High Pearson Correlation Research Articles

A global Anopheles gambiae gene co-expression network constructed from hundreds of experimental conditions with missing values

BackgroundGene co-expression networks (GCNs) can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these methodologies provide results that cannot be directly compared. Thus, it is complex to analyze co-expression relations between genes, especially when there are missing values arising for experimental reasons. Networks are a helpful tool for studying gene co-expression, where nodes represent genes and edges represent co-expression of pairs of genes.ResultsIn this paper, we establish a method for constructing a gene co-expression network for the Anopheles gambiae transcriptome from 257 unique studies obtained with different methodologies and experimental designs. We introduce the sliding threshold approach to select node pairs with high Pearson correlation coefficients. The resulting network, which we name AgGCN1.0, is robust to random removal of conditions and has similar characteristics to small-world and scale-free networks. Analysis of network sub-graphs revealed that the core is largely comprised of genes that encode components of the mitochondrial respiratory chain and the ribosome, while different communities are enriched for genes involved in distinct biological processes.ConclusionAnalysis of the network reveals that both the architecture of the core sub-network and the network communities are based on gene function, supporting the power of the proposed method for GCN construction. Application of network science methodology reveals that the overall network structure is driven to maximize the integration of essential cellular functions, possibly allowing the flexibility to add novel functions.

Machine learning for accelerated prediction of the Seebeck coefficient at arbitrary carrier concentration

The convenient determination of Seebeck coefficient is a major challenge in the thermoelectric field, either for experimental metrology or theoretical prediction. Taking Heusler compounds as a prototypical class of examples, we propose a neural network model by which the Seebeck coefficient can be quickly evaluated at arbitrary carrier concentration. Using only a few elemental properties of the constituent atoms as input features, the derived model exhibits a high Pearson correlation between the real and predicted Seebeck coefficients for both the n- and p-type systems. Beyond the training data, we have checked the Seebeck coefficients of four randomly selected Heusler compounds and found strong predictive power of our model, which can be in turn utilized to screen a vast number of Heusler compounds for accelerated discovery of new materials with desired Seebeck coefficients. As an addition, we adopt the sure independence screening and sparsifying operator to obtain a three-dimensional descriptor, which is physically interpretable and shows comparable predictive power as our neural network model.

Seasonal Variability of Water Quality in the Zigi River, Northern Tanzania

Water quality parameters (colour, total suspended solids (TSS), turbidity, electrical conductivity (EC), pH, dissolved oxygen (DO), total dissolved solids (TDS), temperature, nitrate, phosphate and faecal coliform) were evaluated during wet and dry seasons in relation to human activities in Zigi River and its tributaries. Samples were taken from nine strategic sampling points located in different areas of river. The samples were processed and analysed using established procedures. Results of temperature, EC, TDS, TSS, nitrate, colour, turbidity and E. coli were higher in wet season than in dry season. Significant variations (p < 0.05) of temperature, EC, TDS, nitrates, colour and turbidity with changing seasons were observed. Also, results on variations of other parameters were more or less the same in both seasons. Temperature, EC, TDS, nitrate and E. coli were increasing and DO decreasing downstream in both seasons probably due to increased anthropogenic activities along the river. High Pearson correlation coefficient (r2 > 0.53) observed between these parameters indicated that these values are closely related. Results on assessment of water using water quality index have revealed that the river water in both seasons is unsuitable for use as drinking water and that the water is more unsuitable for use during the wet season than during the dry season. Implications of the findings on water treatment are vivid and immediate measures are recommended to minimise the further diminishing quality of the water in this river and thus reduce the costs of treating the water for domestic use

Accuracy of atrial fibrillation detection by an insertable cardiac monitor in patients undergoing catheter ablation: Results of the BioVAD study.

