Segmentation Analysis Research Articles

Spatial-temporal characteristics of meteorological drought and its relationship with circulation indices over Songhua River Basin, China

ABSTRACT Global warming has increased the risk of meteorological drought and associated hazards. Based on daily data from 35 meteorological stations in the Songhua River Basin (SRB) collected from 1960 to 2019, this study applied the standardised precipitation evapotranspiration index (SPEI) and used the ensemble empirical mode decomposition (EEMD) method and Pettitt's mutation test to perform a partitioned segmentation analysis of the multi-temporal and spatial variations in the meteorological droughts affecting the basin in terms of frequency and intensity, and discussed the relationship between the atmospheric circulation index and meteorological drought. The results showed that the overall meteorological drought trends shifted from a drought to a humid trend from 1960 to 2019. The drought in the basin had periods of 3, 6.7, 15, and 30 years, with mutation points in 1968, 1980, 1997, and 2012. The drought trend in Regions I and IV of the entire basin was enhanced, while that in Regions II, III, and V was weakened. Drought in the basin was strongly influenced by the Arctic Oscillation (AO) and Southern Oscillation Index (SOI).

Uncorking the virtual frontier of wine experiences: interest drivers and potential consumers’ profile

PurposeVirtual wine experiences (VWEs) replaced in-person wine experiences during the Covid-19 pandemic and continue to be offered by some actors. This study aims to investigate the factors driving interest in VWEs and identify relevant traits of potential consumers to help assess VWEs long-term potential.Design/methodology/approachA representative sample of 399 Oregon and California wine consumers answered a structured online survey. The authors combine ordered logistic regression and qualitative techniques to analyze the data.FindingsVWEs may effectively attract potential wine consumers and tourists. High interest in VWEs is associated with strong wine involvement and intentions to visit wine regions. Digitization, aversion to travel-related risk and convenience are other relevant drivers of VWE interest. The segmentation analysis revealed that consumers with a potentially higher interest in VWE have distinct traits.Practical implicationsWineries and wine tourism destinations could leverage VWEs to attract wine tourists and consumers. The authors discuss specific characteristics of high-interest consumers.Originality/valueParticipants in VWEs interact with hosts and explore products in real time. This engagement has long-term marketing potential for attracting them as customers or visitors. The study provides strategic information for practitioners and academics on VWE interest drivers and potential demand, which is currently missing from the literature.

Rapid Separation and Detection of Drugs in Complex Biological Matrix Using TD-CDI Mass Spectrometer.

The drug detection technology plays a pivotal role in the domains of pharmaceutical regulation and law enforcement. In this study, we introduce a method that combines thermal desorption corona discharge ionization (TD-CDI) with mass spectrometry for efficient drug detection. The TD-CDI module, characterized by its compact and simple design, enables the separation of analytes within seconds and real-time presentation of one or two analyte peaks on the mass spectrum most of the time, which reduces matrix interference and improves detection performance. Through experimental investigation, we studied the characteristics of TD-CDI for analyte separation and detection, even with the same mass number, and optimized the TD-CDI approach. TD-CDI-MS was employed for the rapid detection of drugs in various traditional medicine, food products, and human samples. Additionally, by utilizing TD-CDI for segmented hair direct analysis, it becomes possible to trace the drug usage cycle of individuals. This underscores the feasibility of the proposed analytical method within the realm of drug detection.

Ultrasound segmentation analysis via distinct and completed anatomical borders

PurposeSegmenting ultrasound images is important for precise area and/or volume calculations, ensuring reliable diagnosis and effective treatment evaluation for diseases. Recently, many segmentation methods have been proposed and shown impressive performance. However, currently, there is no deeper understanding of how networks segment target regions or how they define the boundaries. In this paper, we present a new approach that analyzes ultrasound segmentation networks in terms of learned borders because border delimitation is challenging in ultrasound.MethodsWe propose a way to split the boundaries for ultrasound images into distinct and completed. By exploiting the Grad-CAM of the split borders, we analyze the areas each network pays attention to. Further, we calculate the ratio of correct predictions for distinct and completed borders. We conducted experiments on an in-house leg ultrasound dataset (LEG-3D-US) as well as on two additional public datasets of thyroid, nerves, and one private for prostate.ResultsQuantitatively, the networks exhibit around 10% improvement in handling completed borders compared to distinct borders. Similar to doctors, the network struggles to define the borders in less visible areas. Additionally, the Seg-Grad-CAM analysis underscores how completion uses distinct borders and landmarks, while distinct focuses mainly on the shiny structures. We also observe variations depending on the attention mechanism of each architecture.ConclusionIn this work, we highlight the importance of studying ultrasound borders differently than other modalities such as MRI or CT. We split the borders into distinct and completed, similar to clinicians, and show the quality of the network-learned information for these two types of borders. Additionally, we open-source a 3D leg ultrasound dataset to the community https://github.com/Al3xand1a/segmentation-border-analysis.

