition of a source with a known amount of radioactivity. The decay-corrected calibration factor is the “output” quantity in a measurement model for the process. This quantity is a function of a number of “input” variables, including total counts in the volume of interest (VOI), radionuclide activity concentration, source volume, acquisition duration, radionuclide half-life, and calibration time of the radionuclide. Uncertainties in the input variables propagate through the calculation to the “combined” uncertainty in the output quantity. In the present study, using the general formula given in the GUM (Guide to the Expression of Uncertainty in Measurement) for aggregating uncertainty components, we derive a practical relation to assess the combined standard uncertainty for the calibration factor of an emission tomography system. At a time of increasing need for accuracy in quantification studies, the proposed approach has the potential to be easily implemented in clinical practice.
      PubDate: Thu, 12 Oct 2017 00:00:00 +000
       
  • Construction of an miRNA-Regulated Pathway Network Reveals Candidate
           Biomarkers for Postmenopausal Osteoporosis

    • Abstract: We aimed to identify risk pathways for postmenopausal osteoporosis (PMOP) via establishing an microRNAs- (miRNA-) regulated pathway network (MRPN). Firstly, we identified differential pathways through calculating gene- and pathway-level statistics based on the accumulated normal samples using the individual pathway aberrance score (iPAS). Significant pathways based on differentially expressed genes (DEGs) using DAVID were extracted, followed by identifying the common pathways between iPAS and DAVID methods. Next, miRNAs prediction was implemented via calculating TargetScore values with precomputed input (log fold change (FC), TargetScan context score (TSCS), and probabilities of conserved targeting (PCT)). An MRPN construction was constructed using the common genes in the common pathways and the predicted miRNAs. Using false discovery rate (FDR) < 0.05, 279 differential pathways were identified. Using the criteria of FDR < 0.05 and , 39 DEGs were retrieved, and these DEGs were enriched in 64 significant pathways identified by DAVID. Overall, 27 pathways were the common ones between two methods. Importantly, MAPK signaling pathway and PI3K-Akt signaling pathway were the first and second significantly enriched ones, respectively. These 27 common pathways separated PMOP from controls with the accuracy of 0.912. MAPK signaling pathway and PI3K/Akt signaling pathway might play crucial roles in PMOP.
      PubDate: Wed, 11 Oct 2017 00:00:00 +000
       
  • Hemoglobin-Dilution Method: Effect of Measurement Errors on Vascular
           Volume Estimation

    • Abstract: The hemoglobin-dilution method (HDM) has been used to estimate changes in vascular volumes in patients because direct measurements with radioisotopes are time-consuming and not practical in many facilities. The HDM requires an assumption of initial blood volume, repeated measurements of plasma hemoglobin concentration, and the calculation of the ratio of hemoglobin measurements. The statistics of these ratio distributions resulting from measurement error are ill-defined even when the errors are normally distributed. This study uses a “Monte Carlo” approach to determine the distribution of these errors. The finding was that these errors could be closely approximated with a log-normal distribution that can be parameterized by a geometric mean () and a dispersion factor (). When the ratio of successive Hb concentrations is used to estimate blood volume, normally distributed hemoglobin measurement errors tend to produce exponentially higher values of and as the SD of the measurement error increases. The longer tail of the distribution to the right could produce much greater overestimations than would be expected from the SD values of the measurement error; however, it was found that averaging duplicate and triplicate hemoglobin measurements on a blood sample greatly improved the accuracy.
      PubDate: Mon, 02 Oct 2017 00:00:00 +000
       
  • Predictive Modelling Based on Statistical Learning in Biomedicine

    • PubDate: Thu, 28 Sep 2017 13:39:58 +000
       
  • Numerical Investigation of Pulse Wave Propagation in Arteries Using Fluid
           Structure Interaction Capabilities

    • Abstract: The aim of this study is to present a reliable computational scheme to serve in pulse wave velocity (PWV) assessment in large arteries. Clinicians considered it as an indication of human blood vessels’ stiffness. The simulation of PWV was conducted using a 3D elastic tube representing an artery. The constitutive material model specific for vascular applications was applied to the tube material. The fluid was defined with an equation of state representing the blood material. The onset of a velocity pulse was applied at the tube inlet to produce wave propagation. The Coupled Eulerian-Lagrangian (CEL) modeling technique with fluid structure interaction (FSI) was implemented. The scaling of sound speed and its effect on results and computing time is discussed and concluded that a value of 60 m/s was suitable for simulating vascular biomechanical problems. Two methods were used: foot-to-foot measurement of velocity waveforms and slope of the regression line of the wall radial deflection wave peaks throughout a contour plot. Both methods showed coincident results. Results were approximately 6% less than those calculated from the Moens-Korteweg equation. The proposed method was able to describe the increase in the stiffness of the walls of large human arteries via the PWV estimates.
      PubDate: Sun, 24 Sep 2017 06:27:57 +000
       
  • Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control
           Studies

    • Abstract: Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia.
      PubDate: Sun, 24 Sep 2017 00:00:00 +000
       
  • Estimating and Interpreting Effects from Nonlinear Exposure-Response
           Curves in Occupational Cohorts Using Truncated Power Basis Expansions and
           Penalized Splines

    • Abstract: Truncated power basis expansions and penalized spline methods are demonstrated for estimating nonlinear exposure-response relationships in the Cox proportional hazards model. R code is provided for fitting models to get point and interval estimates. The method is illustrated using a simulated data set under a known exposure-response relationship and in a data application examining risk of carpal tunnel syndrome in an occupational cohort.
      PubDate: Wed, 20 Sep 2017 06:18:44 +000
       
  • Corrigendum to “Comparative Sensitivity Analysis of Muscle
           Activation Dynamics”

    • PubDate: Wed, 20 Sep 2017 00:00:00 +000
       
  • Modeling Impact of Temperature and Human Movement on the Persistence of
           Dengue Disease

    • Abstract: Dengue is a vector-borne infectious disease endemic in many parts of the world. The disease is spreading in new places due to human movement into the dengue disease supporting areas. Temperature is the major climatic factor which affects the biological processes of the mosquitoes and their interaction with the viruses. In the present work, we propose a multipatch model to assess the impact of temperature and human movement in the transmission dynamics of dengue disease. The work consists of system of ordinary differential equations that describe the transmission dynamics of dengue disease between humans and mosquitoes. Human population is divided into four classes: susceptible, exposed, infectious, and recovered. Mosquito population is divided into three classes: susceptible, exposed, and infectious. Basic reproduction number of the model is obtained using Next-Generation Matrix method. The qualitative analysis of the model is made in terms of the basic reproduction number. Parameters used in the model are considered temperature dependent. Dynamics of vector and host populations are investigated with different human movement rates and different temperature levels. Numerical results show that proper management of human movement between patches helps reducing the burden of dengue disease. It is also seen that the temperature affects the transmission dynamics of the disease significantly.
      PubDate: Tue, 19 Sep 2017 09:26:28 +000
       
  • Association of Stressful Life Events with Psychological Problems: A
           Large-Scale Community-Based Study Using Grouped Outcomes Latent Factor
           Regression with Latent Predictors

