Intelligent color image analysis of sintered ores for simple and rapid determination of Fe3O4 concentration.

A simple determination of the Fe3O4 concentrations of sintered ores using color images of the samples has been explored. Sintered ore is mainly composed of Fe2O3 (red), Fe3O4 (black), and other white inorganic oxides, so the color of sintered ore could be representative of the relative abundance of the constituents. Two important challenges were addressed to achieve reliable quantitative color image analysis. First, minute dents and bumps (embosses) exist on the sample surface due to inconsistent particle sizes and particle agglomeration, thereby generating dark shadows. Second, small white spots corresponding to inorganic oxide particles were spread throughout acquired images. The white spots yield very high RGB values, which would hamper the translation of the real sample color originating from the iron oxides. Therefore, the segmentations of particle agglomeration-induced shadows and white spots in the sample images were separately executed using Otsu's method and modified fuzzy C-means (MFCM), respectively. Then, color moments and derived variables from the segmented images were employed to determine Fe3O4 concentrations (6.5-10.5 wt%) using extreme gradient boosting (XGBoost). The predicted concentrations from the color analysis correlated well with reference concentrations determined using conventional titration, with a root mean square error of prediction (RMSEP) of 0.39 wt%.

Long-term Outcomes of Polytetrafluoroethylene Bicuspid Pulmonary Valve Replacement.

BACKGROUND: In 2016 we reported promising midterm outcomes of bicuspid pulmonary valve replacement using 0.1-mm polytetrafluoroethylene (PTFE) membrane. This follow-up study analyzes long-term outcomes and risk factors for reintervention and structural valve deterioration (SVD). METHODS: We performed a retrospective review of the original 119 patients who underwent PTFE bicuspid pulmonary valve replacement. Median patient age was 16.9 years (range, 0.4-57.1). Reintervention was defined as any surgical or percutaneous catheter procedure on the PTFE valve. SVD was defined as development of a peak pressure gradient ≥ 50 mm Hg or at least a moderate amount of pulmonary regurgitation on follow-up echocardiography. RESULTS: The median follow-up duration was 9.5 years. The survival rate was 96.5% at 5 and 10 years, with 2 early and 2 late mortalities. Freedom from reintervention was 90.0% at 5 years and 63.3% at 10 years. Freedom from SVD was 92.8% at 5 years and 51.1% at 10 years, with regurgitation the predominant mode (64.6%). Freedom from both reintervention and SVD at 5 and 10 years were 89.1% and 49.5%, respectively. Multivariable analysis identified smaller valve diameter (hazard ratio, 0.82; P < .001) and more than trivial pulmonary regurgitation at discharge (hazard ratio, 5.81; P < .001) as risk factors for reintervention or SVD. CONCLUSIONS: Long-term results of the PTFE bicuspid pulmonary valve replacement were acceptable. However, improvements may be needed to reduce technical error and improve durability. Smaller valve diameter and more than trivial pulmonary regurgitation at discharge were risk factors for reintervention or SVD, warranting careful follow-up for timely reintervention.

Characteristics and Treatment Outcomes of Transition among Patients with Inflammatory Bowel Disease.

PURPOSE: This study aimed to assess disease characteristics and outcomes of transition in patient care among adolescent patients with inflammatory bowel disease (IBD). MATERIALS AND METHODS: Data from patients younger than 18 years who were diagnosed with IBD (Crohn's disease, ulcerative colitis, or intestinal Behçet's disease) were investigated. We categorized the patients into two groups: transition IBD group (Group A, diagnosed in pediatric care followed by transfer to/attendance in adult IBD care) and non-transition group (Group B, diagnosed and followed up in pediatric care or adult IBD care without transfer). RESULTS: Data from a total of 242 patients [Group A (n=29, 12.0%), Group B (n=213, 88.0%)] were analyzed. A significantly higher number of patients was diagnosed at an earlier age in Group A than in Group B (p<0.001). Group A patients had more severe disease in terms of number of disease flare ups (p=0.011) and frequency of bowel-related complications (p<0.001). Multiple linear regression analysis showed that Group B patients had more medical non-compliance than Group A patients (ß=2.31, p=0.018). After transition, IBD-related admission frequency, emergency admission frequency, disease flare frequency, and medical non-compliance were significantly improved. CONCLUSION: The transition IBD group had more severe disease. Medical non-compliance was lower in the transition IBD group. Clinical outcomes improved after transition.

Feasibility of diffuser-incorporated near-infrared trans-reflectance measurement for quantitative detection of microplastics captured in perfluorocarbon.

A diffuser-incorporated near-infrared (NIR) trans-reflectance measurement is demonstrated for quantitative detection of polyethylene (PE) particles captured in perfluorohexane (PFH, C6F14). PFH effectively captures PE particles through its hydrophobicity and absorbs little NIR radiation, recommending it for use in background-free NIR detection of captured PE particles. A reflective metal disk was used to push the captured PE particles in PFH toward the bottom of the vial that contained the sample, and the trans-reflectance measurement was performed by illuminating NIR radiation from the bottom of the vial at 45o. Reproducibility is limited by the variation in the positions of small PE-particle aggregates at the water/PFH interface and the difficulty in ensuring full NIR sampling (coverage) of large aggregates. An effective way to secure improved reproducibility under these circumstances is illumination of broader and more uniform NIR radiation for measurement. For this purpose, a polytetrafluoroethylene (PTFE) disk was uniquely incorporated as a diffuser for the trans-reflectance measurement. Compared to the measurement with no diffuser, the diffuser-incorporated scheme produced more distinct PE peaks of low-quantity samples (0.1 and 0.2 mg) and enhanced the reproducibility in measurements of all the samples (0.1-4.0 mg of PE). As a result, the correlation between peak intensity and particle quantity was excellent (R2: 0.997), and a limit of detection of 0.07 mg was achieved.

Feasibility study for simple on-line Raman spectroscopic detection of microplastic particles in water using perfluorocarbon as a particle-capturing medium.

A simple Raman spectroscopic scheme for on-line detection of microplastics (MPs) in water is demonstrated. Instead of using a conventional physical filter for MP separation, perfluorohexane (PFH, C6F14) was deployed as an MP-capturing medium in this study. When PFH was added into a water-filled L-shape tube, it formed a firm droplet at the bottom of the 90° curve due to its strong hydrophobicity and high density. When a tap water sample containing dispersed polyethylene (PE) particles was flowed through the L-tube, the contained PFH droplet effectively captured the PE particles, with an average recovery of 95.9%. Next, for reliable quantitative analysis, it was necessary to measure the entire PE particle captured PFH droplet in Raman spectral acquisition without partial spectroscopic sampling. Therefore, a wide area illumination (WAI) scheme providing a laser illumination diameter of 6 mm was adopted for sampling of the whole droplet. The intensity ratios of PE and PFH peaks in the collected spectra clearly increased with elevated quantities of dispersed PE particles. When samples of PE particles were measured in sea water, which possesses much higher ionic strength than does tap water, the shapes of PE particle-captured PFH droplets did not change, and the accuracy was maintained. Based on these results, the demonstrated analytical scheme is feasible for field analysis; further study is required to strengthen its utility.

Spatially offset Raman scattering line-mapping as a potential tool for particle size analysis.

A spatially offset Raman spectroscopy (SORS) line-mapping scheme was explored as a tool for the measurement of particle size. The proposed scheme is based on the fact that photon migration in powder packing varies as a function of the reduced scattering coefficient, which is directly related to the particle size of the sample. It is known that a smaller particle yields a larger reduced scattering coefficient. Therefore, recognition of the particle size-dependent photon migration (distribution) could be a means to determine the sample's particle size and SORS is a versatile tool for this purpose. Peak intensities acquired along the SORS mapping line are expected to decrease with an increase of the offset distance and the descending slope of the peak intensity can be translated into particle size, for example, a greater slope (steeper intensity decrease) for smaller particles yielding a narrower (denser) photon distribution. For the study, low-density polyethylene (LDPE) and middle-density PE (MDPE) powders with four particle sizes were measured. In each case, the slope of intensity decrease became less steep with the increase of particle size due to the broader photon distribution. A comparative analysis of LDPE and MDPE spectra found that the slope was steeper in the measurement of MDPE powder since the photon distribution was narrower owing to the high particle density. Together, these findings suggest that the proposed scheme is potentially expandable to measure particle sizes of samples with relevant prior calibration and provide useful information on sample composition also for chemical analysis.

