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The plate-like graphene shells (GS) produced by an original methane pyrolysis method and their derivatives graphene oxide (GO) and graphene oxide paper (GO-P) were evaluated with luminescent Escherichia coli biotests and additional bacterial-based assays which together revealed the graphene-family nanomaterials’ toxicity and bioactivity mechanisms. Bioluminescence inhibition assay, fluorescent two-component staining to evaluate cell membrane permeability, and atomic force microscopy data showed GO expressed bioactivity in aqueous suspension, whereas GS suspensions and the GO-P surface were assessed as nontoxic materials. The mechanism of toxicity of GO was shown not to be associated with oxidative stress in the targeted soxS::lux and katG::lux reporter cells; also, GO did not lead to significant mechanical disruption of treated bacteria with the release of intracellular DNA contents into the environment. The well-coordinated time- and dose-dependent surface charge neutralization and transport and energetic disorders in the Escherichia coli cells suggest direct membrane interaction, internalization, and perturbation (i.e., “membrane stress”) as a clue to graphene oxide’s mechanism of toxicity. [ABSTRACT FROM AUTHOR]

Hackathons are collaborative events that bring together diverse groups to solve predefined challenges. The COVID-19 pandemic caused by SARS-CoV-2 has emphasized the need for portable and reproducible genomics analysis pipelines to study the genetic susceptibility of the human host and investigate human-SARS-CoV-2 protein interactions. To build and strengthen institutional capacities in OMICS data analysis applied to host-pathogen interaction (HPI), the PHINDaccess project organized two hackathons in 2020 and 2021. These hackathons are aimed at developing bioinformatics pipelines related to the SARS-CoV-2 viral genome, its phylodynamic transmission, and the identification of human genome host variants, with a focus on addressing global health challenges, particularly in low- and middle-income countries (LMIC). This paper outlines the preparation, proceedings, and lessons learned from these hackathons, including the challenges faced by participants and our recommendations based on our experience for organizing hackathons in LMIC and beyond. [ABSTRACT FROM AUTHOR]

The elbow joint is prone to stiffness and adhesion after trauma or surgery. High-energy trauma easily led to loss of mobility of the elbow joint. Mild trauma can also cause stiffness in the elbow joint. In order to investigate the pathogenesis of stiffness and adhesion in elbow joint, a feedback-type elbow joint control system based on network is proposed in this paper. The simulation results show that it has significant differences in elbow bioelectrical impedance in patients with elbow trauma, and this new method realizes the control strategy of converting the patient's elbow joint pathological information into elbow joint orthosis control information. [ABSTRACT FROM AUTHOR]

This study showed the prevalence of human papillomavirus (HPV) variants as well as nucleotide changes within L1 gene and LCR of the HPV16, HPV31, and HPV58 found in cervical lesions of women from North-East Brazil. [ABSTRACT FROM AUTHOR]

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F -score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance. [ABSTRACT FROM AUTHOR]

Long bone defects represent a clinical challenge. Bone tissue engineering (BTE) has been developed to overcome problems associated with conventional methods. The aim of this study was to assess the BTE strategies available in preclinical and clinical settings and the current evidence supporting this approach. A systematic literature screening was performed on PubMed database, searching for both preclinical (only on large animals) and clinical studies. The following string was used: “(Scaffold OR Implant) AND (Long bone defect OR segmental bone defect OR large bone defect OR bone loss defect).” The search retrieved a total of 1573 articles: 51 preclinical and 4 clinical studies were included. The great amount of preclinical papers published over the past few years showed promising findings in terms of radiological and histological evidence. Unfortunately, this in vivo situation is not reflected by a corresponding clinical impact, with few published papers, highly heterogeneous and with small patient populations. Several aspects should be further investigated to translate positive preclinical findings into clinical protocols: the identification of the best biomaterial, with both biological and biomechanical suitable properties, and the selection of the best choice between cells, GFs, or their combination through standardized models to be validated by randomized trials. [ABSTRACT FROM AUTHOR]

Cancer is a complex disease residing in various tissues of human body, accompanied with many abnormalities and mutations in genomes, transcriptome, and epigenome. Early detection plays a crucial role in extending survival time of all major cancer types. Recent advances in microarray and sequencing techniques have given more support to identifying effective biomarkers for early detection of cancer. MicroRNAs (miRNAs) are more and more frequently used as candidates for biomarkers in cancer related studies due to their regulation of target gene expression. In this paper, the comparative analysis is used to discover miRNA expression patterns in cancer versus normal samples on early stage of eight prevalent cancer types. Our work focuses on the specific miRNAs biomarkers identification and function analysis. Several identified miRNA biomarkers in this paper are matched well with those reported in existing researches, and most of them could serve as potential candidate indicators for clinical early diagnosis applications. [ABSTRACT FROM AUTHOR]