Exercise-induced regional heterogeneity of myocardial left ventricular contraction in hereditary long QT syndrome

Abstract Background Hereditary long QT syndrome (LQTS) is characterized by prolonged ventricular repolarization and an increased risk for ventricular arrhythmias (VA) and sudden cardiac death. Dispersion of ventricular repolarization in LQTS may be more pronounced during exercise and not only impacts the heart’s electrical functions but also the pattern of mechanical contraction. Little is known about the intraventricular heterogeneity of wall motion abnormalities in LQTS patients. Methods In this cross-sectional study we quantify regional, transmural, and longitudinal alterations in left ventricular (LV) contraction at rest, during exercise, and recovery in genetically confirmed LQTS patients and healthy controls to evaluate their value in differentiating high risk patients as defined by a history of VA. ECG and echocardiographic assessments were conducted during rest, peak ergometer activity (105 to 125 bpm), and four minutes into recovery phases. Longitudinal strain (LStr) parameters were measured for each LV segment separately. We differentiated LStr variations as heterogeneity of LStr amplitude (hetLStr; standard deviation [SD] or mean range) and dispersion of time-to-peak LStr (disLStr; SD or mean range). Here, we present the analyses of disLStr. Results At present, 15 patients with genetically confirmed LQTS (LQT1: 8, LQTS 2: 7; mean QTc 475 ± 46 ms) have been included of which 9 had a history of VA. In addition, we collected data from 8 age- and gender-matched control patients (mean QTc 401 ± 18 ms). LV ejection fraction and LStr were not significantly different between the LQTS and control groups. However, LQTS patients exhibited a significantly lower mean LStr in both the endocardial and epicardial layers across all phases compared to control. We observed a significantly more pronounced disLStr across all segments in the LQTS group during the rest and recovery phases in the endocardial layer and during all phases in the epicardial layer. Regional differences (apical vs. basal) were found in all LQTS patients only endocardially at rest. Globally, disLStr showed transmural differences (epicardial vs. endocardial) in both asymptomatic and symptomatic LQTS patients during peak exercise and recovery. Upon segmental analysis, it was observed that symptomatic patients exhibited these transmural differences apically already at rest (p=.004), as well as in the basal region during peak exercise (p=.01), and in the apical region during recovery (p=.02). Conclusion LQTS patients show regional (base to apex) and transmural (endocardial to epicardial) heterogeneities in myocardial contraction at rest, during exercise, and recovery as compared to healthy patients. These regional variations in myocardial contraction patterns may be more pronounced in high risk LQTS patients. Ongoing analyses of hetLStr are in progress to corroborate our findings.

Study of the relationship between the severity of posterior capsular opacification detected by objective detection techniques and visual acuity.

To explore the severity of posterior capsule opacification (PCO) using objective detection techniques and its relationship with visual acuity. The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China. Prospective cohort study. All patients underwent slitlamp examination, intraocular pressure (IOP) measurement, and corrected distance visual acuity (CDVA) testing before Nd:YAG laser capsulotomy, and examination, after fully dilated, with: IOLMaster 700, optical coherence tomography (OCT), Sirius Topographer (CSO) anterior segment analysis, and color fundus photography (CFP). CDVA and IOP were taken post treatment. Thickness and density of the posterior capsule, CFP quality (CFPQ) and OCT signal strength (OCTSS) were recorded. Analysis used Spearman correlation, heatmaps, and receiver operating characteristic curves. 83 eyes in 78 patients were included in this study. Spearman correlation analysis revealed correlations between pretreatment CDVA and IOLMaster 700 PCO thickness (MT), IOLMaster 700 cumulative effect (MCE), Sirius PCO thickness (ST), Sirius maximum density (SMD), Sirius cumulative effect (SCE), OCTSS, and CFPQ (correlation coefficients were 0.500, 0.484, 0.465, -0.256, 0.317, -0.442, -0.412, all P < .05). The improvement of vision acuity (ImpVA) showed correlations with MT, MCE, ST, SCE, OCTSS, and CFPQ (correlation coefficients were -0.452, -0.471, -0.346, -0.278, 0.320, 0.381, all P < .05). For ImpVA, the predictive ability of IOLMaster 700 was superior to Sirius, and the joint model was significantly better than single factors. Posterior capsule thickness and cumulative effect were reliable indicators for evaluating PCO. Compared with Sirius, the IOLMaster 700 demonstrated superior predictive ability and higher correlation.

Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning

Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept source OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of Swept source OCT images. A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.

“If I say sustainable diet”: What are French consumers’ social representations?

The adoption of sustainable diets is essential to maintain our planet’s ecosystem and the well-being of its inhabitants. Scientists characterize sustainable diets by four essential dimensions: respectful of the environment, of good nutritional quality, culturally acceptable, and economically fair. The ‘sustainable diet’ concept, however, can be challenging for consumers to understand. They might partially understand the concept or have representations of the concept that are not shared between individuals, which could result in difficulty putting the recommendations into practice. Our study investigated French consumers’ social representations of sustainable diets, aiming more particularly to show if groups with different levels of education might have different representations. A free word association task was carried out by 273 participants aged between 20 and 60 years. Our results revealed common associations among the participants, including concepts such as ecology, health, environment, and locality. Sociocultural and economic dimensions were rarely mentioned, unlike the predominant environmental dimension. Segmentation analysis by education level revealed that participants in the higher education group had more multidimensional social representations. These results show that the ‘sustainable diet’ concept is not fully shared by French consumers. Clarifying the definition should improve consumer understanding, thus promoting the adoption of sustainable diet practices by all.

Chi-square automatic interaction detection (CHAID) analysis of the use of safety goggles and face masks as personal protective equipment (PPE) to protect against occupational biohazards

BackgroundThis study represents the first Spanish investigation to rigorously evaluate compliance with the use of safety goggles and face masks as essential personal protective equipment (PPE) in companies with workplaces involving exposure to biological agents. ObjectivesThis study aimed to examine the degree of use of face masks and safety goggles as personal protective equipment (PPE), the factors that influence their use, and the profile of workers exposed to occupational biological agents in Spanish companies in the health sector, farming sector, meat industry, waste treatment plants, food industry, and veterinary centers. MethodsWe conducted a cross-sectional descriptive study involving 590 Spanish workers from 51 companies. We developed a 34-item questionnaire to assess workers’ perception of risk related to exposure to biological agents in their workplaces. Among the questions, three were designed to measure the degree of use of key protective equipment in sectors with biological agent exposure: protective gloves, safety goggles or face masks. We only analyzed safety goggles and face masks. We performed various statistical analyses, including Cronbach’s alpha, frequency of endorsement, content validity ratio using Lawshe’s method, varimax rotation, the Kaiser-Meyer-Olkin test, and Bartlett’s sphericity test, to assess the internal consistency and reliability of the questionnaire. Additionally, we employed a chi-square automatic interaction detection (CHAID) segmentation analysis, using workers’ responses regarding their attitudes toward safety goggles and face mask usage as PPE for protection against biological risks, with demographic variables as independent factors. ResultsIn the current study, CHAID analysis revealed that workers exposed to group 2 biological agents used more safety goggles and face shields compared with workers exposed to other groups of biological agents. Moreover, workers in laboratories and the food industry used face masks more than workers of other sectors. ConclusionThe CHAID analysis in the current study indicated that workers exposed to biological agents from both group 2 and group 3 demonstrated satisfactory levels of compliance and utilization of protective masks, surpassing their counterparts in terms of usage. Workers in the food and laboratory industries had subpar compliance with preventive measures, and employees from companies with internal health and safety departments exhibited significant adherence to workplace mask usage, safeguarding themselves against biological risks.