A Novel Low-Dose Dual-Energy Imaging Method for a Fast-Rotating Gantry-Type CT Scanner.

CT scan by use of a beam-filter placed between the x-ray source and the patient allows a single-scan low-dose dual-energy imaging with a minimal hardware modification to the existing CT systems. We have earlier demonstrated the feasibility of such imaging method with a multi-slit beam-filter reciprocating along the direction perpendicular to the CT rotation axis in a cone-beam CT system. However, such method would face mechanical challenges when the beam-filter is supposed to cooperate with a fast-rotating gantry in a diagnostic CT system. In this work, we propose a new scanning method and associated image reconstruction algorithm that can overcome these challenges. We propose to slide a beam-filter that has multi-slit structure with its slits being at a slanted angle with the CT gantry rotation axis during a scan. A streaky pattern would show up in the sinogram domain as a result. Using a notch filter in the Fourier domain of the sinogram, we removed the streaks and reconstructed an image by use of the filtered-backprojection algorithm. The remaining image artifacts were suppressed by applying l0 norm based smoothing. Using this image as a prior, we have reconstructed low- and high-energy CT images in the iterative reconstruction framework. An image-based material decomposition then followed. We conducted a simulation study to test its feasibility using the XCAT phantom and also an experimental study using the Catphan phantom, a head phantom, an iodine-solution phantom, and a monkey in anesthesia, and showed its successful performance in image reconstruction and in material decomposition.

Data-specific mask-guided image reconstruction for diffuse optical tomography.

Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data-specific mask-guided scheme that incorporates a prior mask constraint into the image reconstruction framework. The prior mask was created from the DOT data itself by exploiting the multi-measurement vector formulation. We accordingly propose two methods to integrate the prior mask into the reconstruction process. First, as a soft prior by exploiting a spatially varying regularization. Second, as a hard prior by imposing a region-of-interest-limited reconstruction. Furthermore, the latter method iterates between discrete and continuous steps to update the mask and optical parameters, respectively. The proposed methods showed enhanced optical contrast accuracy, improved spatial resolution, and reduced noise level in DOT reconstructed images compared with the conventional approaches such as the modified Levenberg-Marquardt approach and the l1-regularization based sparse recovery approach.

Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography.

Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to estimate the bulk optical properties of a highly scattering medium such as biological tissue in DOT. We validated the proposed method by using experimental, as well as, simulated data. For performance assessment, we compared the results of the proposed method with those of existing approaches. The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time.

Enhancement of soft-tissue contrast in cone-beam CT using an anti-scatter grid with a sparse sampling approach.

PURPOSE: Anti-scatter grids suppress the scatter substantially thus improving image contrast in radiography. However, its active use in cone-beam CT for the purpose of improving contrast-to-noise ratio (CNR) has not been successful mainly due to the increased noise related to Poisson statistics of photons. This paper proposes a sparse-view scanning approach to address the above issue. METHOD: Compared to the conventional cone-beam CT imaging framework, the proposed method reduces the number of projections and increases exposure in each projection to enhance image quality without an additional cost of radiation dose to patients. For image reconstruction from sparse-view data, an adaptive-steepest-descent projection-onto-convex-sets (ASD POCS) algorithm regularized by total-variation (TV) minimization was adopted. Contrast and CNR with various scattering conditions were evaluated in projection domain by a simulation study using GATE. Then we evaluated contrast, resolution, and image uniformity in CT image domain with Catphan phantom. A head phantom with soft-tissue structures was also employed for demonstrating a realistic application. A virtual grid-based estimation and reduction of scatter has also been implemented for comparison with the real anti-scatter grid. RESULTS: In the projection domain evaluation, contrast and CNR enhancement was observed when using an anti-scatter grid compared to the virtual grid. In the CT image domain, the proposed method produced substantially higher contrast and CNR of the low-contrast structures with much improved image uniformity. CONCLUSION: We have shown that the proposed method can provide high-quality CBCT images particularly with an increased contrast of soft-tissue at a neutral dose for image-guidance.

