Adolescents diagnosed with polycystic ovary syndrome under the Rotterdam criteria but not meeting the diagnosis under the updated guideline.

STUDY QUESTION: What are the characteristics of adolescents diagnosed with polycystic ovary syndrome (PCOS) based on the 2003 Rotterdam criteria, but who do not meet the diagnosis according to the international evidence-based guideline? SUMMARY ANSWER: Adolescents who had features of PCOS but did not meet the evidence-based guideline adolescent criteria exhibited unfavorable metabolic profiles compared to controls and shared considerable metabolic and hormonal features with adolescents who did meet the adolescent criteria. WHAT IS KNOWN ALREADY: The international evidence-based PCOS guideline recommended that ultrasound should not be used for the diagnosis of PCOS in girls with a gynecological age of <8 years. Thus far, few studies have evaluated the clinical characteristics of the girls diagnosed with PCOS based on the Rotterdam criteria but who do not meet the diagnosis according to the updated guideline. STUDY DESIGN, SIZE, DURATION: This is a retrospective study, and subjects attended for care from 2004 to 2022. PARTICIPANTS/MATERIALS, SETTING, METHODS: Adolescent girls with PCOS diagnosed according to the 2003 Rotterdam criteria and healthy controls. All participants were between 2 and 8 years since menarche. MAIN RESULTS AND THE ROLE OF CHANCE: Of the 315 girls diagnosed with PCOS according to the Rotterdam criteria, those with irregular menstruation (IM)/hyperandrogenism (HA)/polycystic ovary (PCO), IM/HA, HA/PCO, and IM/PCO phenotypes accounted for 206 (65.4%), 30 (9.5%), 12 (3.8%), and 67 (21.3%) participants, respectively. According to the evidence-based guideline, 79 girls (25.1%) with the HA/PCO or IM/PCO phenotypes were not diagnosed with PCOS, and aligned to the international guideline; they were designated as the 'at-risk' group. As expected, the girls meeting the evidence-based guideline adolescent criteria showed the worst metabolic profiles (degree of generalized or central obesity, frequency of insulin resistance, prediabetes or diabetes, and metabolic syndrome) and higher hirsutism scores than the at-risk group or controls. Approximately 90% of the at-risk group were not overweight or obese, which was similar to the controls. However, they showed worse metabolic profiles, with higher blood pressure, triglyceride, and insulin resistance parameters than controls; furthermore, these profiles were similar to those of the girls meeting the adolescent criteria. The at-risk group showed similarly elevated serum LH levels and LH/FSH ratio with the girls meeting adolescent criteria. LIMITATIONS, REASONS FOR CAUTION: We could not evaluate hormonal or ultrasound parameters in controls. WIDER IMPLICATIONS OF THE FINDINGS: Compared to the conventional Rotterdam criteria, the recent international evidence-based guideline-avoiding ultrasound in PCOS diagnosis in adolescents-still gives the opportunity to identify young girls at risk, aligned to the findings in this study. A practical approach to this adolescent population would involve establishing IM or HA (with ultrasound not indicated) and designating 'at-risk' PCOS status with regular check-ups for newly developed or worsening PCOS-related symptoms or metabolic abnormalities, with subsequent reassessment including ultrasound or anti-Müllerian hormone, once 8 years post-menarche. STUDY FUNDING/COMPETING INTEREST(S): No funding was received in support of this study. The authors have no conflicts of interest to disclose. TRIAL REGISTRATION NUMBER: N/A.

Abnormal uterine bleeding diagnoses and care following COVID-19 vaccination.

BACKGROUND: There is evidence suggesting that COVID-19 vaccination may be associated with small, transitory effects on uterine bleeding, possibly including menstrual timing, flow, and duration, in some individuals. However, changes in health care seeking, diagnosis, and workup for abnormal uterine bleeding in the COVID-19 vaccine era are less clear. OBJECTIVE: This study aimed to assess the impact of COVID-19 vaccination on incident abnormal uterine bleeding diagnosis and diagnostic evaluation in a large integrated health system. STUDY DESIGN: Using segmented regression, we assessed whether the availability of COVID-19 vaccines was associated with changes in monthly, population-based rates of incident abnormal uterine bleeding diagnoses relative to the prepandemic period in health system members aged 16 to 44 years who were not menopausal. We also compared clinical and demographic characteristics of patients diagnosