Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children.

Importance: Acute otitis media (AOM) is a frequently diagnosed illness in children, yet the accuracy of diagnosis has been consistently low. Multiple neural networks have been developed to recognize the presence of AOM with limited clinical application. Objective: To develop and internally validate an artificial intelligence decision-support tool to interpret videos of the tympanic membrane and enhance accuracy in the diagnosis of AOM. Design, Setting, and Participants: This diagnostic study analyzed otoscopic videos of the tympanic membrane captured using a smartphone during outpatient clinic visits at 2 sites in Pennsylvania between 2018 and 2023. Eligible participants included children who presented for sick visits or wellness visits. Exposure: Otoscopic examination. Main Outcomes and Measures: Using the otoscopic videos that were annotated by validated otoscopists, a deep residual-recurrent neural network was trained to predict both features of the tympanic membrane and the diagnosis of AOM vs no AOM. The accuracy of this network was compared with a second network trained using a decision tree approach. A noise quality filter was also trained to prompt users that the video segment acquired may not be adequate for diagnostic purposes. Results: Using 1151 videos from 635 children (majority younger than 3 years of age), the deep residual-recurrent neural network had almost identical diagnostic accuracy as the decision tree network. The finalized deep residual-recurrent neural network algorithm classified tympanic membrane videos into AOM vs no AOM categories with a sensitivity of 93.8% (95% CI, 92.6%-95.0%) and specificity of 93.5% (95% CI, 92.8%-94.3%) and the decision tree model had a sensitivity of 93.7% (95% CI, 92.4%-94.9%) and specificity of 93.3% (92.5%-94.1%). Of the tympanic membrane features outputted, bulging of the TM most closely aligned with the predicted diagnosis; bulging was present in 230 of 230 cases (100%) in which the diagnosis was predicted to be AOM in the test set. Conclusions and Relevance: These findings suggest that given its high accuracy, the algorithm and medical-grade application that facilitates image acquisition and quality filtering could reasonably be used in primary care or acute care settings to aid with automated diagnosis of AOM and decisions regarding treatment.

The Attitudes and Practices Regarding COVID-19 among General Practitioners from Croatia and Bosnia and Herzegovina: A Cross-Sectional Study.

Attitudes toward COVID-19 (coronavirus disease 2019) prevention and control may have influenced general practitioners' (GPs') work during the COVID-19 pandemic. The present study aimed to investigate the attitudes and practices of GPs from Croatia and Bosna and Herzegovina regarding COVID-19 prevention and control as well as the factors which may have influenced them. A cross-sectional study using a self-administered, anonymous questionnaire was conducted between February and May of 2022 on 200 Croatian and Bosnian GPs. The study revealed that the attitudes and practices of the surveyed GPs regarding COVID-19 prevention and control were satisfactory. The Croatian GPs reported a larger number of positive attitudes toward COVID-19 prevention and control (p = 0.014), while no significant differences in practices were established. Among the Croatian GPs, more positive attitudes toward COVID-19 prevention and control were reported by participants who had finished a formal education on the prevention of infectious diseases and occupational safety (p = 0.018), while among the Bosnian GPs, more positive attitudes were reported by older GPs (p = 0.007), males (p = 0.026), GPs with a longer length of service (p = 0.005), GPs who had finished a formal education on the prevention of infectious diseases and occupational safety (p < 0.001), GPs who had finished a formal education on adequate hand hygiene (p < 0.001), and GPs who had finished a formal education on COVID-19 prevention for GPs (p = 0.001). Considering GPs' practices regarding COVID-19 prevention and control, among the Croatian GPs, more positive practices were reported by older GPs (p = 0.008), females (p = 0.002), GPs who had a partner (p = 0.021), GPs who were specialists in family medicine (p = 0.014), GPs with a longer length of service (p = 0.007), and GPs who had finished a formal education on the prevention of infectious diseases and occupational safety (p = 0.046), while among the Bosnian GPs, no significant correlations were determined. The general practitioners' sociodemographic and employment characteristics strongly influenced their attitudes and practices regarding the prevention and control of COVID-19. The cultural differences between Croatia and Bosnia and Herzegovina, as well as the organizational specificities of their healthcare systems, probably modified the observed differences in the individual patterns of associations between the outcome and explanatory variables in the surveyed neighboring countries.