Hormone replacement therapy (HRT) increases the risk of endometrial and breast cancer. A strategy to reduce this incidence is the use of tibolone (TIB). The aim of this paper was to address the effects of TIB on the central nervous system (CNS). For the present review, MEDLINE (via PubMed), LILACS (via BIREME), Ovid Global Health, SCOPUS, Scielo, and PsycINFO (ProQuest Research Library) electronic databases were searched for the results of controlled clinical trials on peri- and postmenopausal women published from 1990 to September 2016. Also, this paper reviews experimental studies performed to analyze neuroprotective effects, cognitive deficits, neuroplasticity, oxidative stress, and stroke using TIB. Although there are few studies on the effect of this hormone in the CNS, it has been reported that TIB decreases lipid peroxidation levels and improves memory and learning. TIB has important neuroprotective effects that could prevent the risk of neurodegenerative diseases in postmenopausal women as well as the benefits of HRT in counteracting hot flashes, improving mood, and libido. Some reports have found that TIB delays cognitive impairment in various models of neuronal damage. It also modifies brain plasticity since it acts as an endocrine modulator regulating neurotransmitters, Tau phosphorylation, and decreasing neuronal death. Finally, its antioxidant effects have also been reported in different animal models. [ABSTRACT FROM AUTHOR]

One of important sources of biomass-based fuel is Jatropha curcas L. Great attention is paid to the biofuel produced from the oil extracted from the Jatropha curcas L. seeds. A mechanised extraction is the most efficient and feasible method for oil extraction for small-scale farmers but there is a need to extract oil in more efficient manner which would increase the labour productivity, decrease production costs, and increase benefits of small-scale farmers. On the other hand innovators should be aware that further machines development is possible only when applying the systematic approach and design methodology in all stages of engineering design. Systematic approach in this case means that designers and development engineers rigorously apply scientific knowledge, integrate different constraints and user priorities, carefully plan product and activities, and systematically solve technical problems. This paper therefore deals with the complex approach to design specification determining that can bring new innovative concepts to design of mechanical machines for oil extraction. The presented case study as the main part of the paper is focused on new concept of screw of machine mechanically extracting oil from Jatropha curcas L. seeds. [ABSTRACT FROM AUTHOR]

Purpose. The aim of this paper is to review the published studies on the characteristics of impairments in the postural control and gait performance in diabetic peripheral neuropathy (DPN). Methods. A review was performed by obtaining publication of all papers reporting on the postural control and gait performance in DPN from Google Scholar, Ovid, SAGE, Springerlink, Science Direct (SD), EBSCO Discovery Service, and Web of Science databases. The keywords used for searching were “postural control,” “balance,” “gait performance,” “diabetes mellitus,” and “diabetic peripheral neuropathy.” Results. Total of 4,337 studies were hit in the search. 1,524 studies were screened on their titles and citations. Then, 79 studies were screened on their abstract. Only 38 studies were eligible to be selected: 17 studies on postural control and 21 studies on the gait performance. Most previous researches were found to have strong evidence of postural control impairments and noticeable gait deficits in DPN. Deterioration of somatosensory, visual, and vestibular systems with the pathologic condition of diabetes on cognitive impairment causes further instability of postural and gait performance in DPN. Conclusions. Postural instability and gait imbalance in DPN may contribute to high risk of fall incidence, especially in the geriatric population. Thus, further works are crucial to highlight this fact in the hospital based and community adults. [ABSTRACT FROM AUTHOR]

Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification. Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs. Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination. Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions. [ABSTRACT FROM AUTHOR]

Detection of single based genetic mutation by using oligonucleotide probes is one of the common methods of detecting single nucleotide polymorphisms at known loci. In this paper, we demonstrated a hybridization system which included a buffer solution that produced selective salt-induced effect and a locked nucleic acid modified 12 nt oligonucleotide probe. The hybridization system is suitable for hybridization under room temperature. By using magnetic nanoparticles as carriers for PCR products, the SNPs (MDR1 C3435T/A) from 45 volunteers were analyzed, and the results were consistent with the results from pyrophosphoric acid sequencing. The method presented in this paper differs from the traditional method of using molecular beacons to detect SNPs in that it is suitable for research institutions lacking real-time quantitative PCR detecting systems, to detect PCR products at room temperature. [ABSTRACT FROM AUTHOR]

In this paper, a multilayer secure biomedical data management system for managing a very large number of diverse personal health devices is proposed. The system has the following characteristics: the system supports international standard communication protocols to achieve interoperability. The system is integrated in the sense that both a PHD communication system and a remote PHD management system work together as a single system. Finally, the system proposed in this paper provides user/message authentication processes to securely transmit biomedical data measured by PHDs based on the concept of a biomedical signature. Some experiments, including the stress test, have been conducted to show that the system proposed/constructed in this study performs very well even when a very large number of PHDs are used. For a stress test, up to 1,200 threads are made to represent the same number of PHD agents. The loss ratio of the ISO/IEEE 11073 messages in the normal system is as high as 14% when 1,200 PHD agents are connected. On the other hand, no message loss occurs in the multilayered system proposed in this study, which demonstrates the superiority of the multilayered system to the normal system with regard to heavy traffic. [ABSTRACT FROM AUTHOR]

