Research on a Transformer Vibration Fault Diagnosis Method Based on Time-Shift Multiscale Increment Entropy and CatBoost.

A mechanical vibration fault diagnosis is a key means of ensuring the safe and stable operation of transformers. To achieve an accurate diagnosis of transformer vibration faults, this paper proposes a novel fault diagnosis method based on time-shift multiscale increment entropy (TSMIE) combined with CatBoost. Firstly, inspired by the concept of a time shift, TSMIE was proposed. TSMIE effectively solves the problem of the information loss caused by the coarse-graining process of traditional multiscale entropy. Secondly, the TSMIE of transformer vibration signals under different operating conditions was extracted as fault features. Finally, the features were sent into the CatBoost model for pattern recognition. Compared with different models, the simulation and experimental results showed that the proposed model had a higher diagnostic accuracy and stability, and this provides a new tool for transformer vibration fault diagnoses.

Haemosync: A synchronisation algorithm for multimodal haemodynamic signals.

BACKGROUND: Synchronous acquisition of haemodynamic signals is crucial for their multimodal analysis, such as dynamic cerebral autoregulation (DCA) analysis of arterial blood pressure (ABP) and transcranial Doppler (TCD)-derived cerebral blood velocity (CBv). Several technical problems can, however, lead to (varying) time-shifts between the different signals. These can be difficult to recognise and can strongly influence the multimodal analysis results. METHODS: We have developed a multistep, cross-correlation-based time-shift detection and synchronisation algorithm for multimodal pulsatile haemodynamic signals. We have developed the algorithm using ABP and CBv measurements from a dataset that contained combinations of several time-shifts. We validated the algorithm on an external dataset with time-shifts. We additionally quantitatively validated the algorithm's performance on a dataset with artificially added time-shifts, consisting of sample clock differences ranging from -0.2 to 0.2 s/min and sudden time-shifts between -4 and 4 s. The influence of superimposed noise and variation in waveform morphology on the time-shift estimation was quantified, and their influence on DCA-indices was determined. RESULTS: The instantaneous median absolute error (MedAE) between the artificially added time-shifts and the estimated time-shifts was 12 ms (median, IQR 12-12, range 11-14 ms) for drifts between -0.1 and 0.1 s/min and sudden time-shifts between -4 and 4 s. For drifts above 0.1 s/min, MedAE was higher (median 753, IQR 19 - 766, range 13 - 772 ms). When a certainty threshold was included (peak cross-correlation > 0.9), MedAE for all drifts-shift combinations decreased to 12 ms, with smaller variability (IQR 12 - 13, range 8 - 22 ms, p < 0.001). The time-shift estimation is robust to noise, as the MedAE was similar for superimposed white noise with variance equal to the signal variance. After time-shift correction, DCA-indices were similar to the original, non-time-shifted signals. Phase shift differed by 0.17° (median, IQR 0.13-0.2°, range 0.0038-1.1°) and 0.54° (median, IQR 0.23-1.7°, range 0.0088-5.6°) for the very low frequency and low frequency ranges, respectively. DISCUSSION: This algorithm allows visually interpretable detection and accurate correction of time-shifts between pulsatile haemodynamic signals (ABP and CBv).

High parasite diversity maintained after an alga-virus coevolutionary arms race.

Arms race dynamics are a common outcome of host-parasite coevolution. While they can theoretically be maintained indefinitely, realistic arms races are expected to be finite. Once an arms race has ended, for example due to the evolution of a generalist-resistant host, the system may transition into coevolutionary dynamics that favour long-term diversity. In microbial experiments, host-parasite arms races often transition into a stable coexistence of generalist-resistant hosts, (semi-)susceptible hosts, and parasites. While long-term host diversity is implicit in these cases, parasite diversity is usually overlooked. In this study, we examined parasite diversity after the end of an experimental arms race between a unicellular alga (Chlorella variabilis) and its lytic virus (PBCV-1). First, we isolated virus genotypes from multiple time points from two replicate microcosms. A time-shift experiment confirmed that the virus isolates had escalating host ranges, i.e., that arms races had occurred. We then examined the phenotypic and genetic diversity of virus isolates from the post-arms race phase. Post-arms race virus isolates had diverse host ranges, survival probabilities, and growth rates; they also clustered into distinct genetic groups. Importantly, host range diversity was maintained throughout the post-arms race phase, and the frequency of host range phenotypes fluctuated over time. We hypothesize that this dynamic polymorphism was maintained by a combination of fluctuating selection and demographic stochasticity. Together with previous work in prokaryotic systems, our results link experimental observations of arms races to natural observations of long-term host and parasite diversity.

Cholinergic interneurons in the dorsal striatum play an important role in the acquisition of duration memory.

The present study aimed to examine the effect of cholinergic interneuron lesions in the dorsal striatum on duration-memory formation. Cholinergic interneurons in the dorsal striatum may be involved in the formation of duration memory since they are among the main inputs to the dorsal striatal muscarinic acetylcholine-1 receptors, which play a role in the consolidation of duration memory. Rats were sufficiently trained using a peak-interval 20 s procedure and then infused with anti-choline acetyltransferase-saporin into the dorsal striatum to cause selective ablation of cholinergic interneurons. To make the rats acquire new duration-memories, we trained them with a peak interval 40 s after lesion. Before lesion, the peak times (an index of duration memory) for sham-lesioned and lesioned groups were similar at approximately 20 s. In the peak interval 40 s session, the peak times for the sham-lesioned and lesioned groups were approximately 30 and 20 s, respectively. After additional peak interval 40 s sessions, the peak times of both groups were shifted to approximately 40 s. Those results suggest that the cholinergic interneuron lesion delayed new duration-memory acquisition. Subsequent experiments showed that cholinergic interneuron lesions did not retard the shift of peak time to the original target time (20 s). Following experiment without changing the target time after lesion showed that cholinergic interneuron lesions did not change their peak times. Our findings suggest that cholinergic interneurons in the dorsal striatum are involved in new duration-memory acquisition but not in the utilization of already acquired duration memory and interval timing.

Do biological control agents adapt to local pest genotypes? A multiyear test across geographic scales.

Parasite local adaptation has been a major focus of (co)evolutionary research on host-parasite interactions. Studies of wild host-parasite systems frequently find that parasites paired with local, sympatric host genotypes perform better than parasites paired with allopatric host genotypes. In contrast, there are few such tests in biological control systems to establish whether biological control parasites commonly perform better on sympatric pest genotypes. This knowledge gap prevents the optimal design of biological control programs: strong local adaptation could argue for the use of sympatric parasites to achieve consistent pest control. To address this gap, we tested for local adaptation of the biological control bacterium Pasteuria penetrans to the root-knot nematode Meloidogyne arenaria, a global threat to a wide range of crops. We measured the probability and intensity of P. penetrans infection on sympatric and allopatric M. arenaria over the course of 4 years. Our design accounted for variation in adaptation across scales by conducting tests within and across fields, and we isolated the signature of parasite adaptation by comparing parasites collected over the course of the growing season. Our results are largely inconsistent with local adaptation of P. penetrans to M. arenaria: in 3 of 4 years, parasites performed similarly well in sympatric and allopatric combinations. In 1 year, however, infection probability was 28% higher for parasites paired with hosts from their sympatric plot, relative to parasites paired with hosts from other plots within the same field. These mixed results argue for population genetic data to characterize the scale of gene flow and genetic divergence in this system. Overall, our findings do not provide strong support for using P. penetrans from local fields to enhance biological control of Meloidogyne.

[Alignment method for metabolite chromatographic peaks using an N-acyl glycine retention index system].

Peak alignment is a crucial data-processing step in untargeted metabolomics analysis that aims to integrate metabolite data from multiple liquid chromatography-mass spectrometry (LC-MS) batches for enhanced comparability and reliability. However, slight variations in the chromatographic separation conditions can result in retention time (RT) shifts between consecutive analyses, adversely affecting peak alignment accuracy. In this study, we present a retention index (RI)-based chromatographic peak-shift correction (CPSC) strategy to address RT shifts and align chromatographic peaks for metabolomics studies. A series of N-acyl glycine homologues (C2-C23) was synthesized as calibrants, and an LC RI system was established. This system effectively corrected RT shifts arising from variations in flow rate, gradient elution, instrument systems, and chromatographic columns. Leveraging the RI system, we successfully adjusted the RT of raw data to mitigate RT shifts and then implemented the Joint Aligner algorithm for peak alignment. We assessed the accuracy of the RI-based CPSC strategy using pooled human fecal samples as a test model. Notably, the application of the RI-based CPSC strategy to a long-term dataset spanning 157 d as an illustration revealed a significant enhancement in peak alignment accuracy from 15.5% to 80.9%, indicating its ability to substantially improve peak-alignment precision in multibatch LC-MS analyses.

Distinct hemodynamic and functional connectivity features of fatigue in clinically isolated syndrome and multiple sclerosis: accounting for the confounding effect of concurrent depression symptoms.

PURPOSE: This study aims to identify common and distinct hemodynamic and functional connectivity (FC) features for self-rated fatigue and depression symptoms in patients with clinically isolated syndrome (CIS) and relapsing-remitting multiple sclerosis (RR-MS). METHODS: Twenty-four CIS, 29 RR-MS patients, and 39 healthy volunteers were examined using resting-state fMRI (rs-fMRI) to obtain whole-brain maps of (i) hemodynamic response patterns (through time shift analysis), (ii) FC (via intrinsic connectivity contrast maps), and (iii) coupling between hemodynamic response patterns and FC. Each regional map was correlated with fatigue scores, controlling for depression, and with depression scores, controlling for fatigue. RESULTS: In CIS patients, the severity of fatigue was associated with accelerated hemodynamic response in the insula, hyperconnectivity of the superior frontal gyrus, and evidence of reduced hemodynamics-FC coupling in the left amygdala. In contrast, depression severity was associated with accelerated hemodynamic response in the right limbic temporal pole, hypoconnectivity of the anterior cingulate gyrus, and increased hemodynamics-FC coupling in the left amygdala. In RR-MS patients, fatigue was associated with accelerated hemodynamic response in the insula and medial superior frontal cortex, increased functional role of the left amygdala, and hypoconnectivity of the dorsal orbitofrontal cortex, while depression symptom severity was linked to delayed hemodynamic response in the medial superior frontal gyrus; hypoconnectivity of the insula, ventromedial thalamus, dorsolateral prefrontal cortex, and posterior cingulate; and decreased hemodynamics-FC coupling of the medial orbitofrontal cortex. CONCLUSION: There are distinct FC and hemodynamic responses, as well as different magnitude and topography of hemodynamic connectivity coupling, associated with fatigue and depression in early and later stages of MS.

Spatio-temporal PV power forecasting considering the time-shift correction and the information fusion strategy of multi-stations.

Accurate prediction of PV power is essential to ensuring the safe and economic operation of power systems with high PV penetration. The current PV power prediction scheme considering the spatio-temporal correlation characteristics is relatively simple in data processing, resulting in low prediction accuracy; at the same time, the missing data also poses a great problem to the prediction. Therefore, in order to improve the prediction accuracy and solve the problem of missing data, this paper proposes a PV power spatio-temporal prediction model considering time-shift correction and a multi-station information fusion strategy Firstly, relevant power station clusters are constructed using hierarchical clustering, and a similar daily data filtering model considering the variation characteristics of daily power characteristic curves is proposed to filter the data; Secondly, multiple BP neural network models are constructed and multiple reference power stations with high relevance are predicted using irradiance information; Thirdly, the prediction results of multiple reference power stations are input to the data processing module for time-shift analysis and spatial correlation information fusion correction, which solves the missing data problem of the target power station to be predicted. Finally, it is input to One-dimensional Convolutional Neural Network(1DCNN) to achieve the power prediction of the target power station with missing data. The simulation analysis shows that the root mean square error (RMSE) of a sunny day forecast is 3.31%; the RMSE of a non-sunny day forecast is 9.65%, which proves the accuracy of this two-layer neural network is higher compared to other model structures, so the proposed scheme has certain reliability and accuracy in the prediction of PV power with missing data.

Converging evidence of impaired brain function in systemic lupus erythematosus: changes in perfusion dynamics and intrinsic functional connectivity.

PURPOSE: Τhe study examined changes in hemodynamics and functional connectivity in patients with systemic lupus erythematosus (SLE) with or without neuropsychiatric manifestations. METHODS: Participants were 44 patients with neuropsychiatric SLE (NPSLE), 20 SLE patients without such manifestations (non-NPSLE), and 35 healthy controls. Resting-state functional MRI (rs-fMRI) was used to obtain whole-brain maps of (a) perfusion dynamics derived through time shift analysis (TSA), (b) regional functional connectivity (intrinsic connectivity contrast (ICC) coefficients), and (c) hemodynamic-connectivity coupling. Group differences were assessed through independent samples t-tests, and correlations of rs-fMRI indices with clinical variables and neuropsychological test scores were, also, computed. RESULTS: Compared to HC, NPSLE patients demonstrated intrinsic hypoconnectivity of anterior Default Mode Network (DMN) and hyperconnectivity of posterior DMN components. These changes were paralleled by elevated hemodynamic lag. In NPSLE, cognitive performance was positively related to higher intrinsic connectivity in these regions, and to higher connectivity-hemodynamic coupling in posterior DMN components. Uncoupling between hemodynamics and connectivity in the posterior DMN was associated with worse task performance. Non-NPSLE patients displayed hyperconnectivity in posterior DMN and sensorimotor regions paralleled by relatively increased hemodynamic lag. CONCLUSION: Adaptation of regional brain function to hemodynamic changes in NPSLE may involve locally decreased or locally increased intrinsic connectivity (which can be beneficial for cognitive function). This process may also involve elevated coupling of hemodynamics with functional connectivity (beneficial for cognitive performance) or uncoupling, which may be detrimental for the cognitive skills of NPSLE patients.

Multiyear Time-Shift Study of Bacteria and Phage Dynamics in the Phyllosphere.

AbstractCoevolution shapes diversity within and among populations but is difficult to study directly. Time-shift experiments, where individuals from one point in time are experimentally challenged against individuals from past, contemporary, and/or future time points, are a powerful tool to measure coevolution. This approach has proven useful both in directly measuring coevolutionary change and in distinguishing among coevolutionary models. However, these data are only as informative as the time window over which they were collected, and inference from shorter coevolutionary windows might conflict with those from longer time periods. Previous time-shift experiments from natural microbial communities of horse chestnut tree leaves uncovered an apparent asymmetry, whereby bacterial hosts were more resistant to bacteriophages from all earlier points in the growing season, while phages were most infective to hosts from only the recent past. Here, we extend the time window over which these infectivity and resistance ranges are observed across years and confirm that the previously observed asymmetry holds over longer timescales. These data suggest that existing coevolutionary theory should be revised to include the possibility of differing models for hosts and their parasites and examined for how such asymmetries might reshape the predicted outcomes of coevolution.

A new platform for untargeted UHPLC-HRMS data analysis to address the time-shift problem.

Substantial deviations in retention times among samples pose a great challenge for the accurate screening and identifying of metabolites by ultrahigh-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS). In this study, a coarse-to-refined time-shift correction methodology was proposed to efficiently address this problem. Metabolites producing multiple fragment ions were automatically selected as landmarks to generate pseudo-mass spectra for a coarse time-shift correction. Refined peak alignment for extracted ion chromatograms was then performed by using a moving window-based multiple-peak alignment strategy. Based on this novel coarse-to-refined time-shift correction methodology, a new comprehensive UHPLC-HRMS data analysis platform was developed for UHPLC-HRMS-based metabolomics. Original datasets were employed as inputs to automatically extract and register features in the dataset and to distinguish fragment ions from metabolites for chemometric analysis. Its performance was further evaluated using complex datasets, and the results suggest that the new platform can satisfactorily resolve the time-shift problem and is comparable with commonly used UHPLC-HRMS data analysis tools such as XCMS Online, MS-DIAL, Mzmine2, and Progenesis QI. The new platform can be downloaded from: http://www.pmdb.org.cn/antdas2tsc.

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve.

In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving the serious modal aliasing problem of local mean decomposition (LMD) and the dependence of permutation entropy on the length of the original time series. First, by adding a sine wave with a uniform phase as a masking signal, adaptively selecting the amplitude of the added sine wave, the optimal decomposition result is screened by the orthogonality and the signal is reconstructed based on the kurtosis value to remove the signal noise. Secondly, in the RTSMWPE method, the fault feature extraction is realized by considering the signal amplitude information and replacing the traditional coarse-grained multi-scale method with a time-shifted multi-scale method. Finally, the proposed method is applied to the analysis of the experimental data of the reciprocating compressor valve; the analysis results demonstrate the effectiveness of the proposed method.

Intra-dorsal striatal acetylcholine M1 but not dopaminergic D1 or glutamatergic NMDA receptor antagonists inhibit consolidation of duration memory in interval timing.

The striatal beat frequency model assumes that striatal medium spiny neurons encode duration via synaptic plasticity. Muscarinic 1 (M1) cholinergic receptors as well as dopamine and glutamate receptors are important for neural plasticity in the dorsal striatum. Therefore, we investigated the effect of inhibiting these receptors on the formation of duration memory. After sufficient training in a peak interval (PI)-20-s procedure, rats were administered a single or mixed infusion of a selective antagonist for the dopamine D1 receptor (SCH23390, 0.5 µg per side), N-methyl-D-aspartic acid (NMDA)-type glutamate receptor (D-AP5, 3 µg), or M1 receptor (pirenzepine, 10 µg) bilaterally in the dorsal striatum, immediately before initiating a PI-40 s session (shift session). The next day, the rats were tested for new duration memory (40 s) in a session in which no lever presses were reinforced (test session). In the shift session, the performance was comparable irrespective of the drug injected. However, in the test session, the mean peak time (an index of duration memory) of the M1 + NMDA co-blockade group, but not of the D1 + NMDA co-blockade group, was lower than that of the control group (Experiments 1 and 2). In Experiment 3, the effect of the co-blockade of M1 and NMDA receptors was replicated. Moreover, sole blockade of M1 receptors induced the same effect as M1 and NMDA blockade. These results suggest that in the dorsal striatum, the M1 receptor, but not the D1 or NMDA receptors, is involved in the consolidation of duration memory.

Filtering-induced time shifts in photoplethysmography pulse features measured at different body sites: the importance of filter definition and standardization.

Objective.The waveform of a photoplethysmography (PPG) signal depends on the measurement site and individual physiological conditions. Filtering can distort the morphology of the original PPG signal waveform and change the timing of pulse feature points on PPG signals. We aim to quantitatively investigate the effect of PPG signal morphology (related to measurement site) and type of pulse feature on the filtering-induced time shift (TS).Approach.60 s PPG signals were measured from six body sites (finger, wrist under (volar), wrist upper (dorsal), earlobe, and forehead) of 36 healthy adults. Using infinite impulse response digital filters which are common in PPG signal processing, PPG signals were prefiltered (band-pass, pass and stop bands: >0.5 Hz and <0.2 Hz for high-pass filter, <20 Hz and >30 Hz for low-pass filter) and then filtered (low-pass, pass and stop bands: <3 Hz and >5 Hz). Four pulse feature points were defined and extracted (peak, valley, maximal first derivative, and maximal second derivative). For each subject, overall TS and intra-subject TS variability in feature points were calculated as the mean and standard deviation of TS between prefiltered and filtered PPG signals in 50 cardiac cycles. Statistical testing was performed to investigate the effect of measurement site and type of pulse feature on overall TS and intra-subject TS variability.Main results.Measurement site, type of pulse feature, and their interaction had significant impacts on the overall TS and intra-subject TS variability (p < 0.001 for all). Valley and maximal second derivative showed higher overall TS than peak and maximal first derivative. Finger had higher overall TS and lower intra-subject TS variability than other measurement sites.Significance. Measurement site and type of pulse feature can significantly influence the timing of feature points on filtered PPG signals. Filtering parameters should be quoted to support the reproducibility of PPG-related studies.

The dorsal hippocampus is required for the formation of long-term duration memories in rats.

Interval timing-the perception of durations mainly in seconds or minutes-is a ubiquitous behavior in organisms. Animal studies have suggested that the hippocampus plays an essential role in duration memory; however, the memory processes involved are unclear. To clarify the role of the dorsal hippocampus in the acquisition of long-term duration memories, we adapted the "time-shift paradigm" to a peak-interval procedure. After a sufficient number of training with an initial target duration (20 s), the rats underwent "shift sessions" with a new target duration (40 s) under a muscimol (0.5 µg per side) infusion into the bilateral dorsal hippocampus. The memory of the new target duration was then tested in drug-free "probe sessions," including trials in which no lever presses were reinforced. In the probe sessions, the mean response rate distribution of the muscimol group was located leftward to the control group, but these two response rate distributions were superimposed on the standardized time axis, suggesting a scalar property. In the session-by-session analysis, the mean peak time (an index of timing accuracy) of the muscimol group was lower than that of the control group in the probe sessions, but not in the shift sessions. These findings suggest that the dorsal hippocampus is required for the formation of long-term duration memories within the range of interval timing.

Evidence of Age-Related Hemodynamic and Functional Connectivity Impairment: A Resting State fMRI Study.

Purpose: To assess age-related changes in intrinsic functional brain connectivity and hemodynamics during adulthood in the context of the retrogenesis hypothesis, which states that the rate of age-related changes is higher in late-myelinating (prefrontal, lateral-posterior temporal) cerebrocortical areas as compared to early myelinating (parietal, occipital) regions. In addition, to examine the dependence of age-related changes upon concurrent subclinical depression symptoms which are common even in healthy aging. Methods: Sixty-four healthy adults (28 men) aged 23-79 years (mean 45.0, SD = 18.8 years) were examined. Resting-state functional MRI (rs-fMRI) time series were used to compute voxel-wise intrinsic connectivity contrast (ICC) maps reflecting the strength of functional connectivity between each voxel and the rest of the brain. We further used Time Shift Analysis (TSA) to estimate voxel-wise hemodynamic lead or lag for each of 22 ROIs from the automated anatomical atlas (AAL). Results: Adjusted for depression symptoms, gender and education level, reduced ICC with age was found primarily in frontal, temporal regions, and putamen, whereas the opposite trend was noted in inferior occipital cortices (p < 0.002). With the same covariates, increased hemodynamic lead with advancing age was found in superior frontal cortex and thalamus, with the opposite trend in inferior occipital cortex (p < 0.002). There was also evidence of reduced coupling between voxel-wise intrinsic connectivity and hemodynamics in the inferior parietal cortex. Conclusion: Age-related intrinsic connectivity reductions and hemodynamic changes were demonstrated in several regions-most of them part of DMN and salience networks-while impaired neurovascular coupling was, also, found in parietal regions. Age-related reductions in intrinsic connectivity were greater in anterior as compared to posterior cortices, in line with implications derived from the retrogenesis hypothesis. These effects were affected by self-reported depression symptoms, which also increased with age.

Comparison of the Characteristics of Coronary Interventions Performed During Day and Night Shifts in Patients with Acute Myocardial Infarction.

Therapeutic percutaneous coronary intervention (PCI) is the treatment of choice in acute myocardial infarction (AMI). If optimally performed, PCI reduces myocardial injury and improves the likelihood of a positive clinical outcome. Therefore, the equal quality of PCI throughout both day and night shifts is of paramount importance. Our aim was to compare urgent diagnostic and therapeutic coronary interventions performed during day and night shifts. We retrospectively analyzed the medical records of 144 patients who underwent coronary angiography for AMI over six months in a tertiary referral center working in 24/7 mode. The patients' characteristics, procedural data and the operator's experience in interventional cardiology were compared according to the time of intervention during a day shift (8 a.m. until 8 p.m., group A, n = 106) and night shift (from 8 p.m. until 8 a.m. next day, group B, n = 36). The baseline characteristics of the subjects of groups A and B were similar, except for a higher proportion of AMI without persistent ST-segment elevation (NSTEMI) in patients who underwent coronary angiography during regular working hours compared to off-hours (58% vs. 34%, p < 0.05). The average time of diagnostic coronary angiography was longer by about 5 min during the day shift (28.5 ± 12.2 vs. 23.8 ± 8.9 min, p < 0.05), while other procedural data, including the arterial access route, the number of catheters needed and the contrast-medium volume, were similar. The use of additional diagnostic tools for coronary lesion assessment (intracoronary ultrasound or fractional flow reserve measurement) was almost twice as frequent during regular working hours (15% vs. 8%). Urgent therapeutic PCI on the culprit artery was performed in 79% and 89% of group A and B patients, respectively. The groups did not differ in procedural characteristics regarding the total interventional session, including both diagnostic angiography and therapeutic PCI, such as total procedure duration, fluoroscopy time, radiation dose, stenting technique and total stent length. Coronary thrombectomy or rotational atherectomy were more frequently used in group A (27% vs. 15%, p = 0.16). The percentage of doctors with the least experience in interventional cardiology was, albeit insignificantly, lower during day shifts (31% vs. 42%). In conclusion, the majority of clinical and periprocedural characteristics appeared to be independent of intervention time, except for a longer duration of diagnostic coronary angiography during daytime. This finding could probably result from a higher proportion of NSTEMI patients frequently requiring additional angiographic projections and special techniques to properly identify the infarct-related artery during the day shift. Whether a tendency of less frequent use of additional tools at off-hours may also be due to a lower percentage of NSTEMI interventions at night, or whether this can be linked to lower availability of experienced operators, remains to be validated in a large study. The latter possibility, if confirmed, might encourage public health authorities and healthcare organizers to improve off-hours cathlab staffing with experienced interventionalists. Finally, additional obligatory training in special diagnostic and therapeutic invasive techniques might be advisable for the least experienced operators scheduled to work night shifts.

Measuring Coevolutionary Dynamics in Species-Rich Communities.

Identifying different types of coevolutionary dynamics is important for understanding biodiversity and infectious disease. Past work has often focused on pairs of interacting species, but observations of extant communities suggest that coevolution in nature occurs in networks of antagonism and mutualism. We discuss challenges for measuring coevolutionary dynamics in species-rich communities, and we suggest ways that established approaches used for two-species interactions can be applied. We propose ways that such data can be complemented by genomic information and linked back to extant communities via network structure, and we suggest avenues for new theoretical work to strengthen these connections. Quantifying coevolution in species-rich communities has several potential benefits, such as identifying coevolutionary units within networks and uncovering coevolutionary interactions among pathogens of humans, livestock, and crops.

A simple method for correcting for the Will Rogers phenomenon with biometrical applications.

In its basic form, the Will Rogers phenomenon takes place when an increase in the average value of each of two sets is achieved by moving an element from one set to another. This leads to the conclusion that there has been an improvement, when in fact essentially nothing has changed. Extended versions of this phenomenon can occur in epidemiological studies, rendering their results unreliable. After describing epidemiological and clinical studies that have been affected by the Will Rogers phenomenon, this paper presents a simple method to correct for it. The method involves introducing a transition matrix between the two sets and taking probability weighted expectations. Two real-world biometrical examples, based on migration economics and breast cancer epidemiology, are given and improvements against a naïve analysis are demonstrated. In the cancer epidemiology example, we take account of estimation uncertainty. We also discuss briefly some limitations associated with our method.

Predicting the course of Alzheimer's progression.

Alzheimer's disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer's dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer's Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only.