Entire Dataset Research Articles

Multifractal and chaotic characteristics of geomagnetic inter-storm interval over eight solar cycles

The structure and characteristics of geomagnetic inter-storm intervals, the sequence of time differences between two successive storm events, can provide information about the origin and drivers of the storms compared to analyzing the entire data set. In this study, the inter-storm event was investigated using the Kp index over 8 solar cycles (17–24). Three nonlinear approaches were considered: burstiness, multifractal detrended fluctuation analysis, and chaos analysis. The burstiness analysis reveals that the inter-storm intervals are heterogeneous events with irregular time intervals. The multifractal strength of inter-storm intervals ranges from 0.23 to 0.65 for each of the solar cycles, compared to 0.38 for the entire period. Long-range correlations largely drove the multifractality across most solar cycles. Chaos analysis confirmed the low predictability of inter-storm intervals across each solar cycle. The results will be relevant to the prediction and understanding of drivers of storm events across different solar cycles.

Automated detection of vertebral body misalignments in orthogonal kV and MV guided radiotherapy: application to a comprehensive retrospective dataset

Objective. In image-guided radiotherapy (IGRT), off-by-one vertebral body misalignments are rare but potentially catastrophic. In this study, a novel detection method for such misalignments in IGRT was investigated using densely-connected convolutional networks (DenseNets) for applications towards real-time error prevention and retrospective error auditing. Approach. A total of 4213 images acquired from 527 radiotherapy patients aligned with planar kV or MV radiographs were used to develop and test error-detection software modules. Digitally reconstructed radiographs (DRRs) and setup images were retrieved and co-registered according to the clinically applied alignment contained in the DICOM REG files. A semi-automated algorithm was developed to simulate patient positioning errors on the anterior-posterior (AP) and lateral (LAT) images shifted by one vertebral body. A DenseNet architecture was designed to classify either AP images individually or AP and LAT image pairs. Receiver-operator characteristic curves (ROC) and areas under the curves (AUC) were computed to evaluate the classifiers on test subsets. Subsequently, the algorithm was applied to the entire dataset in order to retrospectively determine the absolute off-by-one vertebral body error rate for planar radiograph guided RT at our institution from 2011–2021. Main results. The AUCs for the kV models were 0.98 for unpaired AP and 0.99 for paired AP-LAT. The AUC for the MV AP model was 0.92. For a specificity of 95%, the paired kV model achieved a sensitivity of 99%. Application of the model to the entire dataset yielded a per-fraction off-by-one vertebral body error rate of 0.044% [0.0022%, 0.21%] for paired kV IGRT including one previously unreported error. Significance. Our error detection algorithm was successful in classifying vertebral body positioning errors with sufficient accuracy for retrospective quality control and real-time error prevention. The reported positioning error rate for planar radiograph IGRT is unique in being determined independently of an error reporting system.

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees.

Hundreds or thousands of loci are now routinely used in modern phylogenomic studies. Concatenation approaches to tree inference assume that there is a single topology for the entire dataset, but different loci may have different evolutionary histories due to incomplete lineage sorting (ILS), introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce an implementation of a multi-tree mixture model that we call mixtures across sites and trees (MAST). This model extends a prior implementation by Boussau et al. (2009) by allowing users to estimate the weight of each of a set of pre-specified bifurcating trees in a single alignment. The MAST model allows each tree to have its own weight, topology, branch lengths, substitution model, nucleotide or amino acid frequencies, and model of rate heterogeneity across sites. We implemented the MAST model in a maximum-likelihood framework in the popular phylogenetic software, IQ-TREE. Simulations show that we can accurately recover the true model parameters, including branch lengths and tree weights for a given set of tree topologies, under a wide range of biologically realistic scenarios. We also show that we can use standard statistical inference approaches to reject a single-tree model when data are simulated under multiple trees (and vice versa). We applied the MAST model to multiple primate datasets and found that it can recover the signal of ILS in the Great Apes, as well as the asymmetry in minor trees caused by introgression among several macaque species. When applied to a dataset of 4 Platyrrhine species for which standard concatenated maximum likelihood (ML) and gene tree approaches disagree, we observe that MAST gives the highest weight (i.e., the largest proportion of sites) to the tree also supported by gene tree approaches. These results suggest that the MAST model is able to analyze a concatenated alignment using ML while avoiding some of the biases that come with assuming there is only a single tree. We discuss how the MAST model can be extended in the future.

Validation of Population Pharmacokinetic Models for Clozapine Dosage Prediction.

Clozapine is unique in its capacity to ameliorate severe schizophrenia but at high risk of toxicity. A relationship between blood concentration and clinical response and evidence for concentration-response relationships to some adverse effects justify therapeutic drug monitoring of clozapine. However, the relationship between drug dose and blood concentration is quite variable. This variability is, in part, due to inductive and inhibitory interactions varying the activity of cytochrome P450 1A2 (CYP1A2), the principal pathway for clozapine elimination. Several population pharmacokinetic models have been presented to facilitate dose selection and to identify poor adherence in individual patients. These models have faced little testing for validity in independent populations or even for persisting validity in the source population. Therefore, we collected a large population of clozapine-treated patients (127 patients, 1048 timed plasma concentrations) in whom dosing and covariate information could be obtained with high certainty. A population pharmacokinetic model was constructed with data collected in the first 6 weeks from study enrolment (448 plasma concentrations), to estimate covariate influences and to allow alignment with previously published models. The model was tested for its performance in predicting the concentrations observed at later time intervals up to 5 years. The predictive performances of 6 published clozapine population models were then assessed in the entire population. The population pharmacokinetic model based on the first 6 weeks identified significant influences of sex, smoking, and cotreatment with fluvoxamine on clozapine clearance. The model built from the first 6 weeks had acceptable predictive performance in the same patient population up to the first 26 weeks using individual parameters, with a median predictive error (PE) of -0.1% to -15.9% and median absolute PE of 22.9%-27.1%. Predictive performance fell progressively with time after 26 weeks. Bayesian addition of plasma concentration observations within each prediction period improved individual predictions. Three additional observations extended acceptable predictive performance into the second 6 months of therapy. When the published models were tested with the entire data set, median PE ranged from -8% to +35% with a median absolute PE of >39% in all models. Thus, none of the tested models was successful in external validation. Bayesian addition of single patient observations improved individual predictions from all models but still without achieving acceptable performances. We conclude that the relationship between covariates and blood clozapine concentrations differs between populations and that relationships are not stable over time within a population. Current population models for clozapine are not capturing influential covariates.

Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards

Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables. This retrospective observational study included patients under the age of 18 who were admitted to the general ward of a tertiary children’s hospital between 2020 and 2022. A critical event was defined as cardiopulmonary resuscitation, unplanned transfer to the intensive care unit, or mortality. The vital signs measured during hospitalization, their measurement intervals, sex, and age were used to train a critical event prediction model. Age-specific z-scores were used to normalize the variability of the normal range by age. The entire dataset was classified into a training dataset and a test dataset at an 8:2 ratio, and model learning and testing were performed on each dataset. The predictive performance of the developed model showed excellent results, with an area under the receiver operating characteristics curve of 0.986 and an area under the precision-recall curve of 0.896. We developed a deep learning model with outstanding predictive power using simplified variables to effectively predict critical events while reducing the workload of medical staff. Nevertheless, because this was a single-center trial, no external validation was carried out, prompting further investigation.

Integration of electromagnetic, resistivity-based and production logging data for validating lithofacies and permeability predictive models with tree ensemble algorithms in heterogeneous carbonate reservoirs

This study develops an innovative workflow to identify discrete lithofacies distributions with respect to the well-log records exploiting two tree-based ensemble learning algorithms: extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). In the next step, the predicted discrete lithofacies distribution is further assessed with well-log data using an XGBoost regression to predict reservoir permeability. The input well-logging records are gamma ray, neutron porosity, bulk density, compressional slowness, and deep and shallow resistivity. These data originate from a carbonate reservoir in the Mishrif Basin of southern Iraq's oilfield. To achieve a solid prediction of lithofacies permeability, random subsampling cross-validation was applied to the original dataset to formulate two subsets: training for model tuning and testing for the prediction of subsets that are not observed during the model training. The values for the total correct percentage (TCP) of lithofacies predictions for the entire dataset and testing subset were 98 and 93% using the XGBoost algorithm, and 97 and 89% using the AdaBoost classifier, respectively. The XGBoost predictive models led in attaining the least uncertain lithofacies and permeability records for the cored data. For further validation, the predicted lithofacies and reservoir permeability were then compared with porosity–permeability values derived from the nuclear magnetic resonance (NMR) log, the secondary porosity of the full-bore micro imager (FMI) and the production contribution from the production–logging tool (PLT). Therefore, it is believed that the XGBoost model is capable of making accurate predictions of lithofacies and permeability for the same well's non-cored intervals and other non-cored wells in the investigated reservoir.

Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array

IntroductionBreath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. MethodsPatients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. ResultsSixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. ConclusionBreath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Plasma acylcarnitines and amino acids in dyslipidemia: An integrated metabolomics and machine learning approach.

The Discovery of underlying intermediates associated with the development of dyslipidemia results in a better understanding of pathophysiology of dyslipidemia and their modification will be a promising preventive and therapeutic strategy for the management of dyslipidemia. The entire dataset was selected from the Surveillance of Risk Factors of Noncommunicable Diseases (NCDs) in 30 provinces of Iran (STEPs 2016 Country report in Iran) that included 1200 subjects and was stratified into four binary classes with normal and abnormal cases based on their levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and non-HDL-C.Plasma concentrations of 20 amino acids and 30 acylcarnitines in each class of dyslipidemia were evaluated using Tandem mass spectrometry. Then, these attributes, along with baseline characteristics data, were used to check whether machine learning (ML) algorithms could classify cases and controls. Our ML framework accurately predicts TG binary classes. Among the models tested, the SVM model stood out, performing slightly better with an AUC of 0.81 and a standard deviation of test accuracy at 0.04. Consequently, it was chosen as the optimal model for TG classification. Moreover, the findings showed that alanine, phenylalanine, methionine, C3, C14:2, and C16 had great power in differentiating patients with high TG from normal TG controls. Conclusions: The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia. The online version contains supplementary material available at 10.1007/s40200-024-01384-9.

Assessing the Plastisphere from Floating Plastics in the Northwestern Mediterranean Sea, with Emphasis on Viruses.

Plastics in the ocean create the "plastisphere", a diverse habitat hosting various life forms. Other than the pollution induced by plastics, the co-occurrence of primary producers, symbiotic organisms, decomposers, and pathogens within the plastisphere raises questions about how they influence the dynamics of marine ecosystems. Here, we used a shotgun DNA-sequencing approach to describe the species thriving on floating plastics collected in two Mediterranean sites. Our findings revealed many species of bacteria, eukaryotes, viruses, and archaea on each plastic. Proteobacteria was dominant (70% of reads in the entire dataset), with other groups such as Ascomycota fungi (11%) and Bacteroidetes (9%) also being represented. The community structure was not affected by the polymeric composition or the plastic shape. Notably, pathogenic Vibrio species, including V. campbelli, V. alginolyticus, and V. coralliilyticus, were among the most abundant species. Viruses, despite showing lower relative abundances, occurred in all samples, especially Herpesvirales, Caudovirales, and Poxviridae groups. A significant finding was the presence of the White Spot Syndrome virus (WSSV). This pathogen, responsible for devastating outbreaks in aquaculture systems, had not been previously reported in the marine plastisphere. Our study emphasizes the need for further investigation into the ecological and economic impacts of plastisphere organisms in the ocean.

Mirror, mirror? An evaluation of identical twin mirroring in tooth crown morphology.

It has been estimated that 25% of monozygotic ("identical") twin pairs exhibit reverse asymmetry (RA) or "mirroring" of minor anatomical features as a result of delayed zygote division. Here, we examine whether identical twin mirroring accounts for patterns of dental asymmetry in a sample of monozygotic and dizygotic ("fraternal") twins. We focus on crown morphology to approach the following question: is there an association between dental RA frequency and twin type suggestive of the presence of mirror image twins in our sample? Data were collected from 208 deciduous and 196 permanent dentitions of participants of the University of Adelaide Twin Study using Arizona State University Dental Anthropology System standards. RA frequencies were compared across morphological complexes (deciduous, permanent), twin types (monozygotic, dizygotic), and traits. Fisher's exact tests were performed to formally evaluate the association between twin type and dental RA. Across the entire dataset, RA rates failed to exceed 8% for any twin type. In monozygotic twins, deciduous mirroring totaled 5.3% of observed cases, while permanent mirroring totaled 7.8% of observed cases. We found no statistically significant association between RA and twin type for any morphological character (p-value range: 0.07-1.00). Our results suggest the timing of monozygotic twin division does not explain the structure of asymmetry for our morphology dataset and that published estimates of identical twin mirroring rates may be inflated or contingent upon phenotype. Instead, rates reported for this sample more closely align with the proposed etiology of this condition.

Long-term temperature trend in the mesopause region according to observations of hydroxyl airglow in Zvenigorod

An analysis of the long-term change of the average annual OH* temperature, the values of which were obtained from nighttime spectral observations of the hydroxyl emission bands at the Zvenigorod scientific station (56° N, 37° E) from 1957 to 2022, is presented. This series of OH* temperatures, reflecting the thermal state of the mesopause region, is the longest among all known observations. On its basis, estimates of the linear trend and response of temperature to changes in solar activity was made both in general over the entire set of data and for different time intervals. In the first case, the trend was −0.23 ± 0.04 K/yr. In the second case, the analysis showed strong cooling (−0.53 ± 0.34 K/yr) until the 1970s and its slowdown to −0.14 ± 0.03 K/yr thereafter. A comparison of the results of the analysis with other measurements and model calculations shows that the latter have lower trend values. It is assumed that the reasons for the temperature trend, in addition to the growth of greenhouse gases, the main of which is CO2, may be long-term changes in the dynamics of the upper atmosphere.

Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering

IntroductionThe mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. MethodsMulti-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.Results and discussionThe method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.

Depression Detection of Users in Social-Media Twitter Using Decision Tree with Word2Vec

Social media is a medium or place on the Internet that allows users to be themselves. Interact, cooperate, share, and communicate with other users virtually. Not only do users share happy feelings, but they also share their emotions and sentiments towards a particular issue. Which sometimes makes users look depressed when they deliver it. Depression itself is the most commonly encountered mental illness, which makes the sufferer feel sad, lonely, inferior, and disconnected from the people around them. And even worse, depression can make the sufferer have suicidal thoughts. Therefore, we need to know whether the user indicated being depressed or not to prevent unwanted things by using a depression measurement tool scale called DASS 42 for data labeling. To detect depression, we can use the sufferer's Twitter account to take data based on tweets from the user and change the entire dataset to a vector using both the architectures of Word 2, Vec Skip-Gram, and CBOW. In this research, we utilize a decision tree to detect depression. The best results were obtained from the Word2Vec Skip-Gram model with a data ratio of 90:10 using the Gini criterion parameter and a maximum depth value of 20, resulting in an accuracy of 93% and a f1-score of 94%.

Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China.

The aim of this study was to develop and validate a series of breast cancer-related lymphoedema risk prediction models using machine learning algorithms for early identification of high-risk individuals to reduce the incidence of postoperative breast cancer lymphoedema. This was a retrospective study conducted from January 2012 to July 2022 in a tertiary oncology hospital. Subsequent to the collection of clinical data, variables with predictive capacity for breast cancer-related lymphoedema (BCRL) were subjected to scrutiny utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) technique. The entire dataset underwent a randomized partition into training and test subsets, adhering to a 7:3 distribution. Nine classification models were developed, and the model performance was evaluated based on accuracy, sensitivity, specificity, recall, precision, F-score, and area under curve (AUC) of the ROC curve. Ultimately, the selection of the optimal model hinged upon the AUC value. Grid search and 10-fold cross-validation was used to determine the best parameter setting for each algorithm. A total of 670 patients were investigated, of which 469 were in the modeling group and 201 in the validation group. A total of 174 had BCRL (25.97%). The LASSO regression model screened for the 13 features most valuable in predicting BCRL. The range of each metric in the test set for the nine models was, in order: accuracy (0.75-0.84), sensitivity (0.50-0.79), specificity (0.79-0.93), recall (0.50-0.79), precision (0.51-0.70), F score (0.56-0.69), and AUC value (0.71-0.87). Overall, LR achieved the best performance in terms of accuracy (0.81), precision (0.60), sensitivity (0.79), specificity (0.82), recall (0.79), F-score (0.68), and AUC value (0.87) for predicting BCRL. The study established that the constructed logistic regression (LR) model exhibits a more favorable amalgamation of accuracy, sensitivity, specificity, recall, and AUC value. This configuration adeptly discerns patients who are at an elevated risk of BCRL. Consequently, this precise identification equips nurses with the means to undertake timely and tailored interventions, thus averting the onset of BCRL.

Modeling equilibrium and non-equilibrium thermophysical properties of liquid lubricants using semi-empirical approaches and neural network

Abstract This study explored the capability of semi-empirical and neural network approaches for correlating and predicting some equilibrium and non-equilibrium thermophysical properties of liquid lubricants. The equilibrium properties, including the densities and several thermodynamic coefficients for 12 liquid lubricants, were correlated and predicted through a perturbed hard-chain equation of state (PHC EoS) by an attractive term of Yukawa tail. The molecular parameters of PHC EoS were obtained by correlating them with 935 data points for the densities and isothermal compressibilities of studied systems in the 278–353 K range and pressure up to 70 MPa with the average absolute relative deviations (AARDs) of 0.36 % and 5.25 %, respectively. Then, that EoS was employed to predict the densities of other literature sources (with an AARD of 0.81 %) along with several thermodynamic coefficients, including isobaric expansivities (with an AARD of 12.92 %), thermal pressure coefficients (with the AARD of 12.93 %), and internal pressure (with the AARD of 13.67 %), for which the reference values were obtained from Tait-type equations and available in literature. Apart from the equilibrium mentioned above properties, the PHC EoS was combined with a rough hard-sphere-chain (RHSC) model to correlate and predict the 548 data points for the viscosities of 7 selected liquefied lubricants in 283–353 K range and pressures up to 100 MPa with the AARD of 11.85 %. The accuracy of the results from the RHSC-based model has also been compared with an empirical PηT equation of Tammann-Tait type and an artificial neural network (ANN), both of which were developed in this work. The ANN of one hidden layer and 13 neurons was trained using the back-propagation algorithm. The results acquired from this approach were very promising and demonstrated the potential of the ANN approach for predicting the viscosity of lubricants, reaching an AARD of 0.81 % for the entire dataset.

Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau

Abstract. Evapotranspiration (ET) plays a crucial role in the water balance of the Tibetan Plateau (TP), often referred to as the “Asian water tower” region. However, accurately monitoring and comprehending the spatial and temporal variations of ET components (including soil evaporation Es, canopy transpiration Ec, and intercepted water evaporation Ew) in this remote area remains a significant challenge due to the limited availability of observational data. This study generates a 37-year dataset (1982–2018) of monthly ET components for the TP using the MOD16-STM (MOD16 soil texture model). This model utilizes up-to-date soil properties, meteorological data, and remote sensing datasets. The estimated ET results strongly correlate with measurements from nine flux towers, demonstrating a low root mean square error of 13.48 mm per month, a mean bias of 2.85 mm per month, a coefficient of determination of 0.83, and an index of agreement of 0.92. The annual average ET for the entire TP, defined as elevations higher than 2500 m, is approximately 0.93(± 0.037) × 103 Gt yr−1. The predominant contributor to ET on the TP is Es, accounting for 84 % of the total ET. Our findings reveal a noteworthy upward trend in ET in most central and eastern parts of the TP, with a rate of approximately 1–4 mm yr−1 (p<0.05) and a significant downward trend with rates between −3 and 1 mm yr−1 in the northwestern part of TP during the period from 1982 to 2018. The average annual increase in ET for the entire TP over the past 37 years is approximately 0.96 mm yr−1. This upward trend can be attributed to the TP's warming and wetting climate conditions. The MOD16-STM ET dataset demonstrates a reliable performance across the TP compared to previous research outcomes. This dataset is valuable for research on water resource management, drought monitoring, and ecological studies. The entire dataset is freely accessible through the Science Data Bank (https://doi.org/10.11922/sciencedb.00020, Ma et al., 2021) and the National Tibetan Plateau Data Center (TPDC) (https://doi.org/10.11888/Terre.tpdc.271913, Yuan, 2021).

Exploring the Role of Guidelines in Contributing to Medication Errors: A Descriptive Analysis of National Patient Safety Incident Data.

Clinical guidelines can contribute to medication errors but there is no overall understanding of how and where these occur. We aimed to identify guideline-related medication errors reported via a national incident reporting system, and describe types of error, stages of medication use, guidelines, drugs, specialties and clinical locations most commonly associated with such errors. Retrospective analysis of reports to the National Reporting and Learning System for England and Wales. A hierarchical task analysis (HTA) was developed, describing expected practice when using guidelines. A free-text search was conducted of medication incident reports (2016-2021) using search terms related to common guidelines. All identified reports linked to moderate-severe harm or death, and a random sample of 5100 no/low-harm reports were coded to describe deviations from the HTA. A random sample of 500 cases were independently double-coded. In total, 28,217 reports were identified, with 608 relating to moderate-severe harm or death. Fleiss' kappa for interrater reliability was 0.46. Of the 5708 reports coded, 642 described an HTA step discrepancy (including four linked to a death), suggesting over 3200 discrepancies in the entire dataset of 28,217 reports. Discrepancies related to finding guidelines (n=300 reports), finding information within guidelines (n=166) and using information (n=176). Discrepancies were most frequently identified for guidelines produced by a local organisation (n=405), and most occurred during prescribing (n=277) or medication administration (n=241). Difficulties finding and using information from clinical guidelines contribute to thousands of prescribing and medication administration incidents, some of which are associated with substantial patient harm.

Covering factor in AGNs: Evolution versus selection

Aims. In every proposed unification scheme for active galactic nuclei (AGNs), an integral element is the presence of circumnuclear dust arranged in torus-like structures, partially obscuring the nuclear (accretion-associated) radiation. A crucial model parameter in this context is the covering factor (CF), which can be defined as the ratio of the infrared luminosity of the dusty torus, LIR, to the accretion disk bolometric luminosity, Lagn. Recent research has discussed the potential redshift evolution of the CF. Our study aims to determine whether this observed evolution is genuine or if selection effects significantly influence it. Methods. Based on cross-matched multiwavelength photometrical data from the five major surveys (SDSS, GALEX, UKIDSS, WISE, Spitzer), a sample of over 17 000 quasars was derived. The main parameters of quasars, such as black hole masses and the Eddington ratios, were calculated based on the spectroscopic data. The data were divided into two redshift bins: low-z quasars (redshift from 0.7 to 1.1) and high-z quasars (from 2.0 to 2.4). The associated smaller datasets with higher quality data were constructed from the WISE W3 and W4 detections with S/N > 5 and the Spitzer MIPS 24 μm photometry. The CF was determined by computing the ratio of integrated luminosities, LIR and Lagn, using two methods: power-law fitting and the area between all photometric points. We explored different selection effects and their influence on CF estimates. Finally, statistical tests were employed to assess the hypothesis of CF evolution within the higher-quality datasets. Results. We identified an issue with the accuracy of the WISE W4 filter. Whenever feasible, it is recommended to utilize Spitzer MIPS 24 μm data. Luminosities obtained through direct integration of all photometric data points exhibit higher accuracy compared to values derived from a power-law approximation. The Efron & Petrosian test confirmed the presence of luminosity evolution with redshift for both LIR and Lagn. The low-z and high-z samples both exhibit a similar correlation between Lagn and LIR. The calculated median CF values are comparable within the errors: log CFlow-z = −0.18 ± 0.11 and log CFhigh-z = −0.01 ± 0.13. Additionally, the Spitzer photometry dataset reinforces this consistency with log CFlow-z = −0.19 ± 0.11 and log CFhigh-z = −0.18 ± 0.11. Conclusions. No discernible evolution of the CF was observed in the subsample of quasars with high supermassive black hole (SMBH) mass bin or high luminosities, as the CF values for low-z and high-z quasars have the same distributions. The relationship between LIR and Lagn deviates slightly from the expected 1:1 scaling, suggesting a more intricate connection between CF and Lagn. However, no statistically significant dependence of CF on luminosities could be claimed across the entire dataset (merged redshifts). It is worth noting that the low-z/low-luminosity portion of the CF distribution is influenced by contamination, possibly due to polar dust, as suggested in the literature, while the high-z/high-luminosity segment is affected by observational biases.

A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.

The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has presented significant challenges in the analysis and interpretation of these intricate datasets, leading to the development of specialized software that employs machine learning algorithms for these aims. In this paper, we analyze raw data taken from 16S rRNA gene sequencing from three studies, including stool samples from healthy control, patients with adenoma, and patients with colorectal cancer. Firstly, we use network-based methods to reduce dimensions of the dataset and consider only the most important features. In addition, we employ supervised machine learning algorithms to make prediction. Results show that graph-based techniques reduces dimen-sion from 255 up to 78 features with modularity score 0.73 based on different centrality measures. On the other hand, projection methods (non-negative matrix factorization and principal component analysis) reduce dimensions to 7 features. Furthermore, we apply supervised machine learning algorithms on the most important features obtained from centrality measures and on the ones obtained from projection methods, founding that the evaluation metrics have approximately the same scores when applying the algorithms on the entire dataset, on 78 feature and on 7 features. This study demonstrates the efficacy of graph-based and projection methods in the interpretation for 16S rRNA gene sequencing data. Supervised machine learning on refined features from both approaches yields comparable predictive performance, emphasizing specific microbial features-bacteroides, prevotella, fusobacterium, lysinibacillus, blautia, sphingomonas, and faecalibacterium-as key in predicting patient conditions from raw data.

Leakage detection based on variational mode decomposition and long short-term memory neural network

In the process of long-term continuous operation, fluid transportation pipelines are prone to leakage accidents. Therefore, this study investigates the detection of small-sized leaks with a leakage aperture of 13 mm in pipes with a diameter of 100 mm. The experimental investigation is conducted under the following operating conditions: volume flow of 25-80 m3/h, pressure of 100-200 kPa. The variations in volume flow and pressure signals during leak occurrences are analysed. To mitigate the interference caused by noise, the variational mode decomposition (VMD) method is introduced. The VMD effectively reduces noise interference in the signals. Furthermore, the denoised signals are utilized to establish a long short-term memory neural network (LSTM). The LSTM model achieves a high accuracy rate of 91.67% for the entire dataset.