Epicardial Fat Thickness and Free Fatty Acid Level are Predictors of Acute Ischemic Stroke with Atrial Fibrillation.

BACKGROUND: Increased epicardial fat is known to be associated with the presence and chronicity of atrial fibrillation (AF). Free fatty acids (FFAs) are major components of epicardial fat; however, their potential association with AF in ischemic stroke has not been investigated. We aimed to assess the performance of echocardiographic epicardial fat thickness (EFT) and plasma FFA level in identifying patients with ischemic stroke and AF. METHODS: We enrolled a total of 214 consecutive patients (mean age, 66.8 ± 12.3 years; 39.7% women) diagnosed with acute ischemic stroke between March 2011 and June 2014. The patients were divided into two groups: ischemic stroke with AF (n = 35, 16.4%) and ischemic stroke without AF (n = 179, 83.6%). RESULTS: The ischemic stroke with AF group showed significantly higher serum FFA level (1379.7 ± 717.5 vs. 757.8 ± 520.5 uEq/L, p < 0.0001) and EFT (6.5 ± 1.2 vs. 5.3 ± 1.2 mm, p < 0.001) than the group without AF. Multivariable logistic regression analysis demonstrated that age (odds ratio [OR], 1.112), serum FFA level (OR, 1.002), and EFT (OR, 1.740) were independently associated with the ischemic stroke group with AF. EFT and FFA significantly improved the goodness-of-fit and discriminability of the simple regression model including age as a covariate (log likelihood difference, 21.35; p < 0.001; c-index difference, 17.9%; p < 0.001). CONCLUSIONS: High EFT and serum FFA level were associated with ischemic stroke in patients with AF. Echocardiographic EFT and serum FFA level can play a significant role in identifying ischemic stroke with AF.

Blunted heart rate recovery is associated with coronary artery spasm in patients with suspected vasospastic angina.

BACKGROUND: Autonomic nervous system activity has been shown to be altered in patients with vasospastic angina (VA). Heart rate recovery (HRR) is a simple, non-invasive measurement of autonomic nervous system dysfunction. We aimed to investigate whether HRR is related to VA, as established by an ergonovine test. METHODS: A total of 976 consecutive patients (47.5% male, mean age 55 years) without significant coronary artery disease who underwent both an ergonovine provocation test and a treadmill exercise test were enrolled. The relationship between VA and HRR was evaluated. RESULTS: A total of 30.7% (300/976) of patients were diagnosed with VA, as documented by the ergonovine provocation test. HRR was significantly reduced in patients with VA compared to patients without VA (24.6 ± 18.0 vs. 30.5 ± 22.2, p < 0.001), and HRR was lowest in patients with multi-vessel spasm (21.9 ± 17.3). The proportion of blunted HRR, which was defined as HRR less than 12 beats, was significantly higher in patients with VA than in those without coronary artery spasm (26.6% vs. 39.3%, p < 0.001). In multivariable regression analyses, age (odds ratio (OR) = 1.03; 95% confidence interval (CI): 1.01-1.04; p = 0.001), blunted HRR (OR = 1.71; 95% CI: 1.26-2.31; p < 0.001), current smoking status (OR = 2.11; 95% CI: 1.50-2.98; p < 0.001), and male gender (OR = 2.00; 95% CI: 1.43-2.78; p < 0.001) were significant independent predictors of VA presence. CONCLUSION: Blunted HRR was an independent predictor of VA presence, which suggests a link between coronary artery spasm and autonomic dysregulation.

Sampling scheme optimization for diffuse optical tomography based on data and image space rankings.

We present a methodology for the optimization of sampling schemes in diffuse optical tomography (DOT). The proposed method exploits singular value decomposition (SVD) of the sensitivity matrix, or weight matrix, in DOT. Two mathematical metrics are introduced to assess and determine the optimum source­detector measurement configuration in terms of data correlation and image space resolution. The key idea of the work is to weight each data measurement, or rows in the sensitivity matrix, and similarly to weight each unknown image basis, or columns in the sensitivity matrix, according to their contribution to the rank of the sensitivity matrix, respectively. The proposed metrics offer a perspective on the data sampling and provide an efficient way of optimizing the sampling schemes in DOT. We evaluated various acquisition geometries often used in DOT by use of the proposed metrics. By iteratively selecting an optimal sparse set of data measurements, we showed that one can design a DOT scanning protocol that provides essentially the same image quality at a much reduced sampling.

