Time trajectories and within-subject correlations of matrix metalloproteinases 3, 8, 9, 10, 12, and 13 serum levels and their ability to predict mortality in polytraumatized patients: a pilot study

BACKGROUND

Managing polytrauma victims poses a significant challenge to clinicians since applying the same therapy to patients with similar injury patterns may result in different outcomes. Using serum biomarkers hopefully allows for treating each multiple injured in the best possible individual way. Since matrix metalloproteinases (MMPs) play pivotal roles in various physiological processes, they might be a reliable tool in polytrauma care.

METHODS

We evaluated 24 blunt polytrauma survivors and 12 fatalities (mean age, 44.2 years, mean ISS, 45) who were directly admitted to our Level I trauma center and stayed at the intensive care unit for at least one night. We determined their MMP3, MMP8, MMP9, MMP10, MMP12, and MMP13 serum levels at admission (day 0) and on days 1, 3, 5, 7, and 10.

RESULTS

Median MMP8, MMP9, and MMP12 levels immediately rose after the polytrauma occurred; however, they significantly decreased from admission to day 1 and significantly increased from day 1 to day 10, showing similar time trajectories and (very) strong correlations between each two of the three enzyme levels assessed at the same measurement point. For a two-day lag, autocorrelations were significant for MMP8 (- 0.512) and MMP9 (- 0.302) and for cross-correlations between MMP8 and MMP9 (- 0.439), MMP8 and MMP12 (- 0.416), and MMP9 and MMP12 (- 0.307). Moreover, median MMP3, MMP10, and MMP13 levels significantly increased from admission to day 3 and significantly decreased from day 3 to day 10, showing similar time trajectories and an (almost) strong association between every 2 levels until day 7. Significant cross-correlations were detected between MMP3 and MMP10 (0.414) and MMP13 and MMP10 (0.362). Finally, the MMP10 day 0 level was identified as a predictor for in-hospital mortality. Any increase of the MMP10 level by 200 pg/mL decreased the odds of dying by 28.5%.

CONCLUSIONS

The time trajectories of the highly varying individual MMP levels elucidate the involvement of these enzymes in the endogenous defense response following polytrauma. Similar time courses of MMP levels might indicate similar injury causes, whereas lead-lag effects reveal causative relations between several enzyme pairs. Finally, MMP10 abundantly released into circulation after polytrauma might have a protective effect against dying.

Investigating the spatiotemporal associations between meteorological conditions and air pollution in the federal state Baden-Württemberg (Germany)

When analyzing health data in relation to environmental stressors, it is crucial to identify which variables to include in the statistical model to exclude dependencies among the variables. Four meteorological parameters: temperature, ultraviolet radiation, precipitation, and vapor pressure and four outdoor air pollution parameters: ozone ( O 3 ), nitrogen dioxide ( NO 2 ), particulate matter ( P M 2.5 , P M 10 ) were studied on a daily basis for Baden-Württemberg (Germany). This federal state covers urban and rural compartments including mountainous and river areas. A temporal and spatial analysis of the internal relationships was performed among the variables using (a) cross-correlations, both on the grand ensemble of data as well as within subsets, and (b) the Local Indications of Spatial Association (LISA) method. Meteorological and air pollution variables were strongly correlated within and among themselves in time and space. We found a strong interaction between nitrogen dioxide and ozone, with correlation coefficients varying over time. The coefficients ranged from negative correlations in January (-0.84), April (-0.47), and October (-0.54) to a positive correlation in July (0.45). The cross-correlation plot showed a noticeable change in the correlation direction for O 3 and NO 2 . Spatially, NO 2 , P M 2.5 , and P M 10 concentrations were significantly higher in urban than rural regions. For O 3 , this effect was reversed. A LISA analysis confirmed distinct hot and cold spots of environmental stressors. This work examined and quantified the spatio-temporal relationship between air pollution and meteorological conditions and recommended which variables to prioritize for future health impact analyses. The results found are in line with the underlying physico-chemical atmospheric processes. It also identified postal code areas with dominant environmental stressors for further studies.

Cross-correlation and time series analysis of rabies in different animal species in Nepal from 2005 to 2018

Rabies is a fatal zoonotic disease, resulting in human and livestock deaths. In Nepal, animal rabies has posed a significant challenge to public health. Because animals are the primary source of rabies in humans, a better understanding of rabies epidemiology in animals is necessary. The objectives of this study were to determine the correlation between rabies occurrences in dogs and livestock animals and to detect the trends and change points of the disease using longitudinal data. The nationwide rabies dataset from 2005 to 2018 was analyzed using cross-correlation, multiple change points, and time series methods. Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR) were applied to the time series data. The results show a positive correlation between canine rabies and livestock rabies occurrences. Three significant change points were detected in the time series data, demonstrating that the occurrences were high in the initial years but stabilized before peaking to an upward trend in the final years of the study period. Nonetheless, there was no seasonality pattern in rabies occurrences. The most suitable models were ARIMA (2,1,2) and NNAR (5,1,4) (12). Based on the study findings, both locals and tourists in Nepal need to have enhanced awareness of the potential dangers posed by rabies in canines and livestock. This study offers much-needed insight into the patterns and epidemiology of animal rabies which will be helpful for policymakers in drafting rabies control plans for Nepal.

Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy

BACKGROUND

A common goal of scientific microscopic imaging is to determine if a spatial correlation exists between two imaged structures. This is generally accomplished by imaging fluorescently labeled structures and measuring their spatial correlation with a class of image analysis algorithms known as colocalization. However, the most commonly used methods of colocalization have strict limitations, such as requiring overlap in the fluorescent markers and reporting requirements for accurate interpretation of the data, that are often not met. Due to the development of novel super-resolution techniques, which reduce the overlap of the fluorescent signals, a new colocalization method is needed that does not have such strict requirements.

RESULTS

In order to overcome the limitations of other colocalization algorithms, I developed a new ImageJ/Fiji plugin, Colocalization by cross-correlation (CCC). This method uses cross-correlation over space to identify spatial correlations as a function of distance, removing the overlap requirement and providing more comprehensive results. CCC is compatible with 3D and time-lapse images, and was designed to be easy to use. CCC also generates new images that only show the correlating labeled structures from the input images, a novel feature among the cross-correlating algorithms.

CONCLUSIONS

CCC is a versatile, powerful, and easy to use colocalization and spatial correlation tool that is available through the Fiji update sites. Full and up to date documentation can be found at https://imagej.net/plugins/colocalization-by-cross-correlation . CCC source code is available at https://github.com/andmccall/Colocalization_by_Cross_Correlation .

Gait analysis, trunk movements, and electromyographic patterns after minimally invasive spine surgery for lumbar instability: An observational prospective study

Objective

The aim of the present study was to investigate trunk kinematics and spine muscle activation during walking after minimally invasive surgery in patients with L4-L5 degenerative spondylolisthesis suffering from lumbar instability (LI).

Methods

Eleven patients suffering from LI and 13 healthy controls (HC) were enrolled. Trunk kinematics and spine muscle activation patterns during walking were collected. Maximal trunk ranges of motion were also recorded from standing position. Assessments were performed pre-operatively (T0), 1 month (T1) and 3 months (T2) after MIS.

Results

We found significant improvement in spine muscle activation during walking at T2 compared to T0, mainly involving right/left symmetry at the operated level (L4-L5) and up-down synchronization from L3 to S1. Significant improvements in trunk rotation nearing to the HC group during walking were also found at T2 after surgery, though no changes were observed in the maximal range of motion of the trunk during standing. Furthermore, trunk rotation improvement correlated with a lower grade of residual disability.

Conclusions

Our findings indicate that trunk rotation improves after surgery, and impaired aspects of spine muscle activation can be improved with surgery. These biomechanical parameters could represent novel tools for monitoring the effect of surgery in LI and preventing impaired spine mobility and muscle activation.

