Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Applied genomics for identification of virulent biothreats and for disease outbreak surveillance

Fortifying our preparedness to cope with biological threats by identifying and targeting virulence factors may be a preventative strategy for curtailing infectious disease outbreak. Virulence factors evoke successful pathogenic invasion, and the science and technology of genomics offers a way of identifying them, their agents and evolutionary ancestor. Genomics offers the possibility of deciphering if the release of a pathogen was intentional or natural by observing sequence and annotated data of the causative agent, and evidence of genetic engineering such as cloned vectors at restriction sites. However, to leverage and maximise the application of genomics to strengthen global interception system for real-time biothreat diagnostics, a complete genomic library of pathogenic and non-pathogenic agents will create a robust reference assembly that can be used to screen, characterise, track and trace new and existing strains. Encouraging ethical research sequencing pathogens found in animals and the environment, as well as creating a global space for collaboration will lead to effective global regulation and biosurveillance.

An automatic measurement system of distal femur morphological parameters using 3D slicer software

In the field of joint surgery, the computer-aided design of knee prostheses suitable for the Chinese population requires a large quantity of anatomical knee data. In this study, we propose a new method that uses 3D Slicer software to automatically measure the morphological parameters of the distal femur. First, 141 femur samples were segmented from CT data to establish the femoral shape library. Next, balanced iterative reducing and clustering using hierarchies (BIRCH) combined with iterative closest point (ICP) and generalised procrustes analysis (GPA) were used to achieve fast registration of the femur samples. The statistical model was automatically calculated from the registered femur samples, and an orthopaedic surgeon marked the points on the statistical model. Finally, we developed an automatic measurement system using 3D Slicer software, and a deformable model matching method was applied to establish the point correspondence between the statistical model and the other samples. By matching points on the statistical model to corresponding points in other samples, we measured all other samples. We marked six points and measured eight parameters. We evaluated the performance of automatic matching by comparing the points marked manually with those matched automatically and verified the accuracy of the system by comparing the manual and automatic measurement results. The results indicated that the average error of the automatic matching points was 1.03 mm, and the average length error and average angle error measured automatically by the system were 0.37 mm and 0.63°, respectively. These errors were smaller than the intra-rater and inter-rater errors measured manually by two different surgeons, which showed that the accuracy of our automatic method was high. Taken together, this study established an accurate and automatic measurement system for the distal femur based on the secondary development of 3D Slicer software to assist orthopaedic surgeons in completing the measurements of big data and further promote the improved design of Chinese-specific knee prostheses.

Image Features of Resting-State Functional Magnetic Resonance Imaging in Evaluating Poor Emotion and Sleep Quality in Patients with Chronic Pain under Artificial Intelligence Algorithm

The balanced iterative reducing and clustering using hierarchies (BIRCH) method was adopted to optimize the results of the resting-state functional magnetic resonance imaging (RS-fMRI) to analyze the changes in the brain function of patients with chronic pain accompanied by poor emotion or abnormal sleep quality in this study, so as to provide data support for the prevention and treatment of clinical chronic pain with poor emotion or sleep quality. 159 patients with chronic pain who visited the hospital were selected as the research objects, and they were grouped according to the presence or absence of abnormalities in emotion and sleep. The patients without poor emotion and sleep quality were set as the control group (60 cases), and the patients with the above symptoms were defined in the observation group (90 cases). The brain function was detected by RS-fMRI technology based on the BIRCH algorithm. The results showed that the rand index (RI), adjustment of RI (ARI), and Fowlkes–Mallows index (FMI) results in the k-means, flow cytometry (FCM), and BIRCH algorithms were 0.82, 0.71, and 0.88, respectively. The scores of Hamilton Depression Scale (HAHD), Hamilton Anxiety Scale (HAMA), and Pittsburgh Sleep Quality Index (PSQI) were 7.26 ± 3.95, 7.94 ± 3.15, and 8.03 ± 4.67 in the observation group and 4.03 ± 1.95, 5.13 ± 2.35, and 4.43 ± 2.07 in the control group; the higher proportion of RS-fMRI was with abnormal brain signal connections. A score of 7 or more meant that the number of brain abnormalities was more than 90% and that of less than 7 was less than 40%, showing a statistically obvious difference in contrast (P < 0.05). Therefore, the BIRCH clustering algorithm showed reliable value in the optimization of RS-fMRI images, and RS-fMRI showed high application value in evaluating the emotion and sleep quality of patients with chronic pain.

Endogenous growth factors and their empirical verification in the Colombian business context by applying fuzzy measurement techniques

This paper centers on the role and economic effect of the factors that affect the endogenous growth of organizations. To this end, it analyzes the case of Colombian companies, whose economy has been characterized in Latin America by maintaining a traditional economic policy and sectors that seek to maximize the opportunities offered by international markets. This study seeks to identify and analyze the factors that influence the endogenous economic growth of organizations and that allow them to be sustainable over time. For this purpose, adaptation algorithms based on fuzzy logics and supervised and unsupervised learning methods were used, generating an adaptation strategy that allows classifying and knowing the endogenous growth of companies in the Colombian context and identifying the most relevant aspects to take into account. Finally, it was found that most Colombian companies focus their business policies on strengthening human capital, followed by innovation, financial resources and, to a lesser extent, strategic alliances.