Role of myocardial strain imaging in diagnosing inducible myocardial ischemia with treadmill contrast-enhanced stress echocardiography

IntroductionThe aim of this study is to analyze the diagnostic value of global longitudinal strain (GLS) in detecting inducible myocardial ischemia in patients with chest pain undergoing treadmill contrast-enhanced stress echocardiography (SE).MethodsWe retrospectively enrolled all patients who underwent invasive coronary angiography after treadmill contrast-enhanced SE. Rest and peak-stress myocardial GLS, segmental LS, and LS of 4-chamber (CH), 2-CH, and 3-CH views were reported. Luminal stenosis of more than 70% or fractional flow reserve (FFR) of < 0.8 was considered significant.ResultsIn total 33 patients were included in the final analysis, among whom sixteen patients (48.4%) had significant coronary artery stenosis. Averaged GLS, 3-CH, and 4-CH LS were significantly lower in patients with critical coronary artery stenosis compared to those without significant stenosis (-17.1 ± 7.1 vs. -24.2 ± 7.2, p = 0.041), (-18.2 ± 8.9 vs. -24.6 ± 8.2, p = 0.045) and (-14.8 ± 6.2 vs. -22.8 ± 7.8, p = 0.009), respectively. Receiver operating characteristic (ROC) analysis of ischemic and non-ischemic segments demonstrated that a cut-off value of -20% of stress LS had 71% sensitivity and 60% specificity for ruling out inducible myocardial ischemia (Area under the curve was AUC = 0.72, P < 0.0001).ConclusionMyocardial LS measured with treadmill contrast-enhanced stress echocardiography demonstrates potential value in identifying patients with inducible myocardial ischemia.

ARCnet: A Multi-Feature-Based Auto Radio Check Model

Radio checks serve as the foundation for ground-to-air communication. To integrate machine learning for automated and reliable radio checks, this study introduces an Auto Radio Check network (ARCnet), a novel algorithm for non-intrusive speech quality assessment in civil aviation, addressing the crucial need for dependable ground-to-air communication. By employing a multi-scale feature fusion approach, including the consideration of audio’s frequency domain, comprehensibility, and temporal information within the radio check scoring network, ARCnet integrates manually designed features with self-supervised features and utilizes a transformer network to enhance speech segment analysis. Utilizing the NISQA open-source dataset and the proprietary RadioCheckSpeech dataset, ARCnet demonstrates superior performance in predicting speech quality, showing a 12% improvement in both the Pearson correlation coefficient and root mean square error (RMSE) compared to existing models. This research not only highlights the significance of applying multi-scale attributes and deep neural network parameters in speech quality assessment but also emphasizes the crucial role of the temporal network in capturing the nuances of voice data. Through a comprehensive comparison of the ARCnet approach to traditional methods, this study underscores its innovative contribution to enhancing communication efficiency and safety in civil aviation.

Examining the Relationship between Geographic Groupings and Perspective of Critical Community Issues: An Audience Segmentation Analysis

The present study examined whether perception of critical community issues was dependent on respondents’ rurality, geographic region, or extension district in the state of Georgia, located in the southeastern United States. A non-probability sampling procedure was employed. A total of 3,374 responses were collected. Five critical community issue themes were analyzed: (1) youth and family development, (2) civic engagement and community development, (3) agriculture and economic development, (4) nutrition education and food availability, and (5) water. Descriptive statistics were analyzed. A series of chi-squared tests of independence were used to test for significant relationships between perception of critical community issues and geographic grouping. Statistically significant differences were observed between all groups (rurality, region, and district). Specifically, significant relationships were observed between all groups and perception of youth and family development and agriculture and economic development. A significant relationship between region and perception of civic engagement and community leadership was observed. Additionally, there was a significant relationship between rurality and water observed. The results indicate that programming efforts should be informed both by proximal communities as well as non-proximal communities sharing common characteristics.

Multi-sensor Data Analysis for Aerial Image Semantic Segmentation and Vectorization

Abstract. One of the urgent and constantly in demand problems is updating maps. Maps, representing geo-information in vector form, have undoubted advantages in compactness and ”readability” compared to aerial photographs. The issue of maps actuality is critically important for rational urban planning, precision farming, the relevance of the cadastre and other geospatial applications. Various sources of data are used for maps updating, with aerial imagery being the main and rich source of information. Automatic processing of aerial photographs makes it possible to efficiently extract vector information, providing operational monitoring and accounting for changes that have appeared. The presented study addresses the problem of multi sensor information fusion in order to obtain accurate vector information. We use aerial images as a main data source and additionally the data of laser scanning and ground survey to increase performance of automatic image semantic segmentation and vectorization. The proposed framework is demonstrated on the task of forest monitoring.

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.

The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS. A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared. The mean DCS of the 3D U-Net were 0.905±0.080, 0.933±0.018, and 0.928±0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p=0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r=0.805). A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.

Muscle fibre morphometric analysis (MusMA) correlates with muscle function and cardiovascular risk prognosis.

Morphometry of striated muscle fibres is critical for monitoring muscle health and function. Here, we evaluated functional parameters of skeletal and cardiac striated muscle in two experimental models using the Morphometric Analysis of Muscle Fibre tool (MusMA). The collagen-induced arthritis model was used to evaluate the function of skeletal striated muscle and the non-alcoholic fatty liver disease model was used for cardiac striated muscle analysis. After euthanasia, we used haeamatoxylin and eosin stained sections of skeletal and cardiac muscle to perform muscle fibre segmentation and morphometric analysis. Morphometric analysis classified muscle fibres into six subpopulations: normal, regular hypertrophic, irregular hypertrophic, irregular, irregular atrophic and regular atrophic. The percentage of atrophic fibres was associated with lower walking speed (p = 0.009) and lower body weight (p = 0.026), respectively. Fibres categorized as normal were associated with maximum grip strength (p < 0.001) and higher march speed (p < 0.001). In the evaluation of cardiac striated muscle fibres, the percentage of normal cardiomyocytes negatively correlated with cardiovascular risk markers such as the presence of abdominal adipose tissue (p = .003), miR-33a expression (p = .001) and the expression of miR-126 (p = .042) Furthermore, the percentage of atrophic cardiomyocytes correlated significantly with the Castelli risk index II (p = .014). MusMA is a simple and objective tool that allows the screening of striated muscle fibre morphometry, which can complement the diagnosis of muscle diseases while providing functional and prognostic information in basic and clinical research.

TGFβ Signaling Dysregulation May Contribute to COL4A1-Related Glaucomatous Optic Nerve Damage.

Mutations in the genes encoding type IV collagen alpha 1 (COL4A1) and alpha 2 (COL4A2) cause a multisystem disorder that includes ocular anterior segment dysgenesis (ASD) and glaucoma. We previously showed that transforming growth factor beta (TGFβ) signaling was elevated in developing anterior segments from Col4a1 mutant mice and that reducing TGFβ signaling ameliorated ASD, supporting a role for the TGFβ pathway in disease pathogenesis. Here, we tested whether altered TGFβ signaling also contributes to glaucoma-related phenotypes in Col4a1 mutant mice. To test the role of TGFβ signaling in glaucoma-relevant phenotypes, we genetically reduced TGFβ signaling using mice with mutated Tgfbr2, which encodes the common receptor for all TGFβ ligands in Col4a1+/G1344D mice. We performed slit-lamp biomicroscopy and optical coherence tomography for qualitative and quantitative analyses of anterior and posterior ocular segments, histological analyses of ocular tissues and optic nerves, and intraocular pressure assessments using rebound tonometry. Col4a1+/G1344D mice showed defects of the ocular drainage structures, including iridocorneal adhesions, and phenotypes consistent with glaucomatous neurodegeneration, including thinning of the nerve fiber layer, retinal ganglion cell loss, optic nerve head excavation, and optic nerve degeneration. We found that reducing TGFβ receptor 2 (TGFBR2) was protective for ASD, ameliorated ocular drainage structure defects, and protected against glaucomatous neurodegeneration in Col4a1+/G1344D mice. Our results suggest that elevated TGFβ signaling contributes to glaucomatous neurodegeneration in Col4a1 mutant mice.

Changes in inpatient brain arteriovenous malformation management in the United States following the ARUBA trial: analysis of an interrupted time series design

The November 2013 online publication of ARUBA, the first multi-institutional randomized controlled trial for unruptured brain arteriovenous malformations (AVMs), has sparked over 100 publications in protracted debates MethodsThis study sought to examine inpatient management patterns of brain AVMs from 2009 to 2016 and observe if changes in U.S. inpatient management were attributable to the ARUBA publication using interrupted time series of brain AVM studies from the National Inpatient Sample data 2009–2016. Outcomes of interest were use of embolization, surgery, combined embolization and microsurgery, radiotherapy, and observation during that admission. An interrupted time series design compared management trends before and after ARUBA. Segmented linear regression analysis tested for immediate and long-term impacts of ARUBA on management. ResultsElective and asymptomatic patient admissions declined 2009–2016. In keeping with the ARUBA findings, observation for unruptured brain AVMs increased and microsurgery decreased. However, embolization, radiosurgery, and combined embolization and microsurgery also increased. For ruptured brain AVMs, treatment modality trends remained positive with even greater rates of observation, embolization, and combined embolization and microsurgery occurring after ARUBA (data on radiosurgery were scarce). None of the estimates for the change in trends were statistically significant. ConclusionsThe publication of ARUBA was associated with a decrease in microsurgery and increase in observation for unruptured brain AVMs in the US. However, inpatient radiotherapy, embolization, and combined embolization and surgery also increased, suggesting trends moved counter to ARUBA’s conclusions. This analysis suggested that ARUBA had a small impact as clinicians rejected ARUBA’s findings in managing unruptured brain AVMs.

Sexually transmitted infections, the epidemic that persists after the COVID-19 pandemic: an analysis of the primary care electronic health records covering about 5 million people in Catalonia

BackgroundThe aim of our study is to analyse the trends in the diagnosis of sexually transmitted infections (STIs) during the COVID-19 pandemic.MethodsWe conducted an observational retrospective population-based study using data from primary care electronic health records spanning from January 2016 to December 2022 (involving 5.1 million people older than 14 years). We described the daily number of new STI diagnoses from 2016 to 2022; as well as the monthly accumulation of new STI diagnoses for each year. We compared the monthly averages of new diagnoses in 2019, 2020, 2021 and 2022 using the T-test. Finally, we performed a segmented regression analysis of the daily number of STI diagnoses.ResultsWe analysed 200,676 new STI diagnoses. The number of diagnoses abruptly decreased coinciding with the lockdown. Overall in 2020, we observed a reduction of 15%, with higher reductions for specific STIs such as gonorrhoea (-21%), chlamydia (-24%), and HIV (-31%) compared to 2019. Following this drastic drop, which was temporarily associated with the lockdown, we observed a rapid rebound. In 2021, the number of STI diagnoses was similar to that of 2019. Notably, we found a considerable increase in 2022, particularly for non-specific STI, which lack laboratory confirmation (67% increase). HIV was the only STI with a reduction of up to -38% in diagnoses at the end of 2022 compared to 2019.ConclusionsAfter a significant reduction in 2020, the number of STIs recorded in primary care rapidly rebounded, and the current trend is similar to that of 2019, except for HIV. These findings underscore the dynamic impact of the COVID-19 pandemic on STI diagnoses and highlight the importance of ongoing monitoring and public health interventions in the post-pandemic period.

A Comparison of Pixel Based and Object Based Image Classification for Cropland Area Estimation

Pixel-based (PB) and object-based (OB) Land Use–Land Cover (LULC) classification techniques can be applied in Google Earth Engine (GEE), a flexible cloud-based platform, because of its numerous state-of-the-art functions that comprise several Machine Learning (ML) methods. Adding some texture measure, any measure to a classification usually improves the accuracy. Object segmentation and object textural analysis are two OB methods that are still uncommon in the GEE environment. Object based image classification is difficult because it can be challenging to concatenate the correct functions and adjust various parameters in order to get past the computational limitations of GEE. The goal of this work is to develop and test an OB classification approach that combines the ML algorithm Random Forest (RF) to perform the final classification, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices and the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters. The primary seven GLCM indices are subjected to a Principal Components Analysis (PCA) in order to combine the textural data needed for the OB classification into a single band. The proposed methodology was broadly tested in a 304 km2 study area, located in the Telangana state (India), using Sentinel 2 (S2).

A systematic investigation of grooving and rolling abrasive mechanisms using surface topography

Rolling and grooving are typical phenomena observed in ball cratering tests. Despite extensive research, their impact on the wear rate remains a subject of debate. Besides, the identification of rolling and grooving is predominantly performed with qualitative analysis. Therefore, this investigation introduced a systematic methodology for the assessment of the worn surface topography. Ball cratering tests were performed with increasing load to induce the transition between rolling and grooving mechanisms. Several roughness metrics were analysed, among which the surface aspect ratio (Str) was found the most effective parameter for identifying the transition between rolling and grooving. Additionally, the transition was found to occur in a range of load conditions. Moreover, a segmented analysis was implemented and further revealed a propensity for grooving to manifest towards the centre and trailing edge of the wear crater. Finally, it was found no discernible difference in the wear rate between rolling and grooving conditions, and different hypotheses were discussed to explain this behaviour. The superposition of mechanisms and the occurrence of grooving by rolling were found plausible explanations for the similar wear rate. As consequence, an improved terminology for rolling and grooving was proposed. The term rolling refers to the micro abrasive mechanism, while grooving designates the resulting failure mode.