    • Abstract: Objective. The current study is aimed at investigating the association between stressful life events and psychological problems in a large sample of Iranian adults. Method. In a cross-sectional large-scale community-based study, 4763 Iranian adults, living in Isfahan, Iran, were investigated. Grouped outcomes latent factor regression on latent predictors was used for modeling the association of psychological problems (depression, anxiety, and psychological distress), measured by Hospital Anxiety and Depression Scale (HADS) and General Health Questionnaire (GHQ-12), as the grouped outcomes, and stressful life events, measured by a self-administered stressful life events (SLEs) questionnaire, as the latent predictors. Results. The results showed that the personal stressors domain has significant positive association with psychological distress (), anxiety (), depression (), and their collective profile score (), with greater associations in females () than in males () (all ). In addition, in the adjusted models, the regression coefficients for the association of social stressors domain and psychological problems profile score were 0.37, 0.35, and 0.46 in total sample, males, and females, respectively (). Conclusion. Results of our study indicated that different stressors, particularly those socioeconomic related, have an effective impact on psychological problems. It is important to consider the social and cultural background of a population for managing the stressors as an effective approach for preventing and reducing the destructive burden of psychological problems.
      PubDate: Tue, 19 Sep 2017 00:00:00 +000
       
  • Stochastic Models of Emerging Infectious Disease Transmission on Adaptive
           Random Networks

    • Abstract: We presented adaptive random network models to describe human behavioral change during epidemics and performed stochastic simulations of SIR (susceptible-infectious-recovered) epidemic models on adaptive random networks. The interplay between infectious disease dynamics and network adaptation dynamics was investigated in regard to the disease transmission and the cumulative number of infection cases. We found that the cumulative case was reduced and associated with an increasing network adaptation probability but was increased with an increasing disease transmission probability. It was found that the topological changes of the adaptive random networks were able to reduce the cumulative number of infections and also to delay the epidemic peak. Our results also suggest the existence of a critical value for the ratio of disease transmission and adaptation probabilities below which the epidemic cannot occur.
      PubDate: Sun, 17 Sep 2017 00:00:00 +000
       
  • Biomarker MicroRNAs for Diagnosis of Oral Squamous Cell Carcinoma
           Identified Based on Gene Expression Data and MicroRNA-mRNA Network
           Analysis

    • Abstract: Oral squamous cell carcinoma is one of the most malignant tumors with high mortality rate worldwide. Biomarker discovery is critical for early diagnosis and precision treatment of this disease. MicroRNAs are small noncoding RNA molecules which often regulate essential biological processes and are good candidates for biomarkers. By integrative analysis of both the cancer-associated gene expression data and microRNA-mRNA network, miR-148b-3p, miR-629-3p, miR-27a-3p, and miR-142-3p were screened as novel diagnostic biomarkers for oral squamous cell carcinoma based on their unique regulatory abilities in the network structure of the conditional microRNA-mRNA network and their important functions. These findings were confirmed by literature verification and functional enrichment analysis. Future experimental validation is expected for the further investigation of their molecular mechanisms.
      PubDate: Sun, 17 Sep 2017 00:00:00 +000
       
  • Modelling Risk to US Military Populations from Stopping Blanket Mandatory
           Polio Vaccination

    • Abstract: Objectives. Transmission of polio poses a threat to military forces when deploying to regions where such viruses are endemic. US-born soldiers generally enter service with immunity resulting from childhood immunization against polio; moreover, new recruits are routinely vaccinated with inactivated poliovirus vaccine (IPV), supplemented based upon deployment circumstances. Given residual protection from childhood vaccination, risk-based vaccination may sufficiently protect troops from polio transmission. Methods. This analysis employed a mathematical system for polio transmission within military populations interacting with locals in a polio-endemic region to evaluate changes in vaccination policy. Results. Removal of blanket immunization had no effect on simulated polio incidence among deployed military populations when risk-based immunization was employed; however, when these individuals reintegrated with their base populations, risk of transmission to nondeployed personnel increased by 19%. In the absence of both blanket- and risk-based immunization, transmission to nondeployed populations increased by 25%. The overall number of new infections among nondeployed populations was negligible for both scenarios due to high childhood immunization rates, partial protection against transmission conferred by IPV, and low global disease incidence levels. Conclusion. Risk-based immunization driven by deployment to polio-endemic regions is sufficient to prevent transmission among both deployed and nondeployed US military populations.
      PubDate: Thu, 14 Sep 2017 00:00:00 +000
       
 
 
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