Genetically Homogeneous or Heterogeneous Population - Distribution of the Recessive Allele in Selected Qualitative Characteristics of Selected Working-Age Population Sample.

Background: The genetic structure of each population can be explained according to the frequency of genes and their allelic variants, genotypes, and phenotypes. Objective: To analyze the genetic heterogeneity of the working-age population from the area of Sarajevo Canton based on classic genetic markers. The studied parameters of genetic heterogeneity were assessed by the relative frequency of the recessive allele for static-morphological traits (earlobe shape, chin shape, hairiness of the middle digital phalanx, bending of the distal phalanx of the little finger and digital index) and dynamic-morphological traits (rolling of the tongue into a groove, extensibility of the proximal thumb knuckle, extensibility of the distal thumb knuckle, the way the forearms are crossed, and the way the fists are made). Results: The results of the t-test showed a significant difference in the manifestation of the recessive homozygote for the observed parameters of qualitative variation in the subsamples of men and women. Only for two traits (attached earlobe and hyperextensibility of the distal knuckle of the thumb). The selected sample represent a relatively genetically homogenous population. Conclusion: This study serves as a valuable source of data for future research and the formation of a genetic database in Bosnia and Herzegovina.

Personality Traits of Croatian University Students with Internet Addiction.

Specific personality traits may predispose individuals to various forms of addictive behaviors. This study aimed to investigate the association between personality traits of university students and Internet addiction (IA). A sample of 1051 university students was recruited from the largest university in Eastern Croatia. A structured anonymous questionnaire that included questions regarding students' sociodemographic information and Internet usage patterns, the Young Internet Addiction Test and Big Five Inventory served as a research tool. The study revealed that 1.0% of the studied sample expressed severe IA while 24.6% of study participants expressed some signs of addiction. The IA was detected in 576 (80.0%) students who used the Internet mainly for social networking, in 30 (78.9%) students who mainly used it for online gaming, and in 153 (52.2%) students who mainly used it for university assignments (p < 0.001). Higher neuroticism, higher extraversion, and higher openness to new experiences were connected with IA in general (p < 0.001). Higher neuroticism, higher extraversion, and higher openness to new experiences were significantly associated with addictive behavior during social networking (p < 0.001). Higher extraversion and higher openness to new experiences were significantly associated with addictive behavior during Internet usage for university assignments (p = 0.025), while there were no significant associations between specific personality traits and addictive behavior during online gaming (p = 0.059). Personality traits must be taken into account while developing programs and implementing interventions for preventing IA in the university student population.

Dynamics and thermal sensitivity of ribosomal RNA maturation paths in plants.

Ribosome biogenesis is a constitutive fundamental process for cellular function. Its rate of production depends on the rate of maturation of precursor ribosomal RNA (pre-rRNA). The rRNA maturation paths are marked by four dominant rate-limiting intermediates with cell-type variation of the processivity rate. We have identified that high temperature stress in plants, while halting the existing pre-rRNA maturation schemes, also transiently triggers an atypical pathway for 35S pre-rRNA processing. This pathway leads to production of an aberrant precursor rRNA, reminiscent of yeast 24S, encompassing 18S and 5.8S rRNA that do not normally co-occur together at sub-unit levels; this response is elicited specifically by high and not low temperatures. We show this response to be conserved in two other model crop plant species (Rice and Tomato). This pathway persists even after returning to normal growth conditions for 1 hour and is reset between 1-6 hours after stress treatment, likely, due to resumption of normal 35S pre-rRNA synthesis and processing. The heat-induced ITS2 cleavage-derived precursors and stalled P-A2-like precursors were heterogeneous in nature with a fraction containing polymeric (A) tails. Furthermore, high temperature treatment and subsequent fractionation resulted in polysome and precursor rRNA depletion.

Different Patterns of Mental Health Outcomes among Road Traffic Crash Survivors: A Prospective Cohort Study.