The Combined Impact of Neutrophil-to-Lymphocyte Ratio and Type 2 Diabetic Mellitus on Significant Coronary Artery Disease and Carotid Artery Atherosclerosis.

BACKGROUND: Neutrophil-to-lymphocyte ratio (NLR) has recently emerged as a new important inflammatory marker for predicting cardiovascular events. This study aimed to evaluate the combined impact of NLR and type 2 diabetes mellitus (T2DM) on significant coronary artery disease (CAD) and carotid artery atherosclerosis. METHODS: This study includes a total of 828 patients evaluated by coronary angiography and carotid ultrasonography. Significant CAD was defined as at least one vessel with stenosis greater than 50%. We employed logistic regression models to investigate the association of NLR and T2DM with significant CAD. The goodness-of-fit and discriminability of the models were assessed by the loglikelihood ratio test and C-index, respectively. Also, we investigated the clinical relevance of the categorized NLR that classifies patients into three risk groups (low, intermediate, high). RESULTS: According to logistic regression analysis, both NLR {adjusted odds ratio (OR) 1.31, p < 0.001} and T2DM (adjusted OR 2.46, p = 0.006) were independent risk factors of significant CAD. The addition of NLR and T2DM into a logistic regression model including conventional cardiovascular risk factors significantly improved the goodness-of-fit (p < 0.001) and the discriminability of the model (p = 0.004). Also, T2DM patients assigned into the high risk group (NLR > 2) showed the greater prevalence of significant CAD and carotid artery atherosclerosis compared with patients without T2DM or type 2 diabetic patients assigned into the low risk group (NLR ≤ 1). CONCLUSION: Our results suggest that type 2 diabetic patients with high inflammatory state would be more vulnerable to significant CAD and carotid artery atherosclerosis.

Prognostic factors for patients with bone-only metastasis in breast cancer.

PURPOSE: Bone is the most frequent site of metastasis among breast cancer patients. We investigated prognostic factors affecting survival following bone-only metastasis in breast cancer patients. MATERIALS AND METHODS: The medical records of breast cancer patients who were treated and followed at Gangnam Severance Hospital retrospectively reviewed to identify patients with bone-only metastasis. RESULTS: The median time from the diagnosis of bone-only metastasis to the last follow-up or death was 55.2 [95% confidence interval (CI), 38.6-71.9] months. The Kaplan-Meier overall survival estimate at 10 years for all patients was 34.9%. In the multivariate Cox regression model, bisphosphonate treatment [hazard ratio=0.18; 95% CI, 0.07-0.43], estrogen receptor positivity (hazard ratio=0.51; 95% CI, 0.28-0.94), and solitary bone metastasis (hazard ratio=0.32; 95% CI, 0.14-0.72) were significantly associated with longer overall survival in the bone-only recurrence group. Among the treatment modalities, only bisphosphonate treatment was identified as a significant prognostic factor. CONCLUSION: Identifying the factors influencing breast cancer mortality after bone-only metastasis will help clarify the clinical course and improve the treatment outcome for patients with breast cancer and bone-only metastasis. Bisphosphonates, as a significant prognostic factor, warrant further investigation.

The difference in prognostic factors between early recurrence and late recurrence in estrogen receptor-positive breast cancer: nodal stage differently impacts early and late recurrence.

BACKGROUND: Probability of recurrence in patients with estrogen receptor (ER)-positive breast cancer remains constant for long periods. We compared tumor burden impact on late versus early recurrence in our cohort with long-term follow-up. METHODS: Five hundred and ninety five patients diagnosed with ER-positive breast cancer between 1989 and 2001 were classified into three groups: early recurrence within 5 years, late recurrence after 5 years, and no recurrence. We identified prognostic factors among the groups using logistic regression analysis. RESULTS: At median follow-up of 11.7 years, among 595 ER-positive women, 98 (16.4%) had early recurrence and 58 (9.7%) had late recurrence. On multivariate analysis, higher nodal stage (N0 vs. N2, odds ratio [OR] 3.189; N0 vs. N3, OR 9.948), higher histologic grade (grade 1 vs. grade 2, OR 3.896; grade 1 vs. grade 3, OR 5.945), age >35 years (OR 0.295), and receiving endocrine therapy (OR 0.293) affected early recurrence. Compared to no recurrence, receiving endocrine therapy (OR 0.285) was solely related to decreased risk of late recurrence. Increased risk of early recurrence was noted with the higher nodal stage when early and no recurrences were compared. This phenomenon was not found in late recurrence. In the last comparison between the early and late recurrence, higher nodal stage (N0 vs. N3, OR 16.779) and higher histologic grade (grade 1 vs. grade 3, OR 18.111) repeatedly weighted for early recurrence. CONCLUSIONS: Nodal burden had an attenuated influence on late recurrence, which suggests that, unlike early recurrence, tumor biology might have a more important role than tumor load for late recurrence in ER-positive disease.

Personalized medicine in breast cancer: a systematic review.

The recent advent of "-omics" technologies have heralded a new era of personalized medicine. Personalized medicine is referred to as the ability to segment heterogeneous subsets of patients whose response to a therapeutic intervention within each subset is homogeneous. This new paradigm in healthcare is beginning to affect both research and clinical practice. The key to success in personalized medicine is to uncover molecular biomarkers that drive individual variability in clinical outcomes or drug responses. In this review, we begin with an overview of personalized medicine in breast cancer and illustrate the most encountered statistical approaches in the recent literature tailored for uncovering gene signatures.

Multigene expression-based predictors for sensitivity to Vorinostat and Velcade in non-small cell lung cancer.

The ability to predict the efficacy of molecularly targeted therapies for non-small cell lung cancer (NSCLC) for an individual patient remains problematic. The purpose of this study was to identify, using a refined "coexpression extrapolation (COXEN)" algorithm with a continuous spectrum of drug activity, tumor biomarkers that predict drug sensitivity and therapeutic efficacy in NSCLC to Vorinostat, a histone deacetylase inhibitor, and Velcade, a proteasome inhibitor. Using our refined COXEN algorithm, biomarker prediction models were discovered and trained for Vorinostat and Velcade based on the in vitro drug activity profiles of nine NSCLC cell lines (NCI-9). Independently, a panel of 40 NSCLC cell lines (UVA-40) were treated with Vorinostat or Velcade to obtain 50% growth inhibition values. Genome-wide expression profiles for both the NCI-9 and UVA-40 cell lines were determined using the Affymetrix HG-U133A platform. Modeling generated multigene expression signatures for Vorinostat (45-gene; P = 0.002) and Velcade (15-gene; P = 0.0002), with one overlapping gene (CFLAR). Examination of Vorinostat gene ontogeny revealed a predilection for cellular replication and death, whereas that of Velcade suggested involvement in cellular development and carcinogenesis. Multivariate regression modeling of the refined COXEN scores significantly predicted the activity of combination therapy in NSCLC cells (P = 0.007). Through the refinement of the COXEN algorithm, we provide an in silico method to generate biomarkers that predict tumor sensitivity to molecularly targeted therapies. Use of this refined COXEN method has significant implications for the a priori examination of targeted therapies to more effectively streamline subsequent clinical trial design and cost.

Multi-gene expression-based statistical approaches to predicting patients' clinical outcomes and responses.

Gene expression profiling technique now enables scientists to obtain a genome-wide picture of cellular functions on various human disease mechanisms which has also proven to be extremely valuable in forecasting patients' prognosis and therapeutic responses. A wide range of multivariate techniques have been employed in biomedical applications on such expression profiling data in order to identify expression biomarkers that are highly associated with patients' clinical outcome and to train multi-gene prediction models that can forecast various human disease outcome and drug toxicities. We provide here a brief overview on some of these approaches, succinctly summarizing relevant basic concepts, statistical algorithms, and several practical applications. We also introduce our recent in vitro molecular expression-based algorithm, the so-called COXEN technique, which uses specialized gene profile signatures as a Rosetta Stone for translating the information between two different biological systems or populations.