BackgroundInsertable cardiac monitors (ICMs) are increasingly used to evaluate the atrial fibrillation (AF) burden after catheter ablation of AF. BioMonitor III (BM3) is an ICM with a long sensing vector, which enhances sensing capabilities. The AF detection algorithm of the BM3 is based on R–R interval variability.ObjectiveTo evaluate the performance of the AF detection algorithm of BM3 in patients before and after catheter ablation of AF using simultaneous Holter recordings.MethodsIn this prospective study, we enrolled patients scheduled for catheter ablation of paroxysmal or persistent AF. After BM3 implantation, patients had a 4 days Holter registration before and 3 months after ablation. All true AF episodes ≥2 min on the Holter were annotated and matched with BM3 detected AF detections.ResultsThirty‐one patients were enrolled (mean age 60 ± 8, 74% male, 68% paroxysmal AF). Fifty‐six Holter registrations were performed in 30 patients. Twelve patients demonstrated at least one true AF episode with a total AF duration of 570 h. The AF burden accuracy of BM3 before catheter ablation was 99.6%, with a duration sensitivity of 98.6% and a duration specificity of 99.9%. The AF burden accuracy of BM3 after catheter ablation was 99.8%, with a duration sensitivity of 90.2% and a duration specificity of 99.9%. Overall, the AF burden detected on the Holter and BM3 demonstrated a high Pearson correlation coefficient of 0.996.ConclusionBM3 accurately detects AF burden in patients before and after catheter ablation of AF.

Transferability of features for neural networks links to adversarial attacks and defences.

The reason for the existence of adversarial samples is still barely understood. Here, we explore the transferability of learned features to Out-of-Distribution (OoD) classes. We do this by assessing neural networks' capability to encode the existing features, revealing an intriguing connection with adversarial attacks and defences. The principal idea is that, "if an algorithm learns rich features, such features should represent Out-of-Distribution classes as a combination of previously learned In-Distribution (ID) classes". This is because OoD classes usually share several regular features with ID classes, given that the features learned are general enough. We further introduce two metrics to assess the transferred features representing OoD classes. One is based on inter-cluster validation techniques, while the other captures the influence of a class over learned features. Experiments suggest that several adversarial defences decrease the attack accuracy of some attacks and improve the transferability-of-features as measured by our metrics. Experiments also reveal a relationship between the proposed metrics and adversarial attacks (a high Pearson correlation coefficient and low p-value). Further, statistical tests suggest that several adversarial defences, in general, significantly improve transferability. Our tests suggests that models having a higher transferability-of-features have generally higher robustness against adversarial attacks. Thus, the experiments suggest that the objectives of adversarial machine learning might be much closer to domain transfer learning, as previously thought.

Effect of Cyclosporin A and Impact of Dose Staggering on OATP1B1/1B3 Endogenous Substrates and Drug Probes for Assessing Clinical Drug Interactions.

This study was designed to assess the quantitative performance of endogenous biomarkers for organic anion transporting polypeptide (OATP) 1B1/1B3‐mediated drug‐drug interactions (DDIs). Ten healthy volunteers orally received OATP1B1/1B3 probe cocktail (0.2 mg pitavastatin, 1 mg rosuvastatin, and 2 mg valsartan) and an oral dose of cyclosporin A (CysA, 20 mg and 75 mg) separated by a 1‐hour interval (20 mg (−1 hour), and 75 mg (−1 hour)). CysA 75 mg was also given with a 3‐hour interval (75 mg (−3 hours)) to examine the persistence of OATP1B1/1B3 inhibition. The area under the plasma concentration‐time curve ratios (AUCRs) were 1.63, 3.46, and 2.38 (pitavastatin), 1.39, 2.16, and 1.81 (rosuvastatin), and 1.42, 1.77, and 1.85 (valsartan), at 20 mg, 75 mg (−1 hour) and 75 mg (−3 hours) of CysA, respectively. CysA effect on OATP1B1/1B3 was unlikely to persist at the dose examined. Among 26 putative OATP1B1/1B3 biomarkers evaluated, AUCR and maximum concentration ratio (C maxR) of CP‐I showed the highest Pearson’s correlation coefficient with CysA AUC (0.94 and 0.93, respectively). Correlation between AUCR of pitavastatin, and C maxR or AUCR of CP‐I were consistent between this study and our previous study using rifampicin as an OATP1B1/1B3 inhibitor. Nonlinear regression analysis of AUCR−1 of pitavastatin and CP‐I against CysA C max yielded K i,OATP1B1/1B3,app (109 ± 35 and 176 ± 42 nM, respectively), similar to the Ki ,OATP1B1/1B3 estimated by our physiologically‐based pharmacokinetic model analysis described previously (107 nM). The endogenous OATP1B1/1B3 biomarkers, particularly C maxR and AUCR of CP‐I, corroborates OATP1B1/1B3 inhibition and yields valuable information that improve accurate DDI predictions in drug development, and enhance our understanding of interindividual variability in the magnitude of DDIs.

Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network.

As part of our low-cost sensor network, we colocated multipollutant monitors containing sensors for particulate matter, carbon monoxide, ozone, nitrogen dioxide, and nitrogen monoxide at a reference field site in Baltimore, MD, for 1 year. The first 6 months were used for training multiple regression models, and the second 6 months were used to evaluate the models. The models produced accurate hourly concentrations for all sensors except ozone, which likely requires nonlinear methods to capture peak summer concentrations. The models for all five pollutants produced high Pearson correlation coefficients (r > 0.85), and the hourly averaged calibrated sensor and reference concentrations from the evaluation period were within 3-12%. Each sensor required a distinct set of predictors to achieve the lowest possible root-mean-square error (RMSE). All five sensors responded to environmental factors, and three sensors exhibited cross-sensitives to another air pollutant. We compared the RMSE from models (NO2, O3, and NO) that used colocated regulatory instruments and colocated sensors as predictors to address the cross-sensitivities to another gas, and the corresponding model RMSEs for the three gas models were all within 0.5 ppb. This indicates that low-cost sensor networks can yield useable data if the monitoring package is designed to comeasure key predictors. This is key for the utilization of low-cost sensors by diverse audiences since this does not require continual access to regulatory grade instruments.

Estimation of maize plant height in North China by means of backscattering coefficient and depolarization parameters using Sentinel-1 dual-pol SAR data

ABSTRACT SAR parameters have been used many times to characterize crops. However, it was found that the scattering mechanisms derived from quad-pol SAR were different from that of the dual-pol SAR data. The objective of this study is to better understand the scattering mechanism of the maize field to dual-pol (VV, VH) SAR data and propose a new radar vegetation index to improve the inversion accuracy of the crop height. We first analyzed the temporal variation of SAR parameters before the maize height stopped increasing. It showed that the sensitivity of SAR parameters to the plant height was strong in the early stage, but the correlation became weak later. Then, according to the scattering mechanism of the maize field to the dual pol (VV, VH) SAR data, we combined use of the backscattering coefficient of σ0VH and the depolarization parameter proposed the mPRVI index. In this paper, the simple linear regression model was used to fit the six parameters separately to estimation the plant height, including σ0VH (R = 0.84), VIRatio (R = 0.82), RVI (R = 0.83), DOD+RVI (R = 0.85), PRVI (R = 0.86) and mPRVI (R = 0.88), which showed a relatively high Pearson correlation coefficient (R) with the plant height. According to adjusted coefficient of determination R2 (0.70, 0.70, 0.71, 0.74, 0.76, 0.80) and RMSE (62.8 cm, 62.7 cm, 61.8 cm, 57.5 cm, 56.3 cm, 51.2 cm) for σ0VH, VIRatio, RVI, DOD+RVI, PRVI, mPRVI, respectively, the mPRVI index outperforms other parameters throughout the maize growing season. And this outperformance is not only manifested in the early growth stage of the maize, but also in the later stage before the plant height stopped increasing.

Performance evaluation of the i-Smart 300E cartridge for point-of-care electrolyte measurement in serum and plasma.

BackgroundElectrolytes are measured regularly in a variety of clinical settings because electrolyte imbalance can be life‐threatening. Although arterial blood‐gas analysis reports electrolyte levels, the result often is discrepant with results from serum and plasma samples. Since prompt and accurate measurement of serum electrolyte levels could allow early treatment, point‐of‐care (POC) electrolyte analyzers would be beneficial. We evaluated a POC electrolyte analyzer cartridge based on the Clinical and Laboratory Standard Institute (CLSI) guidelines.MethodsPrecision and linearity were assessed according to the CLSI EP05‐A3 and EP06‐A guidelines, respectively. A comparison study was conducted with both serum and plasma samples according to the CLSI EP09‐A3. For serum, results from the i‐Smart 300E analyzer were compared with results from the Nova 8 and i‐Smart 30 analyzers. For plasma, results were compared among the i‐Smart 300E, Nova 8, i‐Smart 30, and Cobas c702 analyzers.ResultsCoefficients of variation in the precision analysis were all less than 5%. Linearity assessment demonstrated a coefficient of determination between 0.999 and 1.000 for all analytes. The comparison study showed a high Pearson's correlation coefficient greater than 0.9 for all analytes, instruments, and specimens.ConclusionsThe i‐Smart 300E demonstrated good analytical performance. Its use could be beneficial in terms of both efficiency and clinical outcome in point‐of‐care testing (POCT) for electrolyte levels from serum and plasma samples.

A novel hybrid methodology for computing semantic similarity between sentences through various word senses

In the area of natural language processing, measuring sentence similarity is an essential problem. Searching for semantic meaning in natural language is a related issue. The task of measuring sentence similarity is to find semantic symmetry in two sentences, not matter how they are arranged. It is important to measure the similarity of sentences accurately. To compute the similarity between sentences, existing methods have been constructed from approaches for large texts. Since these methods work in very high-dimensional spaces, they are inefficient, require human input, and are not flexible enough for some applications. In this study, we propose a hybrid method (HydMethod) which considers not only semantic information including lexical databases, word embeddings, and corpus statistics, but also implied word order information. With lexical databases, our method models human common sense knowledge, and that knowledge can then be adapted to be used in different domains with the incorporation of corpus statistics. Therefore, the methodology is applicable across several domains. As part of our experiments, we used two standard datasets - Pilot Short Text Semantic Similarity Benchmark and MS paraphrase - in order to demonstrate the efficacy of our proposed method. As a result, the proposed method outperforms the existing approaches when tested on these two datasets, giving the highest correlation value for both word and sentence similarity. Moreover, it achieves a maximum of 32% higher increase than only using word vector or WorldNet based methodology. With Rubenstein and Goodenough word & sentence pairs, our algorithm's similarity measure shows a high Pearson correlation coefficient of 0.8953.

Comparison of Enzyme-Linked Lectin Sorbent Assay and Flow Cytometry for Profiling Microbial Glycans.

The surface of microorganisms is covered with carbohydrates, which makes them unique, self-sustaining glycan probes. Lectins are able to bind to these probes, and this interaction can be exploited for selecting microorganisms or novel lectins. To examine lectin-microorganism interactions, we have previously developed an enzyme-linked lectin sorbent assay (ELLSA) with whole bacterial cells. To further test the validity of this methodology, here we compare it with flow cytometry. For this purpose, we used biotinylated recombinantly produced lectin from Musa acuminata (BanLec), this lectin's recombinantly produced chimera with green fluorescent protein (BanLec-eGFP) and a lectin from Ricinus communis (RCA120), both biotinylated and FITC labeled. Parallel testing showed equivalent results for the two methods, in terms of the presence or absence of binding, with signal intensity yielding high Pearson correlation coefficient of 0.8 for BanLec and 0.95 for RCA120. The ELLSA method demonstrated multiple advantages, such as reliability and convenience for high-throughput analysis; it also required less lectin and yielded more consistent results. As such, ELLSA proved to be a useful tool for profiling microbial glycan structures or testing novel lectins.

Climatology and modeling of ionospheric irregularities over Greenland based on empirical orthogonal function method

This paper addresses the long-term climatology (over two solar cycles) of total electron content (TEC) irregularities from a polar cap station (Thule) using the rate of change of the TEC index (ROTI). The climatology reveals variabilities over different time scales, i.e., solar cycle, seasonal, and diurnal variations. These variations in different time scales can be explained by different drivers/contributors. The solar activity (represented by the solar radiation index F10.7P) dominates the longest time scale variations. The seasonal variations are controlled by the interplay of the energy input into the polar cap ionosphere and the solar illumination that damps the amplitude of ionospheric irregularities. The diurnal variations (with respect to local time) are controlled by the relative location of the station with respect to the auroral oval. We further decompose the climatology of ionospheric irregularities using the empirical orthogonal function (EOF) method. The first four EOFs could reflect the majority (99.49%) of the total data variability. A climatological model of ionospheric irregularities is developed by fitting the EOF coefficients using three geophysical proxies (namely, F10.7P, Bt, and Dst). The data-model comparison shows satisfactory results with a high Pearson correlation coefficient and adequate errors. Additionally, we modeled the historical ROTI during the modern grand maximum dating back to 1965 and made the prediction during solar cycle 25. In such a way, we can directly compare the climatic variations of the ROTI activity across six solar cycles.

Music emotion recognition method based on multi feature fusion

There are some problems in music emotion recognition, such as large root mean square error of recognition results and low Pearson correlation coefficient. The music signal is divided into frames by window function, the noise in the music signal is reduced by the time domain endpoint detection, and the music signal is preprocessed. The characteristics of pitch change, gene rise and fall, speech speed and gene slope were extracted by Mehr frequency cepstrum coefficient. According to the extracted music emotion features, the multi-feature fusion kernel function is constructed. Based on the fusion results, the multi-level SVM emotion recognition model is built with the support vector mechanism to realise music emotion recognition. Experimental results show that the root mean square error of the proposed method is always within the range of 0.02, and the highest Pearson correlation coefficient is about 0.9.

A Hybrid Islanding Detection Method Based on Pearson Correlation Coefficient

Renewable Energy Resources (RERs) have become essential components in modern electrical power systems. However, one of the significant problems in grid-connected microgrid is unintentional islanding, which can result into outages on the main grid power supply in cases of faults or other uncertainties in the main network. This problem poses a danger to maintenance workers can cause damage to customers and microgrid equipment. Therefore, effective islanding detection methods are necessary to improve the operation of microgrids mitigate and tackle these effects. Previously hybrid proposed islanding detection methods such as the modified reactive power control have only focused on inverter-based sources. This paper proposes a hybrid technique for islanding detection with zero non-detection zone and low impact on power quality. A novel voltage fluctuation injection method based on Pearson correlation coefficient is proposed for islanding detection that involves monitoring the rate of change of Point of Common Coupling (PCC) voltage and switching a high impedance. The Pearson correlation coefficient between the three-phase PCC voltage and the three-phase voltage of switched impedance is used as the Islanding Detection (ID) index. The proposed method is implemented in a modified IEEE 13-bus test system using MATLAB Simulink. The normally operated grid-connected microgrid had the highest Pearson correlation coefficient. In contrast, the islanding operation had the lowest Pearson correlation coefficient. The method can thus successfully differentiate between islanding and non-islanding events, and islanding can be detected in less than 0.20 s. The proposed method is compared with the islanding detection method based-modified Reactive Power Control (RPC). The proposed method can differentiate between islanding and non-islanding successfully. In contrast, some non-islanding events are incorrectly detected by the RPC-based method.