This study aimed to investigate factors associated with the symptoms of mental disorders following a road traffic crash (RTC). A prospective cohort of 200 people was followed for 6 months after experiencing an RTC. The cohort was comprised of uninjured survivors and injured victims with all levels of road traffic injury (RTI) severity. Multivariable logistic regression analyses were performed to evaluate the associations between the symptoms of depression, posttraumatic stress disorder and anxiety one and six months after the RTC, along with sociodemographic factors, health status before and after the RTC, factors related to the RTI and factors related to the RTC. The results showed associations of depression, anxiety, and posttraumatic stress disorder symptoms with sociodemographic factors, factors related to the health status before and after the RTC and factors related to the RTC. Factors related to the RTI showed associations only with depression and posttraumatic stress disorder symptoms. Identifying factors associated with mental disorders following an RTC is essential for establishing screening of vulnerable individuals at risk of poor mental health outcomes after an RTC. All RTC survivors, regardless of their RTI status, should be screened for factors associated with mental disorders in order to successfully prevent them.

Quality of Life in Road Traffic Accident Survivors.

INTRODUCTION: The loss of quality of life is the major consequence following a non-fatal road traffic accident (RTA). Previous research regarding quality of life did not include uninjured RTA survivors. The research aim was thus to evaluate the quality of life of the RTA survivors regardless of whether or not they sustained injures, and to identify factors associated with decreased quality of life after the RTA. METHODS: A cohort of 200 RTA survivors with and without injuries was followed after experiencing an RTA. The quality of life and mental health outcomes were assessed 1 month following RTA. A vast range of sociodemographic, pre-RTA health-related, RTA related, RTA injury-related, compensation-related factors and mental health outcomes were investigated. RESULTS: Decreased quality of life following an RTA showed an association with the low socioeconomic status of the RTA victims, poor pre-RTA health, injury-related factors, compensation-related factors and psychological disorders after the RTA. CONCLUSIONS: Identifying predictors of decreased quality of life following an RTA will enable planning interventions targeting the most important factors that influence recovery of RTA victims. Assessing and recording of self-reported quality of life should be a part of the routine protocol in RTA survivors' health-care.

Predictors of Mental Health Outcomes in Road Traffic Accident Survivors.

Mental health outcomes of road traffic accidents (RTAs) are always investigated in assessments of those involved. The aim of this study was to investigate the psychological consequences and associated factors in all RTA survivors, irrelevant of their injury status. A cohort of 155 people was assessed one month after experiencing a RTA using self-reported measures for posttraumatic stress disorder (PTSD), depression, and anxiety. Associations between mental health outcomes and sociodemographic factors, pre-RTA health status, injury-related factors, and RTA details were analyzed. RTA survivors reported substantial rates of PTSD (32.3%) and depression (17.4%) symptoms, and low rates of anxiety (5.8%). Symptoms of depression were associated with below-average self-perceived economic status, irreligiousness, medication use, psychiatric medication use, and injury-related factors. PTSD symptoms were associated with female gender, below-average self-perceived economic status, previous psychiatric illness, medication use, psychiatric medication use, not being at fault in the relevant RTA, claiming compensation, and injury-related factors. Anxiety symptoms were associated with previous chronic or psychiatric illness, previous permanent pain, psychiatric medication use, and self-perceived threat to life, but not with sustaining injury. Along with the evaluation and treatment of RTA injuries, health care providers should evaluate the pre-RTA health status of all RTA victims. Psychological support to those at risk may prevent psychological disorders after RTAs.

Vector-Valued Graph Trend Filtering with Non-Convex Penalties.

This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. We extend the graph trend filtering framework to denoising vector-valued graph signals with a family of non-convex regularizers, which exhibit superior recovery performance over existing convex regularizers. Using an oracle inequality, we establish the statistical error rates of first-order stationary points of the proposed non-convex method for generic graphs. Furthermore, we present an ADMM-based algorithm to solve the proposed method and establish its convergence. Numerical experiments are conducted on both synthetic and real-world data for denoising, support recovery, event detection, and semi-supervised classification.

Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh.

We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. This dataset provides dynamic responses of in-service trains via vibration data collected by accelerometers, which enables a low-cost way of monitoring rail tracks more frequently. Such an approach will result in more reliable and economical ways to monitor rail infrastructure. The dataset also includes corresponding GPS positions of the trains, environmental conditions (including temperature, wind, weather, and precipitation), and track maintenance logs. The data, which is stored in a MAT-file format, can be conveniently loaded for various potential uses, such as validating anomaly detection and data fusion as well as investigating environmental influences on train responses.

Co-orthologues of ribosome biogenesis factors in A. thaliana are differentially regulated by transcription factors.

KEY MESSAGE: Different genes coding for one ribosome biogenesis factor are differentially expressed and are likely under the control of distinct transcription factors, which contributes to the regulatory space for ribosome maturation. Maturation of ribosomes including rRNA processing and modification, rRNA folding and ribosome protein association requires the function of many ribosome biogenesis factors (RBFs). Recent studies document plant-specific variations of the generally conserved process of ribosome biogenesis. For instance, distinct rRNA maturation pathways and intermediates have been identified, the existence of plant specific RBFs has been proposed and several RBFs are encoded by multiple genes. The latter in combination with the discussed ribosome heterogeneity points to a possible function of the different proteins representing one RBF in diversification of ribosomal compositions. Such factor-based regulation would require a differential regulation of their expression, may be even controlled by different transcription factors. We analyzed the expression profiles of genes coding for putative RBFs and transcription factors. Most of the genes coding for RBFs are expressed in a comparable manner, while different genes coding for a single RBF are often differentially expressed. Based on a selected set of genes we document a function of the transcription factors AtMYC1, AtMYC2, AtbHLH105 and AtMYB26 on the regulation of different RBFs. Moreover, on the example of the RBFs LSG1 and BRX1, both encoded by two genes, we give a first hint on a differential transcription factor dependence of expression. Consistent with this observation, the phenotypic analysis of RBF mutants suggests a relation between LSG1-1 and BRX1-1 expression and the transcription factor MYC1. In summary, we propose that the multiple genes coding for one RBF are required to enlarge the regulatory space for ribosome biogenesis.

Light field otoscope design for 3D in vivo imaging of the middle ear.

We present a light field digital otoscope designed to measure three-dimensional shape of the tympanic membrane. This paper describes the optical and anatomical considerations we used to develop the prototype, along with the simulation and experimental measurements of vignetting, field curvature, and lateral resolution. Using an experimental evaluation procedure, we have determined depth accuracy and depth precision of our system to be 0.05-0.07 mm and 0.21-0.44 mm, respectively. To demonstrate the application of our light field otoscope, we present the first three-dimensional reconstructions of tympanic membranes in normal and otitis media conditions, acquired from children who participated in a feasibility study at the Children's Hospital of Pittsburgh of the University of Pittsburgh Medical Center.

Plant-Specific Features of Ribosome Biogenesis.

The biogenesis of eukaryotic ribosomes is a fundamental process involving hundreds of ribosome biogenesis factors (RBFs) in three compartments of the cell, namely the nucleolus, nucleus, and cytoplasm. Many RBFs are involved in the processing of the primary ribosomal (r)RNA transcript, in which three of the four rRNAs are imbedded. While pre-rRNA processing is well described for yeast and mammals, a detailed processing scheme for plants is lacking. Here, we discuss the emerging scheme of pre-rRNA processing in Arabidopsis thaliana in comparison to other eukaryotes, with a focus on plant characteristics. In addition, we highlight the impact of the ribosome and its biogenesis on developmental processes because common phenotypes can be observed for ribosomal protein and RBF mutants.

Radial pressure profiles in a cold-flow gas-solid vortex reactor.

A unique normalized radial pressure profile characterizes the bed of a gas-solid vortex reactor over a range of particle densities and sizes, solid capacities, and gas flow rates: 950-1240 kg/m3, 1-2 mm, 2 kg to maximum solids capacity, and 0.4-0.8 Nm3/s (corresponding to gas injection velocities of 55-110 m/s), respectively. The combined momentum conservation equations of both gas and solid phases predict this pressure profile when accounting for the corresponding measured particle velocities. The pressure profiles for a given type of particles and a given solids loading but for different gas injection velocities merge into a single curve when normalizing the pressures with the pressure value downstream of the bed. The normalized-with respect to the overall pressure drop-pressure profiles for different gas injection velocities in particle-free flow merge in a unique profile.

A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples.

UNLABELLED: We propose a methodology for the design of features mimicking the visual cues used by pathologists when identifying tissues in hematoxylin and eosin (H&E)-stained samples. BACKGROUND: H&E staining is the gold standard in clinical histology; it is cheap and universally used, producing a vast number of histopathological samples. While pathologists accurately and consistently identify tissues and their pathologies, it is a time-consuming and expensive task, establishing the need for automated algorithms for improved throughput and robustness. METHODS: We use an iterative feedback process to design a histopathology vocabulary (HV), a concise set of features that mimic the visual cues used by pathologists, e.g. "cytoplasm color" or "nucleus density". These features are based in histology and understood by both pathologists and engineers. We compare our HV to several generic texture-feature sets in a pixel-level classification algorithm. RESULTS: Results on delineating and identifying tissues in teratoma tumor samples validate our expert knowledge-based approach. CONCLUSIONS: The HV can be an effective tool for identifying and delineating teratoma components from images of H&E-stained tissue samples.

Images as occlusions of textures: a framework for segmentation.

We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on nonnegative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.

Automated diagnosis of otitis media: vocabulary and grammar.

WE PROPOSE A NOVEL AUTOMATED ALGORITHM FOR CLASSIFYING DIAGNOSTIC CATEGORIES OF OTITIS MEDIA: acute otitis media, otitis media with effusion, and no effusion. Acute otitis media represents a bacterial superinfection of the middle ear fluid, while otitis media with effusion represents a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is difficult, often leading to overprescription of antibiotics as they are beneficial only for children with acute otitis media. This underscores the need for an accurate and automated diagnostic algorithm. To that end, we design a feature set understood by both otoscopists and engineers based on the actual visual cues used by otoscopists; we term this the otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this the otitis media grammar. The algorithm achieves 89.9% classification accuracy, outperforming both clinicians who did not receive special training and state-of-the-art classifiers.

Local histograms and image occlusion models.

The local histogram transform of an image is a data cube that consists of the histograms of the pixel values that lie within a fixed neighborhood of any given pixel location. Such transforms are useful in image processing applications such as classification and segmentation, especially when dealing with textures that can be distinguished by the distributions of their pixel intensities and colors. We, in particular, use them to identify and delineate biological tissues found in histology images obtained via digital microscopy. In this paper, we introduce a mathematical formalism that rigorously justifies the use of local histograms for such purposes. We begin by discussing how local histograms can be computed as systems of convolutions. We then introduce probabilistic image models that can emulate textures one routinely encounters in histology images. These models are rooted in the concept of image occlusion. A simple model may, for example, generate textures by randomly speckling opaque blobs of one color on top of blobs of another. Under certain conditions, we show that, on average, the local histograms of such model-generated-textures are convex combinations of more basic distributions. We further provide several methods for creating models that meet these conditions; the textures generated by some of these models resemble those found in histology images. Taken together, these results suggest that histology textures can be analyzed by decomposing their local histograms into more basic components. We conclude with a proof-of-concept segmentation-and-classification algorithm based on these ideas, supported by numerical experimentation.

Guaranteeing Convergence of Iterative Skewed Voting Algorithms for Image Segmentation.

In this paper we provide rigorous proof for the convergence of an iterative voting-based image segmentation algorithm called Active Masks. Active Masks (AM) was proposed to solve the challenging task of delineating punctate patterns of cells from fluorescence microscope images. Each iteration of AM consists of a linear convolution composed with a nonlinear thresholding; what makes this process special in our case is the presence of additive terms whose role is to "skew" the voting when prior information is available. In real-world implementation, the AM algorithm always converges to a fixed point. We study the behavior of AM rigorously and present a proof of this convergence. The key idea is to formulate AM as a generalized (parallel) majority cellular automaton, adapting proof techniques from discrete dynamical systems.

OTITIS MEDIA VOCABULARY AND GRAMMAR.

We propose an automated algorithm for classifying diagnostic categories of otitis media (middle ear inflammation); acute otitis media, otitis media with effusion and no effusion. Acute otitis media represents a bacterial superinfection of the middle ear fluid and otitis media with effusion a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is hard, leading to overprescription of antibiotics that are beneficial only for children with acute otitis media, prompting a need for an accurate and automated algorithm. To that end, we design a feature set understood by both otoscopists and engineers based on the actual visual cues used by otoscopists; we term this otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this otitis media grammar. The algorithm achieves 84% classification accuracy, in the range or outperforming clinicians who did not receive special training, as well as state-of-the-art classifiers.