There are several methods available in measuring food taste. The sensory evaluation, for instance, is a typical method for panels to test of taste and recognize smell with their nose by measuring the degree of taste characteristic, intensity, and pleasure. There are many issues entailed in the traditional sensory evaluation method such as forming a panel and evaluation cost; moreover, it is only localized in particular areas. Accordingly, this paper aimed to select food in one particular area, and compare and review the content between sensory evaluations using a taste biological sensor, as well as presenting an analysis of brainwaves using EEG and finally a proposal of a new method for sensory evaluation. In this paper, the researchers have conducted a sensory evaluation whereas a maximum of nine points were accumulated by purchasing eight types of rice wine. These eight types of Makgeolli were generalized by generating multidimensional data with the use of TS-5000z, thus learning mapping points and scaling them. The contribution of this paper, therefore, is to overcome the disadvantages of the sensory evaluation with the usage of the suggested taste biological sensor system. [ABSTRACT FROM AUTHOR]

The evolution of the next generation sequencing technology increases the demand for efficient solutions, in terms of space and time, for several bioinformatics problems. This paper presents a practical and easy-to-implement solution for one of these problems, namely, the all-pairs suffix-prefix problem, using a compact prefix tree. The paper demonstrates an efficient construction of this time-efficient and space-economical tree data structure. The paper presents techniques for parallel implementations of the proposed solution. Experimental evaluation indicates superior results in terms of space and time over existing solutions. Results also show that the proposed technique is highly scalable in a parallel execution environment. [ABSTRACT FROM AUTHOR]

It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1′, and p2′ are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future. [ABSTRACT FROM AUTHOR]

Due to the small aperture of blood vessel, a considerable disadvantage to current intravascular ultrasound (IVUS) imaging transducers is that their lateral imaging resolution is much lower than their axial resolution. To solve this problem, a single-element, 50 MHz, 0.6 mm diameter IVUS transducer with a geometric focus at 3 mm was proposed in this paper. The focusing transducer was based on a geometric-shaped 1-3 piezocomposite. The impedance/phase, pulse echo, acoustic intensity field, and imaging resolution of the focusing transducer were tested. For comparison, a flat IVUS transducer with the same diameter and 1-3 piezocomposite was made and tested too. Compared with their results, the fabricated focusing transducer exhibits broad bandwidth (107.21%), high sensitivity (404 mV), high axial imaging resolution (80 μm), and lateral imaging resolution (100 μm). The experimental results demonstrated that the high frequency geometric focusing piezocomposite transducer is capable of visualizing high axial and lateral resolution structure and improving the imaging quality of related interventional ultrasound imaging. [ABSTRACT FROM AUTHOR]

Purpose. This paper aims to analyze the household financial burden and poverty impacts of cancer treatment in Vietnam. Methods. Under the “ASEAN CosTs in ONcology” study design, three major specialized cancer hospitals were employed to assemble the Vietnamese data. Factors of socioeconomic, direct, and indirect costs of healthcare were collected prospectively through both individual interviews and hospital financial records. Results. The rates of catastrophic expenditure based on the cut-off points of 20%, 30%, 40%, and 50% of household’s income were 82.6%, 73.7%, 64.7%, and 56.9%, respectively. 37.4% of the households with patient were impoverished by the treatment costs for cancer. The statistically significant correlates of the impoverishment problem were higher among older patients (40–60 years: 1.77, 95% CI 1.14–2.73; above 60 years: 1.75, 95% CI 1.03–2.98); poorer patients (less than 100% national income: 29, 95% CI 18.6–45.24; less than 200% national income: 2.89, 95% CI 1.69–4.93); patients who underwent surgery alone (receiving nonsurgery treatment: 2.46, 95% CI 1.32–4.59; receiving multiple treatments: 2.4, 95% CI 1.38–4.17). Conclusions. Lots of households were pushed into poverty due to their expenditure on cancer care; more actions are urgently needed to improve financial protection to the vulnerable groups. [ABSTRACT FROM AUTHOR]

The naïve Bayes classifier (NBC) is one of the most popular classifiers for class prediction or pattern recognition from microarray gene expression data (MGED). However, it is very much sensitive to outliers with the classical estimates of the location and scale parameters. It is one of the most important drawbacks for gene expression data analysis by the classical NBC. The gene expression dataset is often contaminated by outliers due to several steps involved in the data generating process from hybridization of DNA samples to image analysis. Therefore, in this paper, an attempt is made to robustify the Gaussian NBC by the minimum β-divergence method. The role of minimum β-divergence method in this article is to produce the robust estimators for the location and scale parameters based on the training dataset and outlier detection and modification in test dataset. The performance of the proposed method depends on the tuning parameter β. It reduces to the traditional naïve Bayes classifier when β → 0. We investigated the performance of the proposed beta naïve Bayes classifier (β-NBC) in a comparison with some popular existing classifiers (NBC, KNN, SVM, and AdaBoost) using both simulated and real gene expression datasets. We observed that the proposed method improved the performance over the others in presence of outliers. Otherwise, it keeps almost equal performance. [ABSTRACT FROM AUTHOR]

As a nonintrusive method, the retina imaging provides us with a better way for the diagnosis of ophthalmologic diseases. Extracting the vessel profile automatically from the retina image is an important step in analyzing retina images. A novel hybrid active contour model is proposed to segment the fundus image automatically in this paper. It combines the signed pressure force function introduced by the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model with the local intensity property introduced by the Local Binary fitting (LBF) model to overcome the difficulty of the low contrast in segmentation process. It is more robust to the initial condition than the traditional methods and is easily implemented compared to the supervised vessel extraction methods. Proposed segmentation method was evaluated on two public datasets, DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structured Analysis of the Retina) (the average accuracy of 0.9390 with 0.7358 sensitivity and 0.9680 specificity on DRIVE datasets and average accuracy of 0.9409 with 0.7449 sensitivity and 0.9690 specificity on STARE datasets). The experimental results show that our method is effective and our method is also robust to some kinds of pathology images compared with the traditional level set methods. [ABSTRACT FROM AUTHOR]

Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum β-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition. [ABSTRACT FROM AUTHOR]

Accurate classification of hepatocellular carcinoma (HCC) image is of great importance in pathology diagnosis and treatment. This paper proposes a concave-convex variation (CCV) method to optimize three classifiers (random forest, support vector machine, and extreme learning machine) for the more accurate HCC image classification results. First, in preprocessing stage, hematoxylin-eosin (H&E) pathological images are enhanced using bilateral filter and each HCC image patch is obtained under the guidance of pathologists. Then, after extracting the complete features of each patch, a new sparse contribution (SC) feature selection model is established to select the beneficial features for each classifier. Finally, a concave-convex variation method is developed to improve the performance of classifiers. Experiments using 1260 HCC image patches demonstrate that our proposed CCV classifiers have improved greatly compared to each original classifier and CCV-random forest (CCV-RF) performs the best for HCC image recognition. [ABSTRACT FROM AUTHOR]

Essential oil of Eucalyptus globulus presents several pharmacological properties. However, their therapeutic efficacy may be affected by limitations due to several conditions, rendering it difficult to obtain stable and effective pharmaceutical formulations. The use of nanotechnology is an alternative to improve their characteristics aiming to ensure their stability and effectiveness. Furthermore, studies about the possible toxic effects of nanostructures are necessary to evaluate safety when the formulation comes into contact with human cells. Hence, in this paper, we evaluate for the first time the stability and in vitro cytogenotoxicity of nanoemulsions containing Eucalyptus globulus in peripheral blood mononuclear cells. As a result, the stability study found that the best condition for storage up to 90 days was refrigeration (4°C); it was the condition that best preserved the nanometric features. The content of the major compounds of oil was maintained after nanoencapsulation and preserved over time. In tests to evaluate the safety of this formulation, we can conclude that, at a low concentration (approximately 0.1%), Eucalyptus globulus nanoemulsion did not cause toxicity in peripheral blood mononuclear cells and also showed a protective effect in cells against possible damage when compared to oil in free form. [ABSTRACT FROM AUTHOR]

The aim of the paper is to identify the breast malignant and benign lesions using the features of apparent diffusion coefficient (ADC), perfusion fraction f, pseudodiffusion coefficient D⁎, and true diffusion coefficient D from intravoxel incoherent motion (IVIM). There are 69 malignant cases (including 9 early malignant cases) and 35 benign breast cases who underwent diffusion-weighted MRI at 3.0 T with 8 b-values (0~1000 s/mm2). ADC and IVIM parameters were determined in lesions. The early malignant cases are used as advanced malignant and benign tumors, respectively, so as to assess the effectiveness on the result. A predictive model was constructed using Support Vector Machine Binary Classification (SVMBC, also known Support Vector Machine Discriminant Analysis (SVMDA)) and Partial Least Squares Discriminant Analysis (PLSDA) and compared the difference between them both. The D value and ADC provide accurate identification of malignant lesions with b=300, if early malignant tumor was considered as advanced malignant (cancer). The classification accuracy is 93.5% for cross-validation using SVMBC with ADC and tissue diffusivity only. The sensitivity and specificity are 100% and 87.0%, respectively, r2cv=0.8163, and root mean square error of cross-validation (RMSECV) is 0.043. ADC and IVIM provide quantitative measurement of tissue diffusivity for cellularity and are helpful with the method of SVMBC, getting comprehensive and complementary information for differentiation between benign and malignant breast lesions. [ABSTRACT FROM AUTHOR]

Background. Detoxification programmes seek to implement the most secure and compassionate ways of withdrawing from opiates so that the inevitable withdrawal symptoms and other complications are minimized. Once detoxification has been achieved, the next stage is to enable the patient to overcome his or her drug addiction by ensuring consumption is permanently and completely abandoned, only after which can the subject be regarded as fully recovered. Methods. A systematic search on the common databases of relevant papers published until 2016 inclusive. Results and Conclusion. Our study of the available oral treatments for opioid dependence has revealed that no current treatment can actually claim to be fully effective. These treatments require daily oral administration and, consequently, regular visits to dispensaries, which in most cases results in a lack of patient compliance, which causes fluctuations in drug plasma levels. We then reviewed alternative treatments in the available scientific literature on polymeric sustained release formulations. Research has been done not only on release systems for detoxification but also on release systems for giving up the habit of taking opioids. These efforts have obtained the recent authorization of polymeric systems for use in patients that could help them to reduce their craving for drugs. [ABSTRACT FROM AUTHOR]

Background. To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selection (CFS) principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. Results. We adopted support vector machines (SVM) as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR), and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. Conclusions. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms. [ABSTRACT FROM AUTHOR]

The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting L1/2 constraint (L1/2 gLPCA) on error function for feature (gene) extraction. The error function based on L1/2-norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others. [ABSTRACT FROM AUTHOR]

Computer-aided detection (CAD) of lobulation can help radiologists to diagnose/detect lung diseases easily and accurately. Compared to CAD of nodule and other lung lesions, CAD of lobulation remained an unexplored problem due to very complex and varying nature of lobulation. Thus, many state-of-the-art methods could not detect successfully. Hence, we revisited classical methods with the capability of extracting undulated characteristics and designed a sliding window based framework for lobulation detection in this paper. Under the designed framework, we investigated three categories of lobulation classification algorithms: template matching, feature based classifier, and bending energy. The resultant detection algorithms were evaluated through experiments on LISS database. The experimental results show that the algorithm based on combination of global context feature and BOF encoding has best overall performance, resulting in F1 score of 0.1009. Furthermore, bending energy method is shown to be appropriate for reducing false positives. We performed bending energy method following the LIOP-LBP mixture feature, the average positive detection per image was reduced from 30 to 22, and F1 score increased to 0.0643 from 0.0599. To the best of our knowledge this is the first kind of work for direct lobulation detection and first application of bending energy to any kind of lobulation work. [ABSTRACT FROM AUTHOR]

Recently, the use of nanometer liposomes as nanocarriers in drug delivery systems mediated by nanoelectroporation has been proposed. This technique takes advantage of the possibility of simultaneously electroporating liposomes and cell membrane with 10-nanosecond pulsed electric fields (nsPEF) facilitating the release of the drug from the liposomes and at the same time its uptake by the cells. In this paper the design and characterization of a 10 nsPEF exposure system is presented, for liposomes electroporation purposes. The design and the characterization of the applicator have been carried out choosing an electroporation cuvette with 1 mm gap between the electrodes. The structure efficiency has been evaluated at different experimental conditions by changing the solution conductivity from 0.25 to 1.6 S/m. With the aim to analyze the influence of device performances on the liposomes electroporation, microdosimetric simulations have been performed considering liposomes of 200 and 400 nm of dimension with different inner and outer conductivity (from 0.05 to 1.6 S/m) in order to identify the voltage needed for their poration. [ABSTRACT FROM AUTHOR]

Perception of the adipose tissue has changed dramatically over the last few decades. Identification of adipose-derived stem cells (ASCs) ultimately transformed paradigm of this tissue from a passive energy depot into a promising stem cell source with properties of self-renewal and multipotential differentiation. As compared to bone marrow-derived stem cells (BMSCs), ASCs are more easily accessible and their isolation yields higher amount of stem cells. Therefore, the ASCs are of high interest for stem cell-based therapies and skin tissue engineering. Currently, freshly isolated stromal vascular fraction (SVF), which may be used directly without any expansion, was also assessed to be highly effective in treating skin radiation injuries, burns, or nonhealing wounds such as diabetic ulcers. In this paper, we review the characteristics of SVF and ASCs and the efficacy of their treatment for skin injuries and disorders. [ABSTRACT FROM AUTHOR]

The PET and CT fusion image, combining the anatomical and functional information, has important clinical meaning. An effective registration of PET and CT images is the basis of image fusion. This paper presents a multithread registration method based on contour point cloud for 3D whole-body PET and CT images. Firstly, a geometric feature-based segmentation (GFS) method and a dynamic threshold denoising (DTD) method are creatively proposed to preprocess CT and PET images, respectively. Next, a new automated trunk slices extraction method is presented for extracting feature point clouds. Finally, the multithread Iterative Closet Point is adopted to drive an affine transform. We compare our method with a multiresolution registration method based on Mattes Mutual Information on 13 pairs (246~286 slices per pair) of 3D whole-body PET and CT data. Experimental results demonstrate the registration effectiveness of our method with lower negative normalization correlation (NC = −0.933) on feature images and less Euclidean distance error (ED = 2.826) on landmark points, outperforming the source data (NC = −0.496, ED = 25.847) and the compared method (NC = −0.614, ED = 16.085). Moreover, our method is about ten times faster than the compared one. [ABSTRACT FROM AUTHOR]

We selected iOS in this study as the App operation system, Objective-C as the programming language, and Oracle as the database to develop an App to inspect controlled substances in patient care units. Using a web-enabled smartphone, pharmacist inspection can be performed on site and the inspection result can be directly recorded into HIS through the Internet, so human error of data translation can be minimized and the work efficiency and data processing can be improved. This system not only is fast and convenient compared to the conventional paperwork, but also provides data security and accuracy. In addition, there are several features to increase inspecting quality: (1) accuracy of drug appearance, (2) foolproof mechanism to avoid input errors or miss, (3) automatic data conversion without human judgments, (4) online alarm of expiry date, and (5) instant inspection result to show not meted items. This study has successfully turned paper-based medication inspection into inspection using a web-based mobile device. [ABSTRACT FROM AUTHOR]

With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective. [ABSTRACT FROM AUTHOR]

The inclusion of a healing chamber in dental implants has been shown to promote biological healing. In this paper, a novel numerical approach to the design of the healing chamber for additive-manufactured dental implants is proposed. This study developed an algorithm for the modeling of bone growth and employed finite element method in ANSYS to facilitate the design of healing chambers with a highly complex configuration. The model was then applied to the design of dental implants for insertion into the posterior maxillary bones. Two types of ITI® solid cylindrical screwed implant with extra rectangular-shaped healing chamber as an initial design are adopted, with which to evaluate the proposed system. This resulted in several configurations for the healing chamber, which were then evaluated based on the corresponding volume fraction of healthy surrounding bone. The best of these implants resulted in a healing chamber surrounded by around 9.2% more healthy bone than that obtained from the original design. The optimal design increased the contact area between the bone and implant by around 52.9%, which is expected to have a significant effect on osseointegration. The proposed approach is highly efficient which typically completes the optimization of each implant within 3–5 days on an ordinary personal computer. It is also sufficiently general to permit extension to various loading conditions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX). The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method. [ABSTRACT FROM AUTHOR]

Since conserved viral proteins of influenza virus, such as nucleoprotein (NP) and matrix 1 protein, are the main targets for virus-specific CD8+ cytotoxic T-lymphocytes (CTLs), we hypothesized that introduction of the NP gene of wild-type virus into the genome of vaccine reassortants could lead to better immunogenicity and afford better protection. This paper describes in vitro and in vivo preclinical studies of two new reassortants of pandemic H1N1 live attenuated influenza vaccine (LAIV) candidates. One had the hemagglutinin (HA) and neuraminidase (NA) genes from A/South Africa/3626/2013 H1N1 wild-type virus on the A/Leningrad/134/17/57 master donor virus backbone (6 : 2 formulation) while the second had the HA, NA, and NP genes of the wild-type virus on the same backbone (5 : 3 formulation). Although both LAIVs induced similar antibody immune responses, the 5 : 3 LAIV provoked greater production of virus-specific CTLs than the 6 : 2 variant. Furthermore, the 5 : 3 LAIV-induced CTLs had higher in vivo cytotoxic activity, compared to 6 : 2 LAIV. Finally, the 5 : 3 LAIV candidate afforded greater protection against infection and severe illness than the 6 : 2 LAIV. Inclusion in LAIV of the NP gene from wild-type influenza virus is a new approach to inducing cross-reactive cell-mediated immune responses and cross protection against pandemic influenza. [ABSTRACT FROM AUTHOR]

Generalized autocalibrating partially parallel acquisition (GRAPPA) has been a widely used parallel MRI technique. However, noise deteriorates the reconstructed image when reduction factor increases or even at low reduction factor for some noisy datasets. Noise, initially generated from scanner, propagates noise-related errors during fitting and interpolation procedures of GRAPPA to distort the final reconstructed image quality. The basic idea we proposed to improve GRAPPA is to remove noise from a system identification perspective. In this paper, we first analyze the GRAPPA noise problem from a noisy input-output system perspective; then, a new framework based on errors-in-variables (EIV) model is developed for analyzing noise generation mechanism in GRAPPA and designing a concrete method—instrument variables (IV) GRAPPA to remove noise. The proposed EIV framework provides possibilities that noiseless GRAPPA reconstruction could be achieved by existing methods that solve EIV problem other than IV method. Experimental results show that the proposed reconstruction algorithm can better remove the noise compared to the conventional GRAPPA, as validated with both of phantom and in vivo brain data. [ABSTRACT FROM AUTHOR]

Retinal blood vessels segmentation plays an important role for retinal image analysis. In this paper, we propose robust retinal blood vessel segmentation method based on reinforcement local descriptions. A novel line set based feature is firstly developed to capture local shape information of vessels by employing the length prior of vessels, which is robust to intensity variety. After that, local intensity feature is calculated for each pixel, and then morphological gradient feature is extracted for enhancing the local edge of smaller vessel. At last, line set based feature, local intensity feature, and morphological gradient feature are combined to obtain the reinforcement local descriptions. Compared with existing local descriptions, proposed reinforcement local description contains more local information of local shape, intensity, and edge of vessels, which is more robust. After feature extraction, SVM is trained for blood vessel segmentation. In addition, we also develop a postprocessing method based on morphological reconstruction to connect some discontinuous vessels and further obtain more accurate segmentation result. Experimental results on two public databases (DRIVE and STARE) demonstrate that proposed reinforcement local descriptions outperform the state-of-the-art method. [ABSTRACT FROM AUTHOR]

Exploring the genetic structure of influenza viruses attracts the attention in the field of molecular ecology and medical genetics, whose epidemics cause morbidity and mortality worldwide. The rapid variations in RNA strand and changes of protein structure of the virus result in low-accuracy subtyping identification and make it difficult to develop effective drugs and vaccine. This paper constructs the evolutionary structure of avian influenza virus system considering both hemagglutinin and neuraminidase protein fragments. An optimization model was established to determine the rational granularity of the virus system for exploring the intrinsic relationship among the subtypes based on the fuzzy hierarchical evaluation index. Thus, an algorithm was presented to extract the rational structure. Furthermore, to reduce the systematic and computational complexity, the granular signatures of virus system were identified based on the coarse-grained idea and then its performance was evaluated through a designed classifier. The results showed that the obtained virus signatures could approximate and reflect the whole avian influenza virus system, indicating that the proposed method could identify the effective virus signatures. Once a new molecular virus is detected, it is efficient to identify the homologous virus hierarchically. [ABSTRACT FROM AUTHOR]

Purpose. Atherosclerosis is a diseased condition of blood vessel. It causes partial blockage in lumen of vessel and affects hemodynamic of localized flowing blood. Complex geometries like region of bifurcation also affects hemodynamic to a larger extent. Complexity further increases in presence of stenoses at region of bifurcation. Such morphological change in vessel largely affects parent as well as corresponding sister and daughter vessels. In this paper, complexity in hemodynamic of blood in pair of carotid arteries (left and right carotid arteries) is evaluated in presence of stenoses at basilar segment of right artery in three-dimensional domain using reconstructed tomographic images of patient. Methods. Transient information of blood flow is obtained using four-dimensional phase-contrast MRI technique. Haematocrit component of blood at diseased condition is considered using Power Law and Quemada model. Numerical techniques are used to solve pressure-coupled governing equations of flowing blood. Results. Dysfunctions of endothelial cells near the wall are characterised by evaluating shear stress markers. Wall shear stress and its gradient based and harmonic based descriptors are calculated over complete geometry during one cardiac cycle. Conclusion. Internal branch of left carotid artery and external branch of right carotid artery are found prone to secondary stenoses in presence of primary stenoses at basilar segment of right carotid artery. [ABSTRACT FROM AUTHOR]

Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work. [ABSTRACT FROM AUTHOR]

Tumors are a serious threat to human health. The oncolytic virus is a kind of tumor killer virus which can infect and lyse cancer cells and spread through the tumor, while leaving normal cells largely unharmed. Mathematical models can help us to understand the tumor-virus dynamics and find better treatment strategies. This paper gives a new mathematical model of tumor therapy with oncolytic virus and MEK inhibitor. Stable analysis was given. Because mitogen-activated protein kinase (MEK) can not only lead to greater oncolytic virus infection into cancer cells, but also limit the replication of the virus, in order to provide the best dosage of MEK inhibitors and balance the positive and negative effect of the inhibitors, we put forward an optimal control problem of the inhibitor. The optimal strategies are given by theory and simulation. [ABSTRACT FROM AUTHOR]

The movement information of the human body can be recorded in the plantar pressure data, and the analysis of plantar pressure data can be used to judge whether the human body motion function is normal or not. A two-meter footscan® system was used to collect the plantar pressure data, and the kinetic and dynamic gait characteristics were extracted. According to the different description of gait characteristics, a set of models was established according to various people to present the movement of lower limbs. By the introduction of algorithm in machine learning, the FCM clustering algorithm is used to cluster the sample set and create a set of models, and then the SVM algorithm was used to identify the new samples, so as to complete the normal and abnormal motion function identification. The multimodel presented in this paper was carried out into the analysis of the anterior cruciate ligament deficiency. This method demonstrated being effective and can provide auxiliary analysis for clinical diagnosis. [ABSTRACT FROM AUTHOR]

Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means L1 minimization may not produce the sparsest solution. In this paper, we propose a novel reconstruction method by minimization of the difference of L1 and L2 norms. To solve the nonconvex L1-2 minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an L1 minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. We investigated the performance of the proposed method with both simulated data and in vivo experimental data. The results demonstrate that the DCA for L1-2 minimization outperforms the representative algorithms for L1, L2, L1/2, and L0 when the system matrix is highly coherent. [ABSTRACT FROM AUTHOR]

Although heart researches and acquirement of clinical and experimental data are progressively open to public use, cardiac biophysical functions are still not well understood. Due to the complex and fine structures of the heart, cardiac electrophysiological features of interest may be occluded when there is a necessity to demonstrate cardiac electrophysiological behaviors. To investigate cardiac abnormal electrophysiological features under the pathological condition, in this paper, we implement a human cardiac ischemic model and acquire the electrophysiological data of excitation propagation. A visualization framework is then proposed which integrates a novel depth weighted optic attenuation model into the pathological electrophysiological model. The hidden feature of interest in pathological tissue can be revealed from sophisticated overlapping biophysical information. Experiment results verify the effectiveness of the proposed method for intuitively exploring and inspecting cardiac electrophysiological activities, which is fundamental in analyzing and explaining biophysical mechanisms of cardiac functions for doctors and medical staff. [ABSTRACT FROM AUTHOR]

Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data. [ABSTRACT FROM AUTHOR]

Nicotinamide N-methyltransferase (NNMT) catalyzes the methylation of nicotinamide. Our previous works indicate that NNMT is involved in the body mass index and energy metabolism, and recently the association between a SNP (rs694539) of NNMT and a variety of cardiovascular diseases was reported. At present, more than 200 NNMT single nucleotide polymorphisms (SNPs) have been identified in the databases of the human genome projects; however, the association between rs694539 variation and hyperlipidemia has not been reported yet, and whether there are any SNPs in NNMT significantly associated with hyperlipidemia is still unclear. In this paper, we selected 19 SNPs in NNMT as the tagSNPs using Haploview software (Haploview 4.2) first and then performed a case-control study to observe the association between these tagSNPs and hyperlipidemia and finally applied physiological approaches to explore the possible mechanisms through which the NNMT polymorphism induces hyperlipidemia. The results show that a SNP (rs1941404) in NNMT is significantly associated with hyperlipidemia, and the influence of rs1941404 variation on the resting energy expenditure may be the possible mechanism for rs1941404 variation to induce hyperlipidemia. [ABSTRACT FROM AUTHOR]

The similarity-based retrieval of lung nodule computed tomography (CT) images is an important task in the computer-aided diagnosis of lung lesions. It can provide similar clinical cases for physicians and help them make reliable clinical diagnostic decisions. However, when handling large-scale lung images with a general-purpose computer, traditional image retrieval methods may not be efficient. In this paper, a new retrieval framework based on a hashing method for lung nodule CT images is proposed. This method can translate high-dimensional image features into a compact hash code, so the retrieval time and required memory space can be reduced greatly. Moreover, a pruning algorithm is presented to further improve the retrieval speed, and a pruning-based decision rule is presented to improve the retrieval precision. Finally, the proposed retrieval method is validated on 2,450 lung nodule CT images selected from the public Lung Image Database Consortium (LIDC) database. The experimental results show that the proposed pruning algorithm effectively reduces the retrieval time of lung nodule CT images and improves the retrieval precision. In addition, the retrieval framework is evaluated by differentiating benign and malignant nodules, and the classification accuracy can reach 86.62%, outperforming other commonly used classification methods. [ABSTRACT FROM AUTHOR]

Anatomical analysis of liver region is critical in diagnosis and treatment of liver diseases. The reports of liver region annotation are helpful for doctors to precisely evaluate liver system. One of the challenging issues is to annotate the functional regions of liver through analyzing Computed Tomography (CT) images. In this paper, we propose a vessel-tree-based liver annotation method for CT images. The first step of the proposed annotation method is to extract the liver region including vessels and tumors from the CT scans. And then a 3-dimensional thinning algorithm is applied to obtain the spatial skeleton and geometric structure of liver vessels. With the vessel skeleton, the topology of portal veins is further formulated by a directed acyclic graph with geometrical attributes. Finally, based on the topological graph, a hierarchical vascular tree is constructed to divide the liver into eight segments according to Couinaud classification theory and thereby annotate the functional regions. Abundant experimental results demonstrate that the proposed method is effective for precise liver annotation and helpful to support liver disease diagnosis. [ABSTRACT FROM AUTHOR]