Assessing the impact of precipitation on hardrock aquifer system using standard precipitation index and groundwater resilience index: a case study of Purulia, West Bengal, India

Groundwater stored in the aquifers provides water security during natural hazards, e.g. clean water access during floods and droughts. Groundwater drought, a phenomenon closely linked with rainfall (climate) variability, is less researched, especially in India. This study aims to detect precipitation and groundwater droughts and comprehend the groundwater response to long-term precipitation trends (25 years). As a case study, the drought-affected and groundwater-depleted Purulia district in West Bengal, India, which is a part of the Chotanagpur plateau, was selected. Precipitation and groundwater droughts (in aquifer types of shallow, moderate and deep) are detected using the Standard Precipitation Index (SPI) and Groundwater Resilience Index (GRI). During the 25 year study period (1996-2020), Purulia had 13 (52%) rainfall deficiency years, with an annual average rainfall of 1382 mm. SPI detected four severe droughts and the most severe occurring in 2010-2011 (1.50). GRI found that aquifermedium had a 71% [Formula: see text] conditions and are the most resilient and aquiferdeep experienced maximum extreme drought events and is the most stressed. The cross-correlation coefficients (CCCs) between rainfall and groundwater is moderate in deep, shallow, and medium aquifers, with CCCs - 0.43, - 0.59, and - 0.49, respectively. Positive CCCs are found for seasonal lags of - 3, - 4, and - 7. The study found that during the monsoon, average depth to groundwater level is 1 - 4 m and it drops to 8 - 10 m during the lean period, more than 85% of wells are vulnerable to extreme droughts (SPI > 1.5), aquifer's response to rainfall is aquifershallow > aquifermoderate > aquiferdeep, and aquifer's may be arranged as aquifermoderate > aquifershallow > aquiferdeep depending on their drought resistance. This study, with the use of statistical tools and long term data, will aid in the management of groundwater at varying depths by creating basis for understanding the groundwater response to rainfall events.

Slow-wave modulation analysis during states of unconsciousness using the novel tau-modulation method

OBJECTIVE

Slow-wave modulation occurs during states of unconsciousness and is a large-scale indicator of underlying brain states. Conventional methods typically characterize these large-scale dynamics by assuming that slow-wave activity is sinusoidal with a stationary frequency. However, slow-wave activity typically has an irregular waveform shape with a non-stationary frequency, causing these methods to be highly unpredictable and inaccurate. To address these limitations, we developed a novel method using tau-modulation, which is more robust than conventional methods in estimating the modulation of slow-wave activity and does not require assumptions on the shape or stationarity of the underlying waveform.

APPROACH.
We propose a novel method to estimate modulatory effects on slow-wave activity. Tau-modulation curves are constructed from cross-correlation between slow-wave and high-frequency activity. The resultant curves capture several aspects of modulation, including attenuation or enhancement of slow-wave activity, the temporal synchrony between slow-wave and high-frequency activity, and the rate at which the overall brain activity oscillates between states.

MAIN RESULTS.
The method's performance was tested on an open electrocorticographic dataset from two monkeys that were recorded during propofol-induced anesthesia, with electrodes implanted over the left hemispheres. We found a robust propagation of slow-wave modulation along the anterior-posterior axis of the lateral aspect of the cortex. This propagation preferentially originated from the anterior superior temporal cortex and anterior cingulate gyrus. We also found the modulation frequency and polarity to track the stages of anesthesia. The algorithm performed well, even with non-sinusoidal activity and in the presence of real-world noise.

SIGNIFICANCE.
The novel method provides new insight into several aspects of slow-wave modulation that have been previously difficult to evaluate across several brain states. This ability to better characterize slow-wave modulation, without spurious correlations induced by non-sinusoidal signals, may lead to robust and physiologically-plausible diagnostic tools for monitoring brain functions during states of unconsciousness.

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients.

Low complexity adaptive ultrasound image beamformer combined with improved multiphase apodization with cross-correlation

In this paper, an ultrasound imaging method combined with low-complexity adaptive beamformer (LCA) and improved multiphase apodization with cross-correlation (IMPAX) is proposed to improve image resolution and contrast with low hardware cost. Firstly, the delayed echo signal is apodized by the LCA to obtain a narrow mainlobe width echo signal and LCA output. Then, multiple pairs of complementary square-wave phase apodizations are applied to the apodized echo signal to obtain corresponding signal pairs, which are used to calculate the normalized cross-correlation (NCC) matrix. Finally, the average value of the NCC matrices is filtered by 2-D means, and the filtered result is introduced as the weighting factor for the LCA output. The simulation and experimental results show that the proposed LCA-IMPAX can effectively reduce the mainlobe width, suppress clutter, and be robust to noise. Compared with DAS, LCA, and MPAX, for simulated point targets, the full-width at half-maximum (FWHM, -6dB) of LCA-IMPAX is reduced by 49.22%, 10.06%, and 48.67%, respectively. For simulated cyst, the CR is improved by 219.91%, 138.08%, and 103.44%, respectively. For experimental cysts, the CR is improved by an average of 145.00%, 136.14%, and 55.09%, respectively. The results of human heart data indicate that LCA-IMPAX has good imaging quality in vivo. Since the proposed method does not involve covariance matrix inversion, it can be applied in real-time imaging systems.

Measurement of the pattern shifts for HR-EBSD with larger lattice rotations

High-resolution electron backscattering diffraction (HR-EBSD) was used to measure rotations and elastic strains by matching diffraction patterns based on cross-correlation. However, the subset-based phase correlation algorithm was unable to determine pattern shifts accurately when large rotations occurred. In this paper, a new matching algorithm was proposed to measure pattern shifts and recover the elastic strain and lattice rotation with finite deformation theory. The algorithm was implemented in two steps: (a) Integral pixel matching: The pixel-related information of the Kikuchi patterns was mapped to the original three-dimensional sphere to obtain the image projected in parallel by using the feature points as the pattern center through the transformation of its spatial coordinates. The correlation between the images projected in parallel before and after deformation was then obtained. The locations of the integral pixels were determined by the peaks of the surface of correlation obtained by traversing all pixels in the search area. (b) subpixel refinement: the locations of subpixels were obtained by FAGN with an appropriate shape function involving rotation and translation. The algorithm was applied to dynamic simulated test sets, and its results were compared with those of the first-pass cross-correlation and the second-pass cross-correlation method with remapping. The proposed method was more robust in the case of rotation and solved the problem that displacement vectors could not be accurately measured when a larger lattice rotation occurred. The mean errors of the measured displacement, rotation, and strain components were 0.02 pixel, 0.5×10^-4rad, and 1×10^-4, respectively. Compared with the second-pass cross-correlation method, the angle of rotation was more precisely extracted.

Differential expression and cross-correlation between global regulator and pho regulon genes involved in decision-making under phosphate stress

The differential gene expression under phosphate stress conditions leads to cross-talk between the global regulator, pho regulon, and metabolic genes. Promoter activity analysis of the selected 23 genes reveals the dynamic nature of real-time gene expression under different phosphate conditions. The expression profiles of the global regulator (rpoD, soxR, soxS, arcB, and fur), pho regulon (phoH, phoR, phoB, and ugpB), and metabolic genes (sdh, pfkA, ldh) varied significantly on phosphate level variation. Under stress conditions, soxR switches expression partners and co-expresses with rpoS instead of soxS. The partner-switching behavior of the genes under a challenging environment represents the intelligence of functional execution and ensures cell survival. The dynamic expression profile of the selected genes applies a time-lagged correlation to provide insight into the differential gene interaction between time-shifted expression profiles. Under different phosphate conditions, the minimum spanning tree graph revealed a different clustering pattern of selected genes depending on the computed distance and its proximity to other promoters.

DNA base order parameter determination without influence of chemical exchange

Nuclear magnetic resonance (NMR) spectroscopy is a versatile tool used to investigate the dynamic properties of biological macromolecules and their complexes. NMR relaxation data can provide order parameters S2, which represent the mobility of bond vectors reorienting within a molecular frame. Determination of S2 parameters typically involves the use of transverse NMR relaxation rates. However, the accuracy in S2 determination can be diminished by elevation of the transverse relaxation rates through conformational or chemical exchange involving protonation/deprotonation or non-Watson-Crick base-pair states of nucleic acids. Here, we propose an approach for determination of S2 parameters without the influence of exchange processes. This approach utilizes transverse and longitudinal 13C chemical shift anisotropy (CSA) - dipole-dipole (DD) cross-correlation rates instead of 13C transverse relaxation rates. Anisotropy in rotational diffusion is taken into consideration. An application of this approach to nucleotide base CH groups of a uniformly 13C/15N-labeled DNA duplex is demonstrated.

Tracking Gut Motility in Organ and Cultures

Gastrointestinal (GI) motility is a key component of digestive health, and it is complex, involving a multitude of cell types and mechanisms to drive both rhythmic and arrhythmic activity. Tracking GI motility in organ and tissue cultures across multiple temporal (seconds, minutes, hours, days) scales can provide valuable information regarding dysmotility and to evaluate treatment options. Here, the chapter describes a simple method to monitor GI motility in organotypic cultures, using a single video camera is placed perpendicularly to the surface of the tissue. A cross-correlational analysis is used to track the relative movements of tissues between subsequent frames and subsequent fitting procedures to fit finite element functions to the deformed tissue to calculate the strain fields. Additional motility index measures from the displacement information are used to further quantify the behaviors of the tissues that are maintained in organotypic culture over days. The protocols presented in this chapter can be adapted to study organotypic cultures from other organs.

Temporal offset association between the number of irinotecan-related adverse reactions and pharmacogenomic studies: A cross-correlation analysis

Objectives

Studies have proved that UGT1A1 (*6, *28 and *93) gene polymorphism was closely related to the side effects of irinotecan. This study intends to perform a correlation analysis on the relationship between pharmacogenomic studies and ADRs based on time series.

Methods

The ADRs related to irinotecan were derived through the FAERS; searched all pharmacogenomic studies in PubMed and Web of Science; then analyzed the sequence of correlation coefficients between total ADRs, fatal ADRs and pharmacogenomic studies under different time offset.

Results

There is a positive correlation between the number of total ADRs and pharmacogenomic studies, of which the maximum correlation coefficient was 0.78 (95 % CI: 0.58-0.90), with a lag of 1 year. There is also a positive correlation between the number of fatal ADRs and pharmacogenomic studies, with the maximum correlation coefficient of 0.87 (95 % CI: 0.73-0.94) and a offset of - 4 years.

Conclusion

It was found that both the total ADRs and fatal ADRs were significantly positively correlated with change trend of published pharmacogenomic literatures, which confirmed the role of pharmacogenomic research in promoting the safe use of irinotecan, and have a faster response time in reducing fatal ADRs during clinical application.

Market Efficiency and Cross-Correlations of Chinese New Energy Market with Other Assets: Evidence from Multifractality Analysis

Despite the upgrading of the attention and investment of new energy in Chinese public, its market efficiency and associations with other assets are relatively rarely explored. This paper, firstly, explores the multifractal feature and market efficiency of Chinese new energy market (NEI) by the multifractal detrended fluctuation analysis. Secondly, the multifractal cross-correlation analysis is performed to discuss the multifractality of cross-correlations between NEI and crude oil, external new energy indices (Global (SPGCE), United States (ECO) and Europe (ERIX)) and safe-haven asset (GOLD) respectively. The results show that Chinese new energy market has obvious multifractality with low market efficiency, which is mainly sourced from long-range correlation. It has the strongest linkages with external new energy markets and most insignificant association with gold. The heterogeneous sources contribute to their multifractal cross-correlations. It provides useful enlightenment for decision-makers to implement energy policy and reform, and for investors to make investment decisions.

Monte Carlo study of metabolite correlations originating from spectral overlap

Monte Carlo simulations and a mathematical model of spectral fitting were used to study the correlations between metabolites with overlapping resonances. The dependence of the polarity and the magnitude of cross-correlation coefficients between overlapping metabolites on the spectral patterns of MRS signals was investigated. The results demonstrate the importance of quantifying metabolite correlations originating from spectral overlap as they may confound determination of correlations of biological origin. The findings also indicate that it is possible to minimize unwanted metabolite correlations by altering spectral patterns in the presence of significant spectral overlap.

Hierarchical data visualization of experimental erythrocyte aggregation employing cross correlation and optical flow applications

Appraisal of microvascular erythrocyte velocity as well as aggregation are critical features of hemorheological assessment. Examination of erythrocyte velocity-aggregate characteristics is critical in assessing disorders associated with coagulopathy. Microvascular erythrocyte velocity can be assessed using various methodologic approaches; however, the shared assessment of erythrocyte velocity and aggregation has not been well described. The purpose of this study therefore is to examine three independent erythrocyte assessment strategies with and without experimentally induced aggregation in order to elucidate appropriate analytic strategy for combined velocity/aggregation assessment applicable to in-vivo capillaroscopy. We employed a hierarchical microfluidic model combined with Bland-Altman analysis to examine agreement between three methodologies to assess erythrocyte velocity appropriate for interpretation of cinematography of in-vivo microvascular hemorheology. We utilized optical and manual techniques as well as a technique which we term transversal temporal cross-correlation (TTC) to observe and measure both erythrocyte velocity and aggregation. In general, optical, manual and TTC agree in estimation of velocity at relatively low flow rate, however with an increase in infusion rate the optical flow method yielded the velocity estimates that were lower than the TTC and manual velocity estimates. We suggest that this difference was due to the fact that slower moving particles close to the channel wall were better illuminated than faster particles deeper in the channel which affected the optical flow analysis. Combined velocity/aggregation appraisal using TTC provides an efficient approach for estimating erythrocyte aggregation appropriate for in-vivo applications. We demonstrated that the optical flow and TTC analyses can be used to estimate erythrocyte velocity and aggregation both in ex-vivo microfluidics laboratory experiments as well as in-vivo recordings. The simplicity of TTC velocity may be advantageous for developing velocity estimate methods to be used in the clinic. The trade-off is that TTC estimation cannot capture features of the flow based on optical flow analysis of individually tracked particles.

Deep learning-based framework for motion-compensated image fusion in catheterization procedures

OBJECTIVE

Augmenting X-ray (XR) fluoroscopy with 3D anatomic overlays is an essential technique to improve the guidance of the catheterization procedures. Unfortunately, cardiac and respiratory motion compromises the augmented fluoroscopy. Motion compensation methods can be applied to update the overlay of a static model with regard to respiratory and cardiac motion. We investigate the feasibility of motion detection between two fluoroscopic frames by applying a convolutional neural network (CNN). Its integration in the existing open-source software framework 3D-XGuide is demonstrated, such extending its functionality to automatic motion detection and compensation.

METHODS

The CNN is trained on reference data generated from tracking of the rapid pacing catheter tip by applying template matching with normalized cross-correlation (CC). The developed CNN motion compensation model is packaged in a standalone web service, allowing for independent use via a REST API. For testing and demonstration purposes, we have extended the functionality of 3D-XGuide navigation framework by an additional motion compensation module, which uses the displacement predictions of the standalone CNN model service for motion compensation of the static 3D model overlay. We provide the source code on GitHub under BSD license.

RESULTS

The performance of the CNN motion compensation model was evaluated on a total of 1690 fluoroscopic image pairs from ten clinical datasets. The CNN model-based motion compensation method clearly overperformed the tracking of the rapid pacing catheter tip with CC with prediction frame rates suitable for live application in the clinical setting.

CONCLUSION

A novel CNN model-based method for automatic motion compensation during fusion of 3D anatomic models with XR fluoroscopy is introduced and its integration with a real software application demonstrated. Automatic motion extraction from 2D XR images using a CNN model appears as a substantial improvement for reliable augmentation during catheter interventions.

Bsoft: Image Processing for Structural Biology

Bsoft is a software package primarily developed for processing electron micrographs, with the goal of determining the structures of biologically relevant molecules, molecular assemblies, and parts of cells. However, it incorporates many ways to deal with images, from the mundane to very sophisticated algorithms. This article is an introduction into its use, illustrating that it is an extensive toolbox, for manipulating and understanding images. Bsoft has over 150 programs, allowing the user an infinite number of ways to process images. These programs can be executed on the command line, or through the interactive program called brun. The main visualization program is bshow, providing numerous ways to manipulate and interpret images. The primary aim is to provide the user with powerful capabilities, including processing large numbers of images. An important additional aim is to make it as accessible as possible, making it easier to deal with image formats and features, and enhance productivity.

Analysis of bluetongue disease epizootics in sheep of Andhra Pradesh, India using spatial and temporal autocorrelation

Bluetongue (BT) disease poses a constant risk to the livestock population around the world. A better understanding of the risk factors will enable a more accurate prediction of the place and time of high-risk events. Mapping the disease epizootics over a period in a particular geographic area will identify the spatial distribution of disease occurrence. A Geographical Information System (GIS) based methodology to analyze the relationship between bluetongue epizootics and spatial-temporal patterns was used for the years 2000 to 2015 in sheep of Andhra Pradesh, India. Autocorrelation (ACF), partial autocorrelation (PACF), and cross-correlation (CCF) analyses were carried out to find the self-dependency between BT epizootics and their dependencies on environmental factors and livestock population. The association with climatic or remote sensing variables at different months lag, including wind speed, temperature, rainfall, relative humidity, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface temperature (LST), was also examined. The ACF & PACF of BT epizootics with its lag showed a significant positive autocorrelation with a month's lag (r = 0.41). Cross-correlations between the environmental variables and BT epizootics indicated the significant positive correlations at 0, 1, and 2 month's lag of rainfall, relative humidity, normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Spatial autocorrelation analysis estimated the univariate global Moran's I value of 0.21. Meanwhile, the local Moran's I value for the year 2000 (r = 0.32) showed a high degree of spatial autocorrelation. The spatial autocorrelation analysis revealed that the BT epizootics in sheep are having considerable spatial association among the outbreaks in nearby districts, and have to be taken care of while making any forecasting or disease prediction with other risk factors.

Lower extremity kinematic coupling during single and double leg landing and gait in female junior athletes with dynamic knee valgus

BACKGROUND

Dynamic knee valgus (DKV) is a common lower extremity movement disorder among females. This study aimed to investigate kinematic couplings between lower extremity joints in female junior athletes with DKV during single and double-leg landing and gait.

METHODS

Twenty-six physically active female junior athletes (10-14 years old) with DKV were recruited. Kinematic couplings between rearfoot, tibia, knee, and hip were extracted using eight Vicon motion capture cameras and two force plates. Zero-lag cross-correlation coefficient and vector coding were used to calculate kinematic couplings between joints during physical tasks. Paired t-test and Wilcoxon tests were run to find significant couplings between joint motions and coupling strengths. Bonferroni posthoc was used to determine significance with α ≤ 0.05.

RESULTS

The results showed that the strongest kinematic relationship existed between rearfoot eversion/inversion and tibial internal/external rotation during all three tasks. Correlations of the rearfoot supination/pronation with tibial rotations, knee, and hip motions in sagittal, frontal, and transverse planes were very strong to strong during double-leg landing and moderate to weak during gait. A weak correlation was observed between rearfoot supination/pronation and hip adduction/abduction during single-leg landing.

CONCLUSIONS

Coupling relationships between rearfoot, knee, and hip vary by the task intensity and alignment profiles in female juniors with DKV.

Variance of filtered signals: Characterization for linear reaction networks and application to neurotransmission dynamics

Neurotransmission at chemical synapses relies on the calcium-induced fusion of synaptic vesicles with the presynaptic membrane. The distance of the synaptic vesicle to the calcium channels determines the release probability and consequently the postsynaptic signal. Suitable models of the process need to capture both the mean and the variance observed in electrophysiological measurements of the postsynaptic current. In this work, we propose a method to directly compute the exact first- and second-order moments for signals generated by a linear reaction network under convolution with an impulse response function, rendering computationally expensive numerical simulations of the underlying stochastic counting process obsolete. We show that the autocorrelation of the process is central for the calculation of the filtered signal's second-order moments, and derive a system of PDEs for the cross-correlation functions (including the autocorrelations) of linear reaction networks with time-dependent rates. Finally, we employ our method to efficiently compare different spatial coarse graining approaches for a specific model of synaptic vesicle fusion. Beyond the application to neurotransmission processes, the developed theory can be applied to any linear reaction system that produces a filtered stochastic signal.

Is the cross-correlation of EU carbon market price with policy uncertainty really being? A multiscale multifractal perspective

This paper aims to examine the cross-correlation relationship between EU carbon market price and the economic policy uncertainty. The United Kingdom and the United State of America are chosen as the representative countries. We first conduct the linear analysis to explore the correlation of EU carbon market futures return with the economic policy uncertainty of the two countries. Our findings show that there is no linear correlation between EU carbon market return and economic policy uncertainty. Then, we apply the multifractal detrended cross-correlation analysis to examine the cross-correlation between the return of EU carbon market futures and economic policy uncertainty. The empirical results indicate that the cross-correlations really exist, and the cross-correlation behavior structure over different carbon trading phases are not the same. Moreover, the empirical results show that the anti-persistence between the EU carbon futures return and economic policy uncertainty changes from the UK and the USA are both relatively strong. The findings provide deeper insights and management implications for the carbon market from a new perspective.

Psychophysiological dynamics of emotional reactivity: Interindividual reactivity characterization and prediction by a machine learning approach

The fast reaction of the autonomic nervous system (ANS) to an emotional challenge (EC) is the result of a functional coupling between parasympathetic (PNS) and sympathetic (SNS) branches. This coupling can be characterized by measures of cross-correlations between electrodermal activity (EDA) (under the influence of the SNS) and the RR interval (the interval between R peaks) (under the influence of the PNS and the SNS). Significant interindividual variability has previously been reported in SNS-PNS coupling in emotional situations, and the present study aimed to identify interindividual cross-correlation variability in ANS reactivity. We therefore studied EDA and the RR interval in 62 healthy subjects, recorded during a 24-minute EC. A Gaussian Mixture Model was used to cluster tonic EDA-RR cross-correlations during the EC. This identified two clusters that were characterized by significant or non-significant cross-correlations (SCC and NCC clusters, respectively). The SCC cluster reported higher negative emotion after the EC, while the NCC cluster reported higher scores on the Center for Epidemiologic Studies-Depression scale. The latter finding suggests that NCC is a pathological mood pattern with altered negative perception. Furthermore, a machine learning model that included three parameters indexing the functionality of both branches of the ANS, measured at baseline, predicted cluster membership. Our results are a first step in detecting dysfunctional ANS reactivity in general population.

An improved ultrasonic coda wave method for concrete behavior monitoring under various loading conditions

Monitoring of concrete behavior is an important task to evaluate the safety of concrete structures. This paper proposes a new stretching factor accumulation method based on the stepwise coda wave interference (CWI) to accurately calculate the relative velocity change Δv/v. The Δv/v and residual decorrelation coefficient Kd are used to study the concrete behavior in step loading experiment and short-time fixed loading experiment. The results show that: (1) In the loading stage of step loading experiments, both Δv/v and Kd can be used to monitor the load and the microcrack development; (2) In the relaxation stage of step loading experiments, the slow dynamics effect of concrete specimens can be characterized by Δv/v and Kd; (3) In the short-time fixed loading experiments, both Δv/v and Kd increase with the time of fixed loading, and the increasing rate of Δv/v decreases with the increase of concrete strength. This work can be used to promote the development of ultrasonic testing techniques that aid in concrete behavior monitoring.

Ultrasonic blood flowmeter with a novel Xero algorithm for a mechanical circulatory support system

Mechanical circulatory support systems (MCSSs) are crucial devices for transplants in patients with heart failure. The blood flowing through the MCSS can be recirculated or even stagnated in the event of critical blood flow issues. To avoid emergencies due to abnormal changes in the flow, continuous changes of the flowrate should be measured with high accuracy and robustness. For better flowrate measurements, a more advanced ultrasonic blood flowmeter (UFM), which is a noninvasive measurement tool, is needed. In this paper, we propose a novel UFM sensor module using a novel algorithm (Xero) that can exploit the advantages of both conventional cross-correlation (Xcorr) and zero-crossing (Zero) algorithms, using only the zero-crossing-based algorithm. To ensure the capability of our own developed and optimized ultrasonic sensor module for MCSSs, the accuracy, robustness, and continuous monitoring performance of the proposed algorithm were compared to those of conventional algorithms after application to the developed sensor module. The results show that Xero is superior to other algorithms for flowrate measurements under different environments and offers an error rate of at least 0.92%, higher robustness for changing fluid temperatures than conventional algorithms, and sensitive responses to sudden changes in flowrates. Thus, the proposed UFM system with Xero has a great potential for flowrate measurements in MCSSs.

Infant functional networks are modulated by state of consciousness and circadian rhythm

Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

Insights to enhance the examination of tool marks in human cartilage

This work deals with the examination of tool marks in human cartilage. We compared the effectiveness of several cleaning methods on cut marks in porcine cartilage. The method cleaning by multiple casts achieved the significantly highest scores (P = 0.02). Furthermore, we examined the grain-like elevations (dots) located on casts of cut cartilage. The results of this study suggest that the casting material forms these dots when penetrating cartilage cavities, which are areas where the strong collagen fibres leave space for the chondrocytes. We performed fixation experiments to avoid this, without success. In addition, 31 casting materials were compared regarding contrast under light-microscope and 3D tool marks scanner. Under the light-microscope, brown materials achieved significantly higher values than grey (P = 0.02) or black (P = 0.00) whereas under the 3D scanner, black materials reached higher contrast values than grey (P = 0.04) or brown (P = 0.047). To compare the accuracy and reproducibility of 6 test materials for cartilage, we used 10 knives to create cut marks that were subsequently scanned. During the alignment of the individual signals of each mark, the cross-correlation coefficients (X_max) and lags (L_Xmax) were calculated. The signals of the marks in agarose were aligned with significantly fewer lags and achieved significantly higher cross-correlation coefficients compared to all tested materials (both P = 0.00). Moreover, we determined the cross-correlation coefficients (X_C) for known-matches (KM) per material. Agarose achieved significantly higher values than AccuTrans®, Clear Ballistics™, and gelatine (all P = 0.00). The results of this work provide valuable insights for the forensic investigation of marks in human costal cartilage.

Stock markets and the COVID-19 fractal contagion effects

This article investigates the fractal contagion effect of the COVID-19 pandemic on the stock markets. The stock market information of the top 32 coronavirus affected economies (as of 31st March 2020) was sampled for ex-ante and ex-post COVID-19 outbreak analysis using the Detrended Moving Cross-Correlation Analysis (DMCA) and Detrended Cross-Correlation Analysis (DCCA) techniques. The results confirm a fractal contagion effect of the COVID-19 pandemic on the stock markets. Furthermore, this fractal contagion effect fizzles out over time (in the middle and long run) for both the stock markets return and volatility. Therefore, this article provides pieces of evidence for the COVID-19 fractal contagion effect on the stock markets.

Correlation networks of air particulate matter ( PM 2.5 ): a comparative study

Over the last decades, severe haze pollution constitutes a major source of far-reaching environmental and human health problems. The formation, accumulation and diffusion of pollution particles occurs under complex temporal scales and expands throughout a wide spatial coverage. Seeking to understand the transport patterns of haze pollutants in China, we review a proposed framework of time-evolving directed and weighted air quality correlation networks. In this work, we evaluate monitoring stations' time-series data from China and California, to test the sensitivity of the framework to region size, climate and pollution magnitude across multiple years (2014-2020). We learn that the use of hourly PM 2.5 concentration data is needed to detect periodicities in the positive and negative correlations of the concentrations. In addition, we show that the standardization of the correlation function method is required to obtain networks with more meaningful links when evaluating the dispersion of a severe haze event at the North China Plain or a wildfire event in California during December 2017. Post COVID-19 outbreak in China, we observe a significant drop in the magnitude of the assigned weights, indicating the improved air quality and the slowed transport of PM 2.5 due to the lockdown. To identify regions where pollution transport is persistent, we extend the framework, partitioning the dynamic networks and reducing the networks' complexity through node subsampling. The end result separates the temporal series of PM 2.5 in set of regions that are similarly affected through the year.

Word-Final Phoneme Segmentation Using Cross-Correlation

The goal of this paper is to present a word-final target phoneme automated segmentation method based on cross-correlation coefficients computed between a reference sound wave and a sample sound wave. Most existing Speech Sound Disorder (SSD) Screening solutions require human intervention to a greater or lesser extent and use segmentation methods based on hard-coded time frames. Moreover, existing solutions extract features from the frequency domain, which entails large amounts of computational power to the detriment of real-time feedback. The pre-processing algorithm proposed in this paper, implemented in a Python version 3.7 script, automatically generates 2 new .wav files corresponding to the phonemes found in word-final position in the initial sound waves. The newly-generated .wav files are meant to be used as valid and homogeneous input in a subsequent classification stage aimed at rigorously discriminating mispronunciations of the target phoneme and assist Speech-Language Pathologists (SLPs) with the SSD screening.

Motoneurone synchronization for intercostal and abdominal muscles: interneurone influences in two different species

The contribution of branched-axon monosynaptic inputs in the generation of short-term synchronization of motoneurones remains uncertain. Here, synchronization was measured for intercostal and abdominal motoneurones supplying the lower thorax and upper abdomen, mostly showing expiratory discharges. Synchronization in the anaesthetized cat, where the motoneurones receive a strong direct descending drive, is compared with that in anaesthetized or decerebrate rats, where the direct descending drive is much weaker. In the cat, some examples could be explained by branched-axon monosynaptic inputs, but many others could not, by virtue of peaks in cross-correlation histograms whose widths (relatively wide) and timing indicated common inputs with more complex linkages, e.g., disynaptic excitatory. In contrast, in the rat, correlations for pairs of internal intercostal nerves were dominated by very narrow peaks, indicative of branched-axon monosynaptic inputs. However, the presence of activity in both inspiration and expiration in many of the nerves allowed additional synchronization measurements between internal and external intercostal nerves. Time courses of synchronization for these often consisted of combinations of peaks and troughs, which have never been previously described for motoneurone synchronization and which we interpret as indicating combinations of inputs, excitation of one group of motoneurones being common with either excitation or inhibition of the other. Significant species differences in the circuits controlling the motoneurones are indicated, but in both cases, the roles of spinal interneurones are emphasised. The results demonstrate the potential of motoneurone synchronization for investigating inhibition and have important general implications for the interpretation of neural connectivity measurements by cross-correlation.

Electromyography of pelvic floor muscles with true differential versus faux differential electrode configuration

INTRODUCTION AND HYPOTHESIS

In pelvic floor muscle (PFM) electromyography (EMG) two different bipolar configurations are applied: "true differential" configuration (TD) measures neuromuscular activity with two ipsilateral electrodes, whereas "faux differential" configuration (FD) has two electrodes placed on each side of the PFMs. The aim of the study was to determine possible differences and the relationship between both configurations.

METHODS

A secondary data analysis of 28 continent (CON) and 22 stress urinary incontinent (SUI) women was performed. Surface EMG was measured using a vaginal probe during maximal voluntary (MVC) and fast voluntary (FVC) contractions. TD and FD were explored with amplitude- and time-related EMG parameters, cross-correlation coefficients (R(0)) and statistical parametric mapping (SPM).

RESULTS

Of a total of 62 comparisons of EMG parameters of MVC and FVC, only one comparison showed significant differences between the two configurations (CON group, FVC_4peak TD versus FD, p = 0.015). R(0) were high in both groups for all MVC and FVC variables (R(0) ≥ 0.989). SPM detected 3 out of 28 comparisons with short (0.124-0.404 s) significant supra-threshold clusters (p < 0.025).

CONCLUSIONS

The findings suggest that TD and FD might measure neuromuscular activity almost the same. Very high cross-correlation coefficients and a very limited number of significant results from EMG parameters, as well as SPM, suggest that in the measured sample the choice of TD or FD might remain practically irrelevant. To gain further insight into the scientific and clinical relevance of choosing either of the electrode configurations, the comparisons should be re-evaluated on a sample with more severe incontinence symptoms.

Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants

Point sources are important routes through which pollutants enter rivers. It is important to identify the characteristics of and trace the origins of water pollutants. In this study, an artificial intelligence system called the integrated long short-term memory network (LSTM), using cross-correlation and association rules (Apriori), was used to identify the characteristics of water pollutants and trace industrial point sources of pollutants. Water quality monitoring data from Shandong Province, China, were used to verify the applicability of the artificial intelligence system using a cross-correlation method to develop a water quality cross-correlation map. The map was used to identify highly correlated pollutants affecting water quality, then the association rules (Apriori) were used to track the pollutants to industries common in the study area. The highly correlated water pollutants and relevant industries were used as inputs for the LSTM to determine how well the LSTM traced sources of water pollutants. The results showed that (1) changes in water quality were affected in different ways by different industries and different distributions and production cycles of the pollutant point sources; (2) water quality correlation maps can be used to identify regular and abnormal fluctuations in point source pollutant emissions by identifying changes in water quality characteristics and frequent itemsets in water quality indices can be used to trace the industries that most strongly affect water quality; and (3) the LSTM accurately traced point sources of future changes in water quality. In conclusion, the artificial intelligence scheme described here can be applied to aquatic systems.

Automatic Analyte-Ion Recognition and Background Removal for Ambient Mass-Spectrometric Data Based on Cross-Correlation

Ambient mass spectrometry is a powerful approach for rapid, high-throughput, and direct sample analysis. Due to the open-air desorption and ionization processes, random fluctuations of ambient conditions can lead to large variances in mass-spectral signals over time. The mass-spectral data also can be further complicated due to multiple analytes present in the sample, background-ion signals stemming from the desorption/ionization source itself, and other laboratory-specific conditions (e.g., ambient laboratory air, nearby hardware). Thus, background removal and analyte-ion recognition can be quite difficult, particularly in non-targeted analyses. Here, we demonstrate the use of a cross-correlation-based approach to exploit chemical information encoded in the time domain to group/categorize mass-spectral peaks from a single analysis dataset. Ions that originate from ambient (or other) background species were readily flagged and removed from spectra; the result was a decrease in mass-spectral complexity by over 70% due to the removal of these background ions. Meanwhile, analyte ions were differentiated and categorized based on their time-domain profiles. With sufficient mass resolving-power and mass-spectral acquisition rate, isolated mass spectra containing ions from the same species in a sample could be extracted, leading to a reduction in mass-spectral complexity by more than 98% in some cases. The cross-correlation approach was tested with different ionization sources as well as reproducible and irreproducible sample introduction. Software built in-house enabled fully automated data processing, which can be performed within a few seconds. Ultimately, this approach provides an additional dimension of analyte separation in ambient mass-spectrometric analyses with information that is already recorded throughout the analysis.

The anterior insula channels prefrontal expectancy signals during affective processing

Expectancy shapes our perception of impending events. Although such an interplay between cognitive and affective processes is often impaired in mental disorders, it is not well understood how top-down expectancy signals modulate future affect. We therefore track the information flow in the brain during cognitive and affective processing segregated in time using task-specific cross-correlations. Participants in two independent fMRI studies (N₁ = 37 & N₂ = 55) were instructed to imagine a situation with affective content as indicated by a cue, which was then followed by an emotional picture congruent with expectancy. To correct for intrinsic covariance of brain function, we calculate resting-state cross-correlations analogous to the task. First, using factorial modeling of delta cross-correlations (task-rest) of the first study, we find that the magnitude of expectancy signals in the anterior insula cortex (AIC) modulates the BOLD response to emotional pictures in the anterior cingulate and dorsomedial prefrontal cortex in opposite directions. Second, using hierarchical linear modeling of lagged connectivity, we demonstrate that expectancy signals in the AIC indeed foreshadow this opposing pattern in the prefrontal cortex. Third, we replicate the results in the second study using a higher temporal resolution, showing that our task-specific cross-correlation approach robustly uncovers the dynamics of information flow. We conclude that the AIC arbitrates the recruitment of distinct prefrontal networks during cued picture processing according to triggered expectations. Taken together, our study provides new insights into neuronal pathways channeling cognition and affect within well-defined brain networks. Better understanding of such dynamics could lead to new applications tracking aberrant information processing in mental disorders.

Non-invasive Small Vessel Imaging of Human Thyroid Using Motion-Corrected Spatiotemporal Clutter Filtering

Reliable assessment of small vessel blood flow in the thyroid, without using any contrast agents, can be challenging because of increased physiological motion resulting from its proximity to the pulsating carotid artery. In this study, we hypothesized that correction of tissue motion prior to singular value decomposition (SVD)-based clutter filtering can improve the coherency of the tissue components and, thus, may allow better clutter suppression and visualization of small vessels in the thyroid. We corroborated this hypothesis by conducting phantom and in vivo studies using a clinical ultrasound scanner implemented with compounded plane wave imaging. The phantom studies were conducted using a homogeneous tissue-mimicking phantom to study the impact of motion on the covariance of the spatiotemporal Doppler data, in the absence of blood activity. The non-invasive in vivo study was conducted on a 74-y-old woman with a thyroid nodule suspicious of malignancy. A rigid body-based motion correction was performed using tissue displacements obtained from 2-D normalized cross-correlation-based speckle tracking. Subsequently, the power Doppler images were computed using SVD-based spatiotemporal clutter filtering. The results from the phantom study revealed that motion can considerably reduce the covariance of the spatiotemporal data and, thus, increase the rank of the tissue components. When the phantom was subjected to a total translation displacement of 6 pixels over the entire ensemble, in each direction (axial and lateral), the covariance dropped by more than 25%. The results obtained from the non-invasive in vivo study indicated that visualization of small vessel blood flow improved with motion correction of the power Doppler ensemble. The contrast-to-noise ratio of the blood signal in motion-corrected power Doppler images was considerably higher (8.17 and 8.32 dB), compared with that obtained using the standard SVD approach at an optimal threshold (0.87 and 4.33 dB) and a lower singular value threshold (1.92 and 3.05 dB). Further, the covariance of the in vivo thyroid spatiotemporal data increased by approximately 10% with motion correction. These preliminary results indicate that motion correction can be used to improve the visualization of small vessel blood flow in the thyroid, without using any contrast agents. The results of this feasibility study were encouraging, and warrant further development and more in vivo validation in moving tissues and organs.

Analysis of trends, periodicities, and correlations in the beryllium-7 time series in Northern Europe

The activity concentrations of beryllium-7, a natural radiotracer that is considered as a tracer of the stratospheric-tropospheric exchange, shows a distinct behaviour in Northern Europe compared to the central and southern parts of the continent. In this study, we use the measurements collected at four sampling stations in Scandinavia (Ivalo, Umea, Kista, Risoe) between 2001 and 2010 and investigate their trends, periodicities and residuals with the aim to further understand the common features in the beryllium-7 data records in northern sampling sites. The beryllium-7 activity concentrations exhibit statistically significant positive trends that range from an average value of 1.50%/year to an average value of 2.96%/year. We detect a one-year periodicity in all the sites, and in the southernmost site, Risoe in Denmark, additional higher-frequency harmonics. In the residual time series, we find outliers that represent occurrences of extremely high beryllium-7 activity concentration. Cross-correlations of the beryllium-7 residuals across the four sites decrease with increasing distance; similarly, as indicated by local Hurst exponents the records exhibit long-range correlations that weaken towards the end of the investigated period. To investigate the causes of the detected trends, we also calculate correlations between beryllium-7 and factors related to its production, transport and removal from the atmosphere: in particular, cross-correlations of the beryllium-7 residuals with residuals in sunspot number, local temperature, atmospheric pressure and precipitation, as well as Arctic Oscillation index and North Atlantic Oscillation index. Most of the obtained correlations, however, are not statistically significant, highlighting the need to analyse a longer time period in order to evaluate the impact of different factors on the airborne beryllium-7 activity concentration.

Spinal segments do not move together predictably during daily activities

BACKGROUND

Considering the thoracic, lumbar spine or whole spine as rigid segments has been the norm until recent studies highlighted the importance of more detailed modelling. A better understanding of the requirement for spine multi-segmental analysis could guide planning of future studies and avoid missing clinically-relevant information.

RESEARCH QUESTION

This study aims to assess the correlation between adjacent spine segments movement thereby evaluating segmental redundancy in both healthy and participants with low back pain (LBP).

METHODS

A 3D motion capture system tracked the movement of upper and lower thoracic and lumbar spine segments in twenty healthy and twenty participants with LBP. Tasks performed included walking, sit-to-stand and lifting, repeated 3 times. 3D angular kinematics were calculated for each spine segment. Segmental redundancy was evaluated through cross-correlation (R_xy) analysis of kinematics time series and correlation of range of motion (R_ROM) of adjacent spine segments.

RESULTS

The upper/lower lumbar pairing showed weak correlations in the LBP group for all tasks and anatomical planes (R_xyrange:0.02-0.36) but moderate and strong correlations during walking (R_xy _frontalplane:0.4) and lifting (R_xy _sagittalplane:0.64) in the healthy group. The lower thoracic/upper lumbar pairing had weak correlations for both groups during lifting and sit-to-stand in the frontal plane and for walking (R_xy:0.01) in the sagittal plane only. The upper/lower thoracic pairing had moderate correlations during sit-to-stand in sagittal and transverse plane in patients with LBP (R_xy _sagittalplane:0.41; R_xy _transverse plane:-0.42) but weak in healthy (R_xy _sagittalplane:0.23; R_xy _transverseplane:-0.34); the contrary was observed during lifting. The majority of R_ROM values (55/72) demonstrated weak correlations.

SIGNIFICANCE

The results suggest that multi-segmental analysis of the spine is necessary if spine movement characteristics are to be fully understood. We cannot establish a priori where redundancy occurs based on healthy data, therefore extra consideration should be made when planning studies with pathological cohorts.

Fluorescence Correlation Spectroscopy to Examine Protein-Lipid Interactions in Membranes

Fluorescence correlation spectroscopy (FCS) is a versatile technique to study membrane dynamics and protein-lipid interactions. It can provide information about diffusion coefficients, concentrations, and molecular interactions of proteins and lipids in the membrane. These parameters allow for the determination of protein partitioning into different lipid environments, the identification of lipid domains, and the detection of lipid-protein complexes on the membrane. During the last decades, FCS studies were successfully performed on model membrane systems as also on living cells, to characterize protein-lipid interactions. Recent developments of the method described here improved quantitative measurements on membranes and decreased the number of potential artifacts. The aim of this chapter is to provide the reader with the necessary information and some practical guidelines to perform FCS studies on artificial and cellular membranes.

Reconstruction of Functional Connectivity from Multielectrode Recordings and Calcium Imaging

In the last two decades, increasing research efforts in neuroscience have been focused on determining both structural and functional connectivity of brain circuits, with the main goal of relating the wiring diagram of neuronal systems to their emerging properties, from the microscale to the macroscale. While combining multisite parallel recordings with structural circuits' reconstruction in vivo is still very challenging, the reductionist in vitro approach based on neuronal cultures offers lower technical difficulties and is much more stable under control conditions. In this chapter, we present different approaches to infer the connectivity of cultured neuronal networks using multielectrode array or calcium imaging recordings. We first formally introduce the used methods, and then we will describe into details how those methods were applied in case studies. Since multielectrode array and calcium imaging recordings provide distinct and complementary spatiotemporal features of neuronal activity, in this chapter we present the strategies implemented with the two different methodologies in distinct sections.

APPLE picker: Automatic particle picking, a low-effort cryo-EM framework

Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While template-based particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, this approach is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and template-free particle picking. This approach is evaluated on publicly available datasets containing micrographs of β-galactosidase, T20S proteasome, 70S ribosome and keyhole limpet hemocyanin projections.

Human gait symmetry assessment using a depth camera and mirrors

It is proposed in this paper a reliable approach for human gait symmetry assessment using a Time-of-Flight (ToF) depth camera and two mirrors. The setup formed from these devices provides a sequence of 3D point clouds that is the input of our system. A cylindrical histogram is estimated for describing the posture in each point cloud. The sequence of such histograms is then separated into two sequences of sub-histograms representing two half-bodies. A cross-correlation technique is finally applied to provide values describing gait symmetry indices. The evaluation was performed on 9 different gait types to demonstrate the ability of our approach in assessing gait symmetry. A comparison between our system and related methods, that employ different input data types, is also provided.

Temporal association between antibiotic use and resistance in Klebsiella pneumoniae at a tertiary care hospital

Background

β-Lactam/β-lactamase inhibitors (BLBLIs) were introduced into clinical practice as an alternative to carbapenems for treating multi-drug-resistant Klebsiella pneumoniae infections. However, little is known about the relationship between BLBLI treatment and antimicrobial resistance. In this study, we investigated the trends and the temporal association between antibiotic use and antimicrobial resistance in K. pneumoniae isolates obtained between 2012 and 2016.

Methods

Data regarding quarterly consumption (total number of prescriptions per quarter) of all BLBLIs, all third-generation cephalosporins, and all fluoroquinolones at a tertiary care hospital were obtained from the Korean Health Insurance Review and Assessment Service. Susceptibility data (isolation rate of antibiotic resistance per quarter) were obtained from the existing database of the same tertiary hospital. Regression analysis was used to analyze annual trends and cross-correlations to assess the temporal association on a quarterly basis between antibiotic consumption and antibiotic resistance in K. pneumoniae.

Results

The rate of resistance to piperacillin/tazobactam in K. pneumoniae significantly increased over the study period (p < 0.01). The consumption of all BLBLIs was also found to be significantly correlated with the rate of resistance to piperacillin/tazobactam (β = 0.66; p < 0.01), ceftazidime (β = 0.54; p = 0.02), and levofloxacin (β = - 0.60; p = 0.01) with two-quarter lags. Furthermore, the consumption of all third-generation cephalosporins was significantly correlated with rates of K. pneumoniae resistance to ceftazidime (β = 0.64; p < 0.01) with a two-quarter lag and levofloxacin (β = 0.50; p = 0.03) with a quarter lag. The consumption of all fluoroquinolones correlated with resistance rates to ceftazidime (β = 0.14; p < 0.01) with a two-quarter lag.

Conclusions

The rate of resistance to piperacillin/tazobactam in K. pneumoniae increased significantly over the study period and was significantly correlated with BLBLI consumption. While BLBLIs can potentially be utilized as an alternative to carbapenems, our findings reinforce concerns of resistance to these drugs. Further research is needed to understand the implications on resistance of utilizing BLBLIs as a carbapenem-sparing option.

A systematic correlation analysis of carotenoids, chlorophyll, non-pigmented cell mass, and cell number for the blueprint of Dunaliella salina culture in a photobioreactor

Microalgal carotenoids are attractive health ingredients, but their production should be optimized to improve cost-effectiveness. Understanding cellular physiology centered on carotenoid synthesis is the prerequisite for this work. Therefore, systematic correlation analyses were conducted among chlorophyll, carotenoids, non-pigmented cell mass, and cell number of Dunaliella salina in a specified condition over a relatively long culture time. First, an integrated correlation was performed: a temporal profile of the carotenoids was correlated with those of other factors, including chlorophyll, non-pigmented cell mass, and cell number. Pearson and Spearman correlation analyses were performed to identify linearity and monotonicity of the correlation, respectively, and then cross-correlation was executed to determine if the correlation had a time lag. Second, to understand the cellular potential of metabolism, the procedure was repeated to provide a data set composed of the specific synthesis rates of the factors or growth rate, which additionally provided kinetic correlations among the constituting components of the cell, excluding the effect of cell number. This systematic approach could generate a blueprint model that is composed of only what it needs, which could make it possible to efficiently control and optimize the process.

A novel approach for the detection and exploration of joint coupling patterns in the lower limb kinetic chain

BACKGROUND

A comprehensive perspective on foot and lower limb joint coupling is lacking since previous studies did not consider the multi-articular nature of the foot and lower limb neither accounted for biomechanical heterogeneity.

RESEARCH QUESTION

The current manuscript describes a novel methodological process for detection and exploration of joint coupling patterns in the lower limb kinetic chain.

METHODS

The first stage of the methodological process encompasses the measurement of 3D joint kinematics of the foot and lower limb kinetic chain during dynamic activities. The second stage consists of selecting the kinematic waveforms of interest. In the third stage, cross-correlation coefficients are calculated across the selected one-dimensional continua of each subject. In the fourth stage, all cross-correlation coefficients per subject are used as input variable in a cluster algorithm. Algorithm specific qualitative metrics are subsequently considered to determine the most robust clustering. Finally, in the fifth stage the process of biomechanical interpretation is initiated and further exploration is recommended by triangulating with other biomechanical variables.

RESULTS

A first clinical illustration of the novel method was provided using data of fourteen young elite athletes. Cross-correlation coefficients for each leg were calculated across continua of the pelvis, hip, knee, rear foot and midfoot. A hierarchical clustering approach stratified the coefficients into two distinct clusters which was mainly guided by the frontal plane knee kinematics. Both clustered differed significantly from each other with respect to their frontal plane ankle, knee and hip kinetics.

SIGNIFICANCE

The presented method seems to provide a valuable approach to gain insight into foot and lower joint coupling.

Spatio-temporal Organization During Ventricular Fibrillation in the Human Heart

In this paper, we present a novel approach to quantify the spatio-temporal organization of electrical activation during human ventricular fibrillation (VF). We propose three different methods based on correlation analysis, graph theoretical measures and hierarchical clustering. Using the proposed approach, we quantified the level of spatio-temporal organization during three episodes of VF in ten patients, recorded using multi-electrode epicardial recordings with 30 s coronary perfusion, 150 s global myocardial ischaemia and 30 s reflow. Our findings show a steady decline in spatio-temporal organization from the onset of VF with coronary perfusion. We observed transient increases in spatio-temporal organization during global myocardial ischaemia. However, the decline in spatio-temporal organization continued during reflow. Our results were consistent across all patients, and were consistent with the numbers of phase singularities. Our findings show that the complex spatio-temporal patterns can be studied using complex network analysis.

An adaptive, real-time cadence algorithm for unconstrained sensor placement

This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods. 22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s^-1), running (3.4 m s^-1), and sprinting (5.2 and 5.7 m s^-1), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm. The described algorithm resulted in consistent numbers of true positives (85.6-100.0%) and false positives (0.0-2.9%) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ± SD: 98.9 ± 0.2%), compared to conventional cross-correlation (87.3 ± 13.5%), biased (73.0 ± 16.2) and unbiased (82.2 ± 20.6) autocorrelation procedures. This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions.

A Biologist-Friendly Method to Analyze Cross-Correlation Between Protrusion Dynamics and Membrane Recruitment of Actin Regulators

During mesenchymal cell motility, various actin regulators are recruited to the leading edge with exquisite precision in time and space to generate protrusion and retraction cycles. We present here an automated method, named CorRecD (from Correlation Recruitment Dynamics), which quantifies cell edge dynamics, protein recruitment and analyze their cross-correlation. The Wave Regulatory Complex (WRC), a master driver of protrusions, is used as a case-of-study. This biologist-friendly method relies on free software tools and can be applied to any fluorescently tagged protein of interest.

Google as a cancer control tool in Queensland

BACKGROUND

Recent advances in methodologies utilizing "big data" have allowed researchers to investigate the use of common internet search engines as a real time tool to track disease. Little is known about its utility with tracking cancer incidence. This study aims to investigate the potential correlates of monthly internet search volume indexes (SVIs) and observed monthly age standardised incidence rates (ASRs) for breast cancer, colorectal cancer, melanoma and prostate cancer.

METHODS

The monthly ASRs for the four cancers in Queensland were calculated using data from the Queensland Cancer Registry between January 2006 and December 2012. The monthly SVIs of the respective cancer search terms in Queensland were accessed from Google Trends for the same period. A time series seasonal decomposition method was performed to detect the seasonal patterns of SVIs and ASRs. Pearson's correlation coefficient and time series cross-correlation analysis were used to assess the associations between SVIs and ASRs. Linear regression models were used to examine the power of SVIs to predict monthly in ASRs.

RESULTS

Increases in the monthly ASRs of the four cancers were significantly correlated with increases in the monthly SVIs of the respective cancers except for colorectal cancer. The predictive power of the SVIs to explain variances in the corresponding ASRs varied by cancer type, with the percent explained ranging from 5.6% for breast cancer to 17.9% for skin cancer (SVI) with melanoma (ASR). Some improvement in the variation explained was obtained by including more search terms or lagged SVIs for the respective cancers in the linear regression models. The seasonal analysis indicated that the SVIs peaked periodically at around their respective cancer awareness months.

CONCLUSIONS

Using SVIs from a popular internet search engine was only able to explain a small portion of changes in the respective ASRs. While an expanded regression model explained a higher proportion of variability, the interpretation of this was difficult. Further development and refinement of this approach will be needed before search-based cancer surveillance can provide useful information regarding resource deployment to guide cancer control and track the impact of cancer awareness and education programmes.