Collaborative boundary-aware context encoding networks for error map prediction

Accurately assessing the medical image segmentation quality of the automatically generated predictions is essential for guaranteeing the reliability of the results of computer-assisted diagnosis (CAD). Many researchers have studied segmentation quality estimation without labeled ground truths. Recently, a novel idea is proposed, which transforms segmentation quality assessment (SQA) into the pixel-wise or voxel-wise error map segmentation task. However, the simple application of vanilla segmentation structures in medical domain fails to achieve satisfactory error segmentation results. In this paper, we propose collaborative boundary-aware context encoding networks called EP-Net for error segmentation task. Specifically, we propose a collaborative feature transformation branch for better feature fusion between images and masks, and precise localization of error regions. Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks. Extensive experiments on IBSR V2.0 dataset, ACDC dataset and M&Ms dataset demonstrate that EP-Net achieves better error segmentation results compared with the traditional segmentation patterns. Based on error prediction results, we obtain a proxy metric of segmentation quality, which has high Pearson correlation coefficient with the real segmentation accuracy on all datasets.

Psychometric Properties of Persian Version of Short Schema Mode Inventory.

To assess the psychometric properties of the Persian version of the short Schema Mode Inventory (SMI). The short SMI was translated into Persian by three clinical psychology professors and then back-translated into English by two professors in English language. Between 2017 and 2018, patients from Iran Psychiatric Hospital and Rasoul Akram Hospital who were diagnosed with personality disorder in Axis II by a psychiatrist and had minimum education of middle school were included. Controls included students and staff of the Iran Medical Sciences University who had minimum education of middle school. All participants were asked to complete the short SMI and the Young Schema Questionnaire - Short Form (YSQ-SF). Internal consistency (Cronbach's alpha), test-retest reliability, confirmatory factor analysis, internal correlation of schema mode subscales, and correlation between short SMI and YSQ-SF were assessed. Of 406 participants, 205 (50.7%) were patients and 201 (49.3%) were controls. The fitness indices indicated that the 14-factor model was reliable, with χ2 = 12917.97, p < 0.001, df = 5795, χ2/df = 2.23, CFI = 0.96, NNFI = 0.96 SRMR = 0.08, and RMSEA = 0.05. The internal consistency of the short SMI was satisfactory (M = 0.94). Among 34 participants in the control group who completed the short SMI again after 2 weeks, test-retest reliability was high (Pearson correlation coefficient = 0.88, p < 0.001). The short SMI and YSQ-SF correlated strongly in terms of the overall scale and most subscales. The patient and control groups differed significantly in most subscales. Psychometric properties of the Persian version of the short SMI showed good validity and reliability. It can be used in clinical and research settings.

Development of a high-throughput RT-PCR based viral infectivity assay for monitoring the stability of a replicating recombinant Lymphocytic Choriomeningitis viral vector

Traditional virus infectivity titration methods for lymphocytic choriomeningitis virus (LCMV) are laborious, time-consuming, and low-throughput (e.g., focus forming unit (FFA) assay). In this report, we developed a high-throughput reverse transcription quantitative PCR (RT-qPCR)-based virus infectivity assay for relative quantitation of a live, recombinant replicating LCMV -based viral vector (TT1). This in vitro infectivity assay demonstrated a 4-log linear range for TT1 titer quantitation. A high positive Pearson correlation coefficient value (≥ 0.80) was obtained between the RT-qPCR vs. the “gold-standard” FFU assay when comparing the stability profiles of stressed TT1 vector samples. In addition to the RT-qPCR infectivity assay, the stability of the TT1 vector upon freeze-thaw stress was investigated further with complementary viral particle characterization techniques (e.g., TEM, NTA, MFI). Correlations between viral infectivity and particle measurements during forced degradation studies were observed to be specific to the TT1 vector and its various formulations and such results facilitated the rank-ordering of formulation conditions. Overall, this infectivity RT-qPCR method showed increased sample throughput and improved assay flexibility compared to traditional viral infectivity assays. These results are discussed in the context of enabling future TT1 vector formulation development work, and potential utilization as an in-process monitoring tool during TT1 vector manufacturing.

Nuclear Magnetic Resonance (NMR) study of Palm Kernel Stearin: Effects of cooling rate on crystallization behaviour

In this study, effects of different cooling rates (0.5,3.3,4.7 and 6.9 °C/min) on the crystallization behavior of palm-kernel-stearin (PKS) were studied by low-field NMR relaxometry. According to results, solid fat content (SFC), longitudinal relaxation time (T1), second moment (M2) and degree of crystallinity (%) of the samples increased with increase in cooling rate from 0.5 to 6.9 °C/min. In contrast, transverse relaxation time (T2) demonstrated an opposite behavior with respect to T1 and decreased when the cooling rate increased. Additionally, effects of cooling rate on the changes of polymorph structures were detected by X-ray measurements. Degree of crystallinity showed high Pearson correlation values (α ≤ 0.05) with SFC (r= 0.771) and T1(r= 0.932). Changes in the crystal polymorphs could also be explained by NMR parameters to some extent as can be observed by the strong correlation between the β crystal content and T2 (r= 0.927). At the highest cooling rate, β′ crystals were the dominant polymorphic form and constituted 75(%) of the total crystals present. Results of this study suggested that NMR relaxometry could be used as a complementary tool to interpret the crystallization behavior of PKS.

Prostate deformation during hypofractionated radiotherapy: an analysis of implanted fiducial marker displacement

BackgroundTo report prostate deformation during treatment, based on an analysis of fiducial marker positional differences in a large sample.Material and methodsThis study included 144 patients treated with prostate stereotactic body radiation therapy after implantation in each of 4 gold fiducial markers (FMs), which were located and numbered consistently. The center of mass of the FMs was recorded for every pair of X-ray images taken during treatment. The distance between each pair of fiducials in the live X-ray images is calculated and compared with the respective distances as determined in the CT volume. The RBE is the difference between these distances. Mean RBE and intrafraction and interfraction RBE were evaluated. The intrafraction and intefraction RBE variability were defined as the standard deviation, respectively, of all RBE during 1 treatment fraction and of the mean daily RBE over the whole treatment course.ResultsWe analyzed 720 treatment fractions comprising 24,453 orthogonal X-ray image acquisitions. We observed a trend to higher RBE related to FM4 (apex) during treatment. The fiducial marker in the prostate apex could not be used in 16% of observations, in which RBE was > 2.5 mm. The mean RBEavg was 0.93 ± 0.39 mm (range 0.32–1.79 mm) over the 5 fractions. The RBEavg was significantly lower for the first and second fraction compared with the others (P < .001). The interfraction variability of RBEavg was 0.26 ± 0.16 mm (range 0.04–0.74 mm). The mean intrafraction variability of all FMs was 0.45 ± 0.25 mm. The highest Pearson correlation coefficient was observed between FM2 and FM3 (middle left and right prostate) (R = 0.78; P < .001). Every combination with FM4 yielded lower coefficients (range 0.66–0.71; P < .001), indicating different deformation of the prostate apex.ConclusionsIdeally, prostate deformation is generally small, but it is very sensitive to rectal and bladder filling. We observed RBE up to 11.3 mm. The overall correlation between FMs was affected by shifts of individual fiducials, indicating that the prostate is not a “rigid” organ. Systematic change of RBE average between subsequent fractions indicates a systematic change in prostate shape.

Magnetic Resonance Imaging-Based Estimation of Knee Cartilage Thickness with MATLAB

Detection of early knee osteoarthritis remains a driving force in the search for more promising quantitative assessment approaches. Apart from other conventional methods such as radiography, computed tomography, and sonography, magnetic resonance imaging has become more widely available and has made it essential to visualize the knee's entire anatomy. Biomarkers such as joint space narrowing, articular cartilage thickness, cartilage volume, cartilage surface curvature, lesion depth, and others are used to determine disease progression in non-invasive manner. In this research, a regional cartilage normal thickness approximation (RCN-ta) model was developed with MATLAB to enable rapid cartilage thickness assessment with a simple click. The model formulated was compared to the FDA-cleared software measurements. A reasonable range of 0.135-0.214 mm of root-mean-square error may be predicted from the model. With a high ICC &gt; 0.975, the model was highly accurate and reproducible. A good agreement between the proposed model and the medically used software can be found with a high Pearson correlation of r &gt; 0.90.