Anomaly detection in IoT-based healthcare: machine learning for enhanced security

Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced representation of classes is used in generating a non-biased supervised (Random Forest, Adaptive Boosting, Logistic Regression, Perceptron, Deep Neural Network) machine learning models. These models are analyzed further by eliminating highly correlated features, reducing dimensionality, minimizing overfitting, and speeding up training times. Random Forest was found to perform optimally across binary and multiclass classification of IoT Attacks with an approximate accuracy of 99.55% under both reduced and all feature space. This improvement was complimented by a reduction in computational response time which is essential for real-time attack detection and response.

Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning

Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology. In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost), to predict the tribological properties of polytetrafluoroethylene (PTFE) composites under high-speed and high-temperature conditions. Firstly, PTFE composites were successfully prepared, and tribological properties under different temperature, speed, and load conditions were studied in order to explore wear mechanisms. Then, the investigation focused on establishing correlations between the friction and wear of PTFE composites by testing these parameters through the prediction of the friction coefficient and wear rate. Importantly, the correlation results illustrated that the friction coefficient and wear rate gradually decreased with the increase in speed, which was also proven by the correlation coefficient. In addition, the GBR model could effectively predict the tribological properties of the PTFE composites. Furthermore, an analysis of relative importance revealed that both load and speed exerted a greater influence on the prediction of the friction coefficient and wear rate.

Waste-to-energy incineration site selection framework based on heterogeneous fuzzy regret-PROMETHEE model considering life-cycle carbon emissions

Waste incineration technology has received extensive attention for its advantages of being harmless, reducing, and recycling. However, the waste-to-energy incineration project confronts significant "not-in-my-backyard (NIMBY) concerns," and irrational location choices will have negative effects on the project's economy and sustainability; it is also a great challenge to the credibility of the government. To this end, a multi-criteria decision-making framework is constructed for the site selection of waste-to-energy incineration projects. To begin with, a site selection criteria system is established including 16 sub-criteria from four aspects, where probabilistic linguistic term sets are introduced to depict the qualitative sub-criteria and probabilistic hesitant fuzzy sets are employed to express the uncertainty of quantitative sub-criteria. An optimization model is then built to determine the weights of criteria based on the Pearson correlation coefficient and least square method. Furthermore, a regret-preference ranking organization methods for enrichment evaluations (PROMETHEE) model is presented to rank alternatives in a heterogeneous decision environment. Finally, a case study in China is conducted to validate the applicability of the proposed framework; the result of the site selection demonstrates that alternative A2 located in Miyun, Beijing, is the most optimal option. This work provides investors with scientific decision reference and also extends the methods in the decision-making field.

Hot spot analysis and evaluation of influencing factors on regional road crash safety and severity indices: insights from Iran

Inadequate regional road safety studies have been conducted in developing countries like Iran. Regarding regional road safety indices (RSIs), a significant disparity between Iranian provinces was observed. Thus, it was aimed to evaluate the regional RSIs in Iran and identify their influencing factors and potential hot spots. Data on regional road crashes, fatalities, demographics, transportation, health institutions, economics, education, and fuel consumption rates were collected. The association between the variables was evaluated using correlation analysis. Using Moran's I and local Moran indices, provinces' spatial distributions were evaluated. Hot spot analysis was used to identify factors influencing RSIs. Significant correlations between the variables were detected. A vast local cluster in terms of fatality per injury (as a crash severity index) was identified in the country's southeast. The distribution patterns of provinces in terms of seven RSIs were cluster-like. Variable groups, including road length, demographic, income, education, and geographic, influence RSIs in hot or cold spot regions. Crashes were severe in underdeveloped and remote provinces. Increasing income and education levels make it possible to reduce crash severity indices in this country. A positive Moran's I index does not guarantee the existence of significant local cluster cores in a country.

Analysis of Return-to-Zero Error after the First Load of Load Cell

The return-to-zero error of the resistance strain load cell is most obvious in the first zero-return process during loading and unloading. To improve the accuracy of the load cell, it is necessary to figure out the cause of the error. The influence of the temperature, material, and weld cup were analyzed in this paper. It was concluded that the hysteresis is the main factor affecting the return-to-zero error after the first load. The relationship between hysteresis and zero-return error after first load was obtained by a data fitting algorithm. A method to improve the return-to-zero error after the first load was proposed.

EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population

BACKGROUND

Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database.

METHOD

The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient's embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model.

RESULTS

Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively.

CONCLUSION

The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient's embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide.

Differential Confocal Optical Probes with Optimized Detection Efficiency and Pearson Correlation Coefficient Strategy Based on the Peak-Clustering Algorithm

Quantifying free-form surfaces using differential confocal microscopy can be challenging, as it requires balancing accuracy and efficiency. When the axial scanning mechanism involves sloshing and the measured surface has a finite slope, traditional linear fitting can introduce significant errors. This study introduces a compensation strategy based on Pearson's correlation coefficient to effectively reduce measurement errors. Additionally, a fast-matching algorithm based on peak clustering was proposed to meet real-time requirements for non-contact probes. To validate the effectiveness of the compensation strategy and matching algorithm, detailed simulations and physical experiments were conducted. The results showed that for a numerical aperture of 0.4 and a depth of slope < 12°, the measurement error was <10 nm, improving the speed of the traditional algorithm system by 83.37%. Furthermore, repeatability and anti-disturbance experiments demonstrated that the proposed compensation strategy is simple, efficient, and robust. Overall, the proposed method has significant potential for application in the realization of high-speed measurements of free-form surfaces.

A robust and fast two-sample test of equal correlations with an application to differential co-expression

A robust and fast two-sample test for equal Pearson correlation coefficients (PCCs) is important in solving many biological problems, including, for example, analysis of differential co-expression. However, few existing methods for this test can achieve robustness against deviation from normal distributions, accuracy under small sample sizes, and computational efficiency simultaneously. Here, we propose a new method for testing differential correlation using a saddlepoint approximation of the residual bootstrap (DICOSAR). To achieve robustness, accuracy, and efficiency, DICOSAR combines the ideas underlying the pooled residual bootstrap, the signed root of a likelihood ratio statistic, and a multivariate saddlepoint approximation. Through a comprehensive simulation study and a real data analysis of gene co-expression, we demonstrate that DICOSAR is accurate and robust in controlling the type I error rate for detecting differential correlation and provides a faster alternative to the bootstrap and permutation methods. We further show that DICOSAR can also be used for testing differential correlation matrices. These results suggest that DICOSAR provides an analytical approach to facilitate rapid testing for the equality of PCCs in large-scale analysis.

Can human activities enhance the trade-off intensity of ecosystem services in arid inland river basins? Taking the Taolai River asin as an example

Driven by economic and social factors, more and more human beings intervene in nature to promote rapid economic and social development at the expense of ecosystem services (ES), which inevitably leads to the occurrence and even aggravation of ES trade-offs. Especially in the arid inland river basin is more serious. Therefore, this paper takes the Taolai River Basin as an example and uses the InVEST model to evaluate the spatial distribution of four typical ES, including carbon sequestration, oxygen release, windbreak and sand fixation, and water production, under the potential-actual states of the watershed. And use the Pearson correlation coefficient and the root mean square error (RMSE) to analyze the trade-off relationship between services from qualitative and quantitative aspects, respectively. Finally, the spatial matching types of trade-offs in the potential-actual states are discussed using Bivariate Local Indicators of Spatial Association, and the degree and scope of the impact of human activities on trade-offs are analyzed. The results show that the spatial distribution of the four ES has obvious heterogeneity in the potential-actual states, and the service volume of most services in the potential state is much higher than in the actual state. Secondly, there is a significant trade-offs relationship between Water production and Carbon sequestration and Oxygen release services under the potential state, while the actual state under the impact of human activities shows a significant synergistic relationship, which shows that human activities will not only increase the probability of trade-off will also increase the probability of synergy between ES. Finally, through the analysis of the meaning and causes of "high and low space dislocation" and "low and high space dislocation", it is shown that human activities will not only increase but also weaken the trade-off intensity of ES. The results of this study can provide a certain scientific basis for regional ecological environment planning and promote regional people to share ecological well-being.

Construction and analysis of functional brain network based on emotional electroencephalogram

Networks play an important role in studying structure or functional connection of various brain areas, and explaining mechanism of emotion. However, there is a lack of comprehensive analysis among different construction methods nowadays. Therefore, this paper studies the impact of different emotions on connection of functional brain networks (FBNs) based on electroencephalogram (EEG). Firstly, we defined electrode node as brain area of vicinity of electrode to construct 32-node small-scale FBN. Pearson correlation coefficient (PCC) was used to construct correlation-based FBNs. Phase locking value (PLV) and phase synchronization index (PSI) were utilized to construct synchrony-based FBNs. Next, global properties and effects of emotion of different networks were compared. The difference of synchrony-based FBN concentrates in alpha band, and the number of differences is less than that of correlation-based FBN. Node properties of different small-scale FBNs have significant differences, offering a new basis for feature extraction of recognition regions in emotional FBNs. Later, we made partition of electrode nodes and 10 new brain areas were defined as regional nodes to construct 10-node large-scale FBN. Results show the impact of emotion on network clusters on the right forehead, and high valence enhances information processing efficiency of FBN by promoting connections in brain areas.

Applying correlation analysis to electrode optimization in source domain

In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.

Modeling the effect of climate change scenarios on water quality for tropical reservoirs

Impact of natural phenomena and anthropogenic activities on water quality is closely related with temperature increase and global warming. In this study, the effects of climate change scenarios on water quality forecasts were assessed through correlations, prediction algorithms, and water quality index (WQI) for tropical reservoirs. The expected trends for different water quality parameters were estimated for the 2030-2100 period in association with temperature trends to estimate water quality using historical data from a dam in Mexico. The WQI scenarios were obtained using algorithms supported by global models of representative concentration pathways (RCPs) adopted by the Intergovernmental Panel on Climate Change (IPCC). The RPCs were used to estimate water and air temperature values and extrapolate future WQI values for the water reservoir. The proposed algorithms were validated using historical information collected from 2012 to 2019 and four temperature variation intervals from 3.2 to 5.4 °C (worst forecast) to 0.9-2.3 °C (best forecast) were used for each trajectory using 0.1 °C increases to obtain the trend for each WQI parameter. Variations in the concentration (±30, ±70, and +100) of parameters related to anthropogenic activity (e.g., total suspended solids, fecal coliforms, and chemical oxygen demand) were simulated to obtain water quality scenarios for future health diagnosis of the reservoir. The results projected in the RCP models showed increasing WQI variation for lower temperature values (best forecast WQI = 74; worst forecast WQI = 71). This study offers a novel approach that integrates multiparametric statistical and WQI to help decision making on sustainable water resources management for tropical reservoirs impacted by climate change.

Research on Multi-Fault Diagnosis Method Based on Time Domain Features of Vibration Signals

The normal operation of the engine is of great importance for the safety of life and property, so we need to monitor and analyze the state of the engine. Most of the existing methods only diagnose the type of engine fault without further analysis of the severity of the engine fault. Additionally, the features used for fault diagnosis are not selected according to faults and do not necessarily contain more fault information. In the paper, we propose using Pearson correlation coefficients in combination with faults selects sensors and the corresponding features, and then single-fault diagnosis combined with GRU (gating recurrent unit) is performed by using the selected sensors and features. Since multi-fault diagnosis is more difficult than single-fault diagnosis, more state information is required. Therefore, the multi-fault diagnosis will directly extract the time domain features screened above from all vibration signals, stack them and send them to GRU for multi-fault diagnosis. From the experimental results we can conclude that the feature selection method combining Pearson correlation coefficient and fault state can extract effective features to diagnose the fault type and its severity. Finally, the influence factors of the model are analyzed through comparative experiments, and the results show the effectiveness of the method and the selected model parameters.

No correlation identified between the proportional size of a prolapsed intravertebral disc with disability or leg pain

AIMS

The aim of the study was to determine if there was a direct correlation between the pain and disability experienced by patients and size of their disc prolapse, measured by the disc's cross-sectional area on T2 axial MRI scans.

METHODS

Patients were asked to prospectively complete visual analogue scale (VAS) and Oswestry Disability Index (ODI) scores on the day of their MRI scan. All patients with primary disc herniation were included. Exclusion criteria included recurrent disc herniation, cauda equina syndrome, or any other associated spinal pathology. T2 weighted MRI scans were reviewed on picture archiving and communications software. The T2 axial image showing the disc protrusion with the largest cross sectional area was used for measurements. The area of the disc and canal were measured at this level. The size of the disc was measured as a percentage of the cross-sectional area of the spinal canal on the chosen image. The VAS leg pain and ODI scores were each correlated with the size of the disc using the Pearson correlation coefficient (PCC). Intraobserver reliability for MRI measurement was assessed using the interclass correlation coefficient (ICC). We assessed if the position of the disc prolapse (central, lateral recess, or foraminal) altered the symptoms described by the patient. The VAS and ODI scores from central and lateral recess disc prolapses were compared.

RESULTS

A total of 56 patients (mean age 41.1 years (22.8 to 70.3)) were included. A high degree of intraobserver reliability was observed for MRI measurement: single measure ICC was 0.99 (95% confidence interval (CI) from 0.97 to 0.99 (p < 0.001)). The PCC comparing VAS leg scores with canal occupancy for herniated disc was 0.056. The PCC comparing ODI for herniated disc was 0.070. We found 13 disc prolapses centrally and 43 lateral recess prolapses. There were no foraminal prolapses in this group. The position of the prolapse was not found to be related to the mean VAS score or ODI experienced by the patients (VAS, p = 0.251; ODI, p = 0.093).

CONCLUSION

The results of the statistical analysis show that there is no direct correlation between the size or position of the disc prolapse and a patient's symptoms. The symptoms experienced by patients should be the primary concern in deciding to perform discectomy. Cite this article: Bone Joint J 2022;104-B(6):715-720.

Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study

The irregular pressure exerted by a prosthetic socket over the residual limb is one of the major factors that cause the discomfort of amputees using artificial limbs. By deploying the wearable sensors inside the socket, the interfacial pressure distribution can be studied to find the active regions and rectify the socket design. In this case study, a clustering-based analysis method is presented to evaluate the density and layout of these sensors, which aims to reduce the local redundancy of the sensor deployment. In particular, a Self-Organizing Map (SOM) and K-means algorithm are employed to find the clustering results of the sensor data, taking the pressure measurement of a predefined sensor placement as the input. Then, one suitable clustering result is selected to detect the layout redundancy from the input area. After that, the Pearson correlation coefficient (PCC) is used as a similarity metric to guide the removal of redundant sensors and generate a new sparser layout. The Jenson-Shannon Divergence (JSD) and the mean pressure are applied as posterior validation metrics that compare the pressure features before and after sensor removal. A case study of a clinical trial with two sensor strips is used to prove the utility of the clustering-based analysis method. The sensors on the posterior and medial regions are suggested to be reduced, and the main pressure features are kept. The proposed method can help sensor designers optimize sensor configurations for intra-socket measurements and thus assist the prosthetists in improving the socket fitting.

Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient

COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.

[Parameter transfer learning based on shallow visual geometry group network and its application in motor imagery classification]

Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.

Phenolic Compounds, Vitamins C and E and Antioxidant Activity of Edible Honeysuckle Berries (Lonicera caerulea L. var. kamtschatica Pojark) in Relation to Their Origin

Honeysuckles are frost tolerant plants providing early-ripening fruits with health-promoting properties which have been used in traditional medicine in China. This study evaluates the impact of the climatic conditions of two areas on the chemical composition and antioxidant activity (AOA; by DPPH-2,2-diphenyl-1-picrylhydrazyl and photochemiluminescence assays) of eight cultivars of honeysuckle berries (Lonicera caerulea L. var. kamtschatica Pojark) of various ripening times. Expectedly, chemical composition and AOA values varied depending on the cultivars, locality and selected methods. Berries from Lednice (the area with more sunshine) showed higher average contents of total monomeric anthocyanins (TMAC; pH differential absorbance method), vitamins C and E and total phenolics (high-performance liquid chromatography). In contrast, berries from Žabčice (the area with more rain) performed higher average contents of total phenolics and flavonoids (UV/VIS spectroscopic analyses). Interestingly, fundamental amounts of chlorogenic acid were determined irrespective of the locality. Regarding TMAC and vitamin C content, early ripening Amphora from both areas has been assessed as the best cultivar; concerning the content of phenolic compounds, Fialka from both areas and Amphora from Lednice is considered as the most valuable. The obtained results may facilitate the selection of the most valuable cultivars for both producers and consumers.

Precise Prediction of Promoter Strength Based on a De Novo Synthetic Promoter Library Coupled with Machine Learning

Promoters are one of the most critical regulatory elements controlling metabolic pathways. However, the fast and accurate prediction of promoter strength remains challenging, leading to time- and labor-consuming promoter construction and characterization processes. This dilemma is caused by the lack of a big promoter library that has gradient strengths, broad dynamic ranges, and clear sequence profiles that can be used to train an artificial intelligence model of promoter strength prediction. To overcome this challenge, we constructed and characterized a mutant library of Trc promoters (P_trc) using 83 rounds of mutation-construction-screening-characterization engineering cycles. After excluding invalid mutation sites, we established a synthetic promoter library that consisted of 3665 different variants, displaying an intensity range of more than two orders of magnitude. The strongest variant was ∼69-fold stronger than the original P_trc and 1.52-fold stronger than a 1 mM isopropyl-β-d-thiogalactoside-driven P_T7 promoter, with an ∼454-fold difference between the strongest and weakest expression levels. Using this synthetic promoter library, different machine learning models were built and optimized to explore the relationships between promoter sequences and transcriptional strength. Finally, our XgBoost model exhibited optimal performance, and we utilized this approach to precisely predict the strength of artificially designed promoter sequences (R² = 0.88, mean absolute error = 0.15, and Pearson correlation coefficient = 0.94). Our work provides a powerful platform that enables the predictable tuning of promoters to achieve optimal transcriptional strength.

Response of environmental factors to attenuation of toluene in vadose zone

Contaminated groundwater migrates in reverse direction under capillary force in vadose zone, and the attenuation process of pollutant adsorption and microbial degradation changes the environment of vadose zone. In this study, the response of toluene to environmental factors during reverse migration and attenuation of toluene from aquifer to vadose zone was studied by column experiment and experimental data analysis. The changes of environmental factors, including potential of hydrogen (pH), dissolved oxygen (DO), and oxidation-reduction potential (ORP), and toluene concentration were monitored by soil column experiment under sterilized and non-sterilized conditions. The 16S rRNA molecular biological detection technology was used to quantitatively analyze the impact of microbial degradation on the environment. Finally, the correlation between environmental factors and concentration in the attenuation process of toluene in the vadose zone was quantitatively studied by Pearson Correlation Coefficient (PCC) and multivariate statistical equation. The results showed that pH was primarily affected by microbial degradation, and DO and ORP were primarily affected by both adsorption and microbial degradation. The attenuation of toluene was divided into two stages: adsorption dominated (0~26 d) and microbial degradation dominated (26~55 d). The degradation amounts of microorganisms at each position in the non-sterilized column from bottom to top were 9.37%, 55.34%, 68.64%, 75.70%, 66.03% and 42.50%. At the same time, the article proposes for the first time that there is an obvious functional relationship between environmental factors (DO, ORP, pH), time (t) and concentration (C_Toluene):C_Toluene=C₀+A100t+Bα+Cβ+D100γ, (α,β,γ are the pH, DO and ORP of capillary water, respectively; A, B, C and D are all undetermined coefficients), R² > 0.95. The results of this study may facilitate the use of simple and easy-to-obtain environmental factors to characterize the dynamic process of pollutant concentration changes.

Cortical bone thickness of the distal radius predicts the local bone mineral density

AIMS

The distal radius is a major site of osteoporotic bone loss resulting in a high risk of fragility fracture. This study evaluated the capability of a cortical index (CI) at the distal radius to predict the local bone mineral density (BMD).

METHODS

A total of 54 human cadaver forearms (ten singles, 22 pairs) (19 to 90 years) were systematically assessed by clinical radiograph (XR), dual-energy X-ray absorptiometry (DXA), CT, as well as high-resolution peripheral quantitative CT (HR-pQCT). Cortical bone thickness (CBT) of the distal radius was measured on XR and CT scans, and two cortical indices mean average (CBTavg) and gauge (CBTg) were determined. These cortical indices were compared to the BMD of the distal radius determined by DXA (areal BMD (aBMD)) and HR-pQCT (volumetric BMD (vBMD)). Pearson correlation coefficient (r) and intraclass correlation coefficient (ICC) were used to compare the results and degree of reliability.

RESULTS

The CBT could accurately be determined on XRs and highly correlated to those determined on CT scans (r = 0.87 to 0.93). The CBTavg index of the XRs significantly correlated with the BMD measured by DXA (r = 0.78) and HR-pQCT (r = 0.63), as did the CBTg index with the DXA (r = 0.55) and HR-pQCT (r = 0.64) (all p < 0.001). A high correlation of the BMD and CBT was observed between paired specimens (r = 0.79 to 0.96). The intra- and inter-rater reliability was excellent (ICC 0.79 to 0.92).

CONCLUSION

The cortical index (CBTavg) at the distal radius shows a close correlation to the local BMD. It thus can serve as an initial screening tool to estimate the local bone quality if quantitative BMD measurements are unavailable, and enhance decision-making in acute settings on fracture management or further osteoporosis screening. Cite this article: Bone Joint Res 2021;10(12):820-829.

Preoperative measures of bone mineral density from digital wrist radiographs

Aims

Assessment of bone mineral density (BMD) with dual-energy X-ray absorptiometry (DXA) is a well-established clinical technique, but it is not available in the acute trauma setting. Thus, it cannot provide a preoperative estimation of BMD to help guide the technique of fracture fixation. Alternative methods that have been suggested for assessing BMD include: 1) cortical measures, such as cortical ratios and combined cortical scores; and 2) aluminium grading systems from preoperative digital radiographs. However, limited research has been performed in this area to validate the different methods. The aim of this study was to investigate the evaluation of BMD from digital radiographs by comparing various methods against DXA scanning.

Methods

A total of 54 patients with distal radial fractures were included in the study. Each underwent posteroanterior (PA) and lateral radiographs of the injured wrist with an aluminium step wedge. Overall 27 patients underwent routine DXA scanning of the hip and lumbar spine, with 13 undergoing additional DXA scanning of the uninjured forearm. Analysis of radiographs was performed on ImageJ and Matlab with calculations of cortical measures, cortical indices, combined cortical scores, and aluminium equivalent grading.

Results

Cortical measures showed varying correlations with the forearm DXA results (range: Pearson correlation coefficient (r) = 0.343 (p = 0.251) to r = 0.521 (p = 0.068)), with none showing statistically significant correlations. Aluminium equivalent grading showed statistically significant correlations with the forearm DXA of the corresponding region of interest (p < 0.017).

Conclusion

Cortical measures, cortical indices, and combined cortical scores did not show a statistically significant correlation to forearm DXA measures. Aluminium-equivalent is an easily applicable method for estimation of BMD from digital radiographs in the preoperative setting.

Cite this article: Bone Joint Res 2021;10(12):830–839.

Analyzing WSTP trend: a new method for global warming assessment

This paper tries to introduce a time-series of temperature parameters as a potential method for studying the global warming. So, we investigated the spatial-temporal variations of warm-season temperature parameters (WSTP), including start time, end time, length of season, base value, peak time, peak value, amplitude, large integrated value, right drive, and left drive, using a database of 30 years' period in different climates of Iran. We used daily temperature data from 1989 to 2018 over Iran to extract the parameters by TIMESAT software. We studied the trend analysis of WSTP through the Mann-Kendall method. Then, we considered the Pearson correlation coefficient to calculate the correlation between WSTP and time. We assessed the trends of the slope using a simple linear regression method. Then, we compared the results of the WSTP trend analysis in climatic zones. Our results accused the hyper-arid climatic zone has the longest warm season (194.89 days a year). The warm season in this region starts earlier than other regions and increases with moderate speed (left drive, 0.19 °C day^-1). Then, it reaches a peak value (31.3 °C) earlier than the different climatic zones. On the other hand, the humid regions' warm season starts with the shortest length and ends later than the other climatic zones (112.1 and 297.5 days a year for start and end times, respectively). We detected that the trend of the start time parameter has decreased by 98.02% of the study area during the last 30 years. The base value, length, and large integrated value parameters have an increasing trend of 66.47%, 80.11%, and 92.95% in Iran. The highest correlation coefficient with time was for start time and large integrated value parameters. Hence, the start time and large integrated value parameters have almost the most negative (< - 0.5) and positive (> 5) trend slope, among other parameters, respectively. In general, these results demonstrate that the studied region has faced global warming impacts over time by increasing the warm season and thermal energy, especially in arid and hyper-arid. We highlight the necessity of planning the land use under the high natural vulnerability of the studied local, especially in this new age of global warming.

Conducting correlation analysis: important limitations and pitfalls

The correlation coefficient is a statistical measure often used in studies to show an association between variables or to look at the agreement between two methods. In this paper, we will discuss not only the basics of the correlation coefficient, such as its assumptions and how it is interpreted, but also important limitations when using the correlation coefficient, such as its assumption of a linear association and its sensitivity to the range of observations. We will also discuss why the coefficient is invalid when used to assess agreement of two methods aiming to measure a certain value, and discuss better alternatives, such as the intraclass coefficient and Bland-Altman's limits of agreement. The concepts discussed in this paper are supported with examples from literature in the field of nephrology.

Assessing the physico-chemical parameters and some metals of underground water and associated soil in the arid and semiarid regions of Tank District, Khyber Pakhtunkhwa, Pakistan

Good-quality water and food are the basic needs of humans, plants, and animals. Polluted groundwater and soil directly and indirectly affect organisms, which is the main environmental concern. In the current study, standard protocols of atomic absorption spectrometry were adopted for the investigation of selected metals (lead, chromium, and iron) in the collected groundwater and soil samples. The Pearson correlation coefficient (r) applied to groundwater and soil samples shows a positive perfect correlation among water parameters (conductivity and total dissolved solids) in all three sources. In the hand pump samples between water table (WT) and water source depth (WSD), Pearson correlation coefficient (r) value was found (r = 0.87) while between EC and TDS, it was r = 1. Similarly, in the bore hole samples between WT and WSD (r = 0.66), EC and TDS (r = 1), EC and Cr (r = 0.70), and TDS and Cr (r = 0.70), which showed weaker correlation. In the tube well samples, correlation between EC and TDS was high (r = 1). The correlation coefficient (r) values of the soil parameters in the hand pump (soil) samples between Fe and Cr (r = 0.86), in bore hole samples between Fe and Cr (r = 0.77), in tube well samples between Fe and Cr (r = 0.69), while all the other parameter correlations were found lower (r = 0.60). Between electrical conductivity and total dissolved solids, high relation has been observed between them (r = 1). Overall, results showed that in most of the studied samples, contents of the target metals were found above the allowable limit set by the World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA).

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Background

Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data.

Objective

In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction.

Methods

Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques.

Results

Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6%, although smaller amounts of training data were selected, with only 20.6 keywords on average.

Conclusions

The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction.

Tracking seasonal and monthly drought with GRACE-based terrestrial water storage assessments over major river basins in South India

Drought is a complex natural hazard that affects ecosystems and society in several ways and it is important to quantify drought at the river basin scale. Assessment of drought requires both hydrological observations and simulation models as the data are generally scarce. Therefore, we use remote sensing products to help understand drought conditions in four basins in South India. This study analysed the correlation among five drought indices for four seasons: gravity recovery and climate experiment - drought severity index (GRACE-DSI), standardized precipitation index (SPI), self-calibrated palmer drought severity index (sc_PDSI), standardized precipitation-evapotranspiration index (SPEI), and combined climatologic deviation index (CCDI) with GRACE terrestrial water storage anomalies (TWSA) using the Pearson correlation coefficient (r) from 2002 to 2016 over the Godavari, Krishna, Pennar, and Cauvery river basins. Basin scale drought events are evaluated using CCDI, GRACEDSI, sc_PDSI, SPI12, and SPEI12 at seasonal and monthly time scale. Characteristics of drought event analysis are calculated for CCDI monthly. The results showed that GRACE TWS is highly correlated with GRACE-DSI, CCDI, and sc_PDSI. Seasonally, high spatial correlations between CCDI and GRACE-DSI with GRACE TWS are evident for all the river basins. Additionally, correlation is found to exist between sc_PDSI and GRACE TWS as soil moisture content is an operating variable between them. The 12-month SPI and SPEI correlated better with GRACE TWS than the 3 and 6-month periods. Among the four basins, droughts in the Krishna Basin lasted 29 months, longer than in the rest of the basins between 2003 and 2005. Overall, CCDI and GRACE-DSI indices are found to be effective for examining and evaluating the drought conditions at the basin scale.

Spatiotemporal dynamics of temperature and precipitation with reference to COVID-19 pandemic lockdown: perspective from Indian subcontinent

ABSTRACT

This study exclusively focuses on spatial and temporal change of temperature and precipitation before and after COVID-19 lockdown and also examines the extent of their variation and the spatial relationship between them. Our main objective is to analyze the spatiotemporal changes of two climatic variables in Indian subcontinent for the period of 2015-2020. Monthly precipitation and temperature data are collected from NOAA and NASA for January to May month across the four zones (northeast, northwest, central, and peninsular zone) of India. To conduct a zone-wise statistical analysis, we have adopted statistical process control (SPC) methods like exponentially weighted moving average (EWMA) control charts, individual charts (I- Chart) to detect the shift in temperature and precipitation over the study period and Pearson correlation coefficient applied to measure the spatial association between the two variables. The findings revealed that temperature parameter has experienced a lot of positive and negative trends in the span of 6 years and detected a weak to moderate negative correlation in many parts of the country in April 2020 after 2016. This study also identified a weak negative correlation mainly in NE zone in 2020 after 2017. This research provides vital scientific contribution to the effects of monthly temperature and precipitation before and after COVID-19 pandemic lockdown.

An ensemble learning based hybrid model and framework for air pollution forecasting

As advance of economy and industry, the impact of air pollution has gradually gained attention. In order to predict air quality, there were many studies that exploited various machine learning techniques to build predictive model for pollutant concentration or air quality prediction. However, enhancing the prediction performance always is the common problem of existing studies. Traditional templates based on machine learning and deep learning methods, such as GBTR (gradient boosted tree regression), SVR (support vector machine-based regression), and LSTM (long short-term memory), are most promising approaches to address these problems. Some previous researches showed that ensemble learning technology can improve predictive performance of other domains. In order to improve the accuracy of forecasting, in this paper, we propose a hybrid model and framework to improve the forecasting accuracy of air pollution. We not only exploit stacking-based ensemble learning scheme with Pearson correlation coefficient to calculate the correlation between different machine learning models to integrate various forecasting models together, but also construct a framework based on Spark+Hadoop machine learning and TensorFlow deep learning framework to physically integrate these models to demonstrate the next 1 to 8 h' air pollution forecasting. We also conduct experiments and compare the result with GBTR, SVR, LSTM, and LSTM2 (version 2) models to demonstrate the proposed hybrid model's predictive performance. The experimental results show that the hybrid model is superior to the existing models used for predicting air pollution.

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID

In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object's radial velocities estimated by the RFID phase. The method also uses a fixed time series as "sliding time window" to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.

Weighted Mean Squared Deviation Feature Screening for Binary Features

In this study, we propose a novel model-free feature screening method for ultrahigh dimensional binary features of binary classification, called weighted mean squared deviation (WMSD). Compared to Chi-square statistic and mutual information, WMSD provides more opportunities to the binary features with probabilities near 0.5. In addition, the asymptotic properties of the proposed method are theoretically investigated under the assumption logp=o(n). The number of features is practically selected by a Pearson correlation coefficient method according to the property of power-law distribution. Lastly, an empirical study of Chinese text classification illustrates that the proposed method performs well when the dimension of selected features is relatively small.

A new method for mining information of co-expression network based on multi-cancers integrated data

Background

Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction.

Methods

In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes.

Results

We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation.

Conclusions

The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation

The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical "xDF" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

An Improved Particle Filtering Algorithm Using Different Correlation Coefficients for Nonlinear System State Estimation

Particle filtering (PF) algorithm has found an increasingly wide utilization in many fields at present, especially in nonlinear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. This article proposed an improved PF algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By simulating iteration operation in Matlab, it discovers that the proposed algorithm provides better accuracy than sequential importance resampling, Gaussian sum particle filter, and Gaussian mixture sigma-point particle filters in Gaussian mixture noise.

Identification of Vibration Events in Rotating Blades Using a Fiber Optical Tip Timing Sensor

The blade tip timing (BTT) technique has been widely used in rotation machinery for non-contact blade vibration measurements. As BTT data is under-sampled, it requires complicated algorithms to reconstruct vibration parameters. Before reconstructing the vibration parameters, the right data segment should first be extracted from the massive volumes of BTT data that include noise from blade vibration events. This step requires manual intervention, is highly dependent on the skill of the operator, and has also made it difficult to automate BTT technique applications. This article proposes an included angle distribution (IAD) correlation method between adjacent revolutions to identify blade vibration events automatically in real time. All included angles of the rotor between any two adjacent blades were accurately detected by only one fiber optical tip timing sensor. Three formulas for calculating IAD correlation were then proposed to identify three types of blade vibration events: the blades’ overall vibrations, vibration of the adjacent two blades, and vibration of a specific blade. Further, the IAD correlation method was optimized in the calculating process to reduce computation load when identifying every blade’s vibration events. The presented IAD correlation method could be used for embedded, real-time, and automatic processing applications. Experimental results showed that the proposed method could identify all vibration events in rotating blades, even small events which may be wrongly identified by skillful operators.

Contribution of phenolic compounds, ascorbic acid and vitamin E to antioxidant activity of currant (Ribes L.) and gooseberry (Ribes uva-crispa L.) fruits

Berries of four gooseberry (Ribes uva-crispa L.) cultivars of Invicta, Rixanta, Karat and Black Negus and five currant (Ribes L.) cultivars of NS 11, Focus, Ben Gairn, Otelo and Viola were evaluated as potential sources of bioactive compounds with extraordinary antioxidant activity. Their total phenolic, flavonoid and anthocyanin contents were determined in the range of 3.52-30.77 g GA.kg^-1, 2.83-17.35 g RE.kg^-1 and 0.03-186.12 mg COG.100 g^-1, respectively. Furthermore, quantification of phenolic compounds and vitamins was established by high-performance liquid chromatography-diode array detection. Flavonoids were the most abundant phenolic substances in the range of 345.0-3726.5 mg.kg^-1. Ascorbic acid and vitamin E were established in the amounts of 6.2-14.04 g.kg^-1 and 0.43-12.85 mg.kg^-1, respectively. Considering all analyzed factors and antioxidant activities determined by various methods (DPPH, ACW and ACL), red gooseberry Black Negus and black currant Otelo were the most significant cultivars.

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction method for underwater acoustic signals is proposed, which combines amplitude-aware permutation entropy (AAPE) and Pearson correlation coefficient (PCC). UPEMD is a recently proposed improved empirical mode decomposition (EMD) algorithm that alleviates the mode splitting and residual noise effects of EMD. AAPE is a tool to quantify the information content of nonlinear time series. Unlike permutation entropy (PE), AAPE can reflect the amplitude information on time series. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by UPEMD. The AAPE of each IMF is calculated. The modes are separated into high-frequency IMFs and low-frequency IMFs, and all low-frequency IMFs are determined as useful IMFs (UIMFs). Then, the PCC between the high-frequency IMF with the smallest AAPE and the original signal is calculated. If PCC is greater than the threshold, the IMF is also determined as a UIMF. Finally, all UIMFs are reconstructed and the denoised signal is obtained. Chaotic signals with different signal-to-noise ratios (SNRs) are used for denoising experiments. Compared with EMD and extreme-point symmetric mode decomposition (ESMD), the proposed method has higher SNR and smaller root mean square error (RMSE). The proposed method is applied to noise reduction of real underwater acoustic signals. The results show that the method can further eliminate noise and the chaotic attractors are smoother and clearer.

Modified Pearson correlation coefficient for two-color imaging in spherocylindrical cells

The revolution in fluorescence microscopy enables sub-diffraction-limit ("superresolution") localization of hundreds or thousands of copies of two differently labeled proteins in the same live cell. In typical experiments, fluorescence from the entire three-dimensional (3D) cell body is projected along the z-axis of the microscope to form a 2D image at the camera plane. For imaging of two different species, here denoted "red" and "green", a significant biological question is the extent to which the red and green spatial distributions are positively correlated, anti-correlated, or uncorrelated. A commonly used statistic for assessing the degree of linear correlation between two image matrices R and G is the Pearson Correlation Coefficient (PCC). PCC should vary from - 1 (perfect anti-correlation) to 0 (no linear correlation) to + 1 (perfect positive correlation). However, in the special case of spherocylindrical bacterial cells such as E. coli or B. subtilis, we show that the PCC fails both qualitatively and quantitatively. PCC returns the same + 1 value for 2D projections of distributions that are either perfectly correlated in 3D or completely uncorrelated in 3D. The PCC also systematically underestimates the degree of anti-correlation between the projections of two perfectly anti-correlated 3D distributions. The problem is that the projection of a random spatial distribution within the 3D spherocylinder is non-random in 2D, whereas PCC compares every matrix element of R or G with the constant mean value [Formula: see text] or [Formula: see text]. We propose a modified Pearson Correlation Coefficient (MPCC) that corrects this problem for spherocylindrical cell geometry by using the proper reference matrix for comparison with R and G. Correct behavior of MPCC is confirmed for a variety of numerical simulations and on experimental distributions of HU and RNA polymerase in live E. coli cells. The MPCC concept should be generalizable to other cell shapes.

Fitting an Optical Fiber Background with a Weighted Savitzky-Golay Smoothing Filter for Raman Spectroscopy

The Raman background arising from optical fiber materials poses a critical problem for fiber optic surface-enhanced Raman spectroscopy (SERS). A novel filter is developed to fit the optical fiber background from the measured SERS spectrum of the target sample. The general model of the filter is built by incorporating a weighted term of matching the similarity between the estimated background spectrum and the measured background spectrum into the classic Savitzky-Golay (SG) smoothing filter model. Through respectively selecting Euclidean cosine coefficient (ECos) and Pearson correlation coefficient (PCor) as the similarity index, two different models of the weighted SG smoothing filter are derived and named as SG-ECos and SG-PCor accordingly. Furthermore, the algorithm is presented, implemented, successfully applied to experimentally measured SERS spectra of rhodamine 6G and crystal violet, and validated with mathematically simulated Raman spectra. Experimental and simulation results show that the SG-ECos filter is effective, fast, flexible, and of certain anti-noise capability in background fitting. It is suggested that the proposed filter may be also applicable for other Raman spectra measurements to remove spectral contaminants originated from sampling substrates such as glass slides.

On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach.

Effect of frozen-storage period on quality of American sirloin and mackerel (Scomber japonicus)

This study aimed to study the effect of frozen-storage period on the quality of sirloin and mackerel (Scomber japonicus). The samples were evaluated after being kept in frozen storage at -17.9 °C for different periods of time (1, 8, 15, 22, and 29 days). The frozen storage resulted in increase in ice crystal formation on the surface of both sirloin and mackerel. Frozen-storage period had an effect on the increase in the drip loss of both sirloin and mackerel with a positive correlation (p < 0.05) as well as on the decrease in the hardness of sirloin with a negative correlation (p < 0.05). During the frozen-storage period, the 2-thiobarbituric acid reactive substance level was increased in mackerel while the level in sirloin was maintained; both levels were within safe limits. Consequently, a 29-day freezing period is postulated to have little effect on the quality of sirloin and mackerel.

Signal-to-Noise Ratio Enhancement Based on Empirical Mode Decomposition in Phase-Sensitive Optical Time Domain Reflectometry Systems

We propose a novel denoising method based on empirical mode decomposition (EMD) to improve the signal-to-noise ratio (SNR) for vibration sensing in phase-sensitive optical time domain reflectometry (φ-OTDR) systems. Raw Rayleigh backscattering traces are decomposed into a series of intrinsic mode functions (IMFs) and a residual component using an EMD algorithm. High frequency noise is eliminated by removing several IMFs at the position without vibration selected by the Pearson correlation coefficient (PCC). When the pulse width is 50 ns, the SNR of location information for the vibration events of 100 Hz and 1.2 kHz is increased to as high as 42.52 dB and 39.58 dB, respectively, with a 2 km sensing fiber, which demonstrates the excellent performance of this new method.

Geostatistical exploration of dataset assessing the heavy metal contamination in Ewekoro limestone, Southwestern Nigeria

The dataset for this article contains geostatistical analysis of heavy metals contamination from limestone samples collected from Ewekoro Formation in the eastern Dahomey basin, Ogun State Nigeria. The samples were manually collected and analysed using Microwave Plasma Atomic Absorption Spectrometer (MPAS). Analysis of the twenty different samples showed different levels of heavy metals concentration. The analysed nine elements are Arsenic, Mercury, Cadmium, Cobalt, Chromium, Nickel, Lead, Vanadium and Zinc. Descriptive statistics was used to explore the heavy metal concentrations individually. Pearson, Kendall tau and Spearman rho correlation coefficients was used to establish the relationships among the elements and the analysis of variance showed that there is a significant difference in the mean distribution of the heavy metals concentration within and between the groups of the 20 samples analysed. The dataset can provide insights into the health implications of the contaminants especially when the mean concentration levels of the heavy metals are compared with recommended regulatory limit concentration.

Extracting Optical Fiber Background from Surface-Enhanced Raman Spectroscopy Spectra Based on Bi-Objective Optimization Modeling

We propose a bi-objective optimization model for extracting optical fiber background from the measured surface-enhanced Raman spectroscopy (SERS) spectrum of the target sample in the application of fiber optic SERS. The model is built using curve fitting to resolve the SERS spectrum into several individual bands, and simultaneously matching some resolved bands with the measured background spectrum. The Pearson correlation coefficient is selected as the similarity index and its maximum value is pursued during the spectral matching process. An algorithm is proposed, programmed, and demonstrated successfully in extracting optical fiber background or fluorescence background from the measured SERS spectra of rhodamine 6G (R6G) and crystal violet (CV). The proposed model not only can be applied to remove optical fiber background or fluorescence background for SERS spectra, but also can be transferred to conventional Raman spectra recorded using fiber optic instrumentation.

Cultivars effect on the physical characteristics of rice (rough and milled) (Oryza Sativa L.) of temperate region of Kashmir (India)

The aim of present research was to evaluate physical and engineering properties of traditional paddy and rice cultivars native to temperate region of India. Length, width, thickness, equivalent diameter, surface area, aspect ratio, volume, bulk density, true density, porosity, thousand kernels weight, angle of repose and coefficient of friction were evaluated, which are required in designing of various post harvest operations and storage structures. The low bulk density of cultivars, Mushki budgi, Mushki tujan and Kaw kareed may be due to the presence of long awns possessed by these cultivars which were bulky and occupied more space. The wide variations were found in rice kernels with respect to colour, which determined the functional properties and energy requirement during polishing of these cultivars. Results indicated significant differences in the physical properties among various paddy and brown rice cultivars when compared with earlier reported results. Thousand kernel weight, width, arithmetic mean diameter and equivalent diameter showed significant positive correlations with spherecity, surface area, volume, true density, and angle of repose; but negatively correlated with bulk density. These desirable characteristics exploit agriculturists/institutions to preserve these races and encourage farmers to cultivate these cherished rice cultivars.

The Synergistic Effect of Selumetinib/Docetaxel Combination Therapy Monitored by [(18)F]FDG/[(18)F]FLT PET and Diffusion-Weighted Magnetic Resonance Imaging in a Colorectal Tumor Xenograft Model

PURPOSE

Positron emission tomography (PET) and diffusion-weighted MRI (DW-MRI) were used to characterize the treatment effects of the MEK1/2 inhibitor selumetinib (AZD6244), docetaxel, and their combination in HCT116 tumor-bearing mice on the molecular level.

PROCEDURES

Mice were treated with vehicle, selumetinib (25 mg/kg), docetaxel (15 mg/kg), or a combination of both drugs for 7 days and imaged at four time points with 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]FDG) or 3'-deoxy-3'-[(18)F]fluorothymidine ([(18)F]FLT) followed by DW-MRI to calculate the apparent diffusion coefficient (ADC). Data was cross-validated using the Pearson correlation coefficient (PCC) and compared to histology (IHC).

RESULTS

Each drug led to tumor growth inhibition but their combination resulted in regression. Separate analysis of PET or ADC could not provide significant differences between groups. Only PCC combined with IHC analysis revealed the highest therapeutic impact for combination therapy.

CONCLUSION

Combination treatment of selumetinib/docetaxel was superior to the respective mono-therapies shown by PCC of PET and ADC in conjunction with histology.

Updated Rice Kinase Database RKD 2.0: enabling transcriptome and functional analysis of rice kinase genes

BACKGROUND

Protein kinases catalyze the transfer of a phosphate moiety from a phosphate donor to the substrate molecule, thus playing critical roles in cell signaling and metabolism. Although plant genomes contain more than 1000 genes that encode kinases, knowledge is limited about the function of each of these kinases. A major obstacle that hinders progress towards kinase characterization is functional redundancy. To address this challenge, we previously developed the rice kinase database (RKD) that integrated omics-scale data within a phylogenetics context.

RESULTS

An updated version of rice kinase database (RKD) that contains metadata derived from NCBI GEO expression datasets has been developed. RKD 2.0 facilitates in-depth transcriptomic analyses of kinase-encoding genes in diverse rice tissues and in response to biotic and abiotic stresses and hormone treatments. We identified 261 kinases specifically expressed in particular tissues, 130 that are significantly up- regulated in response to biotic stress, 296 in response to abiotic stress, and 260 in response to hormones. Based on this update and Pearson correlation coefficient (PCC) analysis, we estimated that 19 out of 26 genes characterized through loss-of-function studies confer dominant functions. These were selected because they either had paralogous members with PCC values of <0.5 or had no paralog.

CONCLUSION

Compared with the previous version of RKD, RKD 2.0 enables more effective estimations of functional redundancy or dominance because it uses comprehensive expression profiles rather than individual profiles. The integrated analysis of RKD with PCC establishes a single platform for researchers to select rice kinases for functional analyses.

TROM: A Testing-Based Method for Finding Transcriptomic Similarity of Biological Samples

Comparative transcriptomics has gained increasing popularity in genomic research thanks to the development of high-throughput technologies including microarray and next-generation RNA sequencing that have generated numerous transcriptomic data. An important question is to understand the conservation and divergence of biological processes in different species. We propose a testing-based method TROM (Transcriptome Overlap Measure) for comparing transcriptomes within or between different species, and provide a different perspective, in contrast to traditional correlation analyses, about capturing transcriptomic similarity. Specifically, the TROM method focuses on identifying associated genes that capture molecular characteristics of biological samples, and subsequently comparing the biological samples by testing the overlap of their associated genes. We use simulation and real data studies to demonstrate that TROM is more powerful in identifying similar transcriptomes and more robust to stochastic gene expression noise than Pearson and Spearman correlations. We apply TROM to compare the developmental stages of six Drosophila species, C. elegans, S. purpuratus, D. rerio and mouse liver, and find interesting correspondence patterns that imply conserved gene expression programs in the development of these species. The TROM method is available as an R package on CRAN (https://cran.r-project.org/package=TROM) with manuals and source codes available at http://www.stat.ucla.edu/~jingyi.li/software-and-data/trom.html.

The effect of salinity on waste activated sludge alkaline fermentation and kinetic analysis

The effect of salinity on sludge alkaline fermentation at low temperature (20°C) was investigated, and a kinetic analysis was performed. Different doses of sodium chloride (NaCl, 0-25g/L) were added into the fermentation system. The batch-mode results showed that the soluble chemical oxygen demand (SCOD) increased with salinity. The hydrolysate (soluble protein, polysaccharide) and the acidification products (short chain fatty acids (SCFAs), NH4(+)-N, and PO4(3-)-P) increased with salinity initially, but slightly declined respectively at higher level salinity (20g/L or 20-25g/L). However, the hydrolytic acidification performance increased in the presence of salt compared to that without salt. Furthermore, the results of Haldane inhibition kinetics analysis showed that the salt enhanced the hydrolysis rate of particulate organic matter from sludge particulate and the specific utilization of hydrolysate, and decreased the specific utilization of SCFAs. Pearson correlation coefficient analysis indicated that the importance of polysaccharide on the accumulation of SCFAs was reduced with salt addition, but the importance of protein and NH4(+)-N on SCFA accumulation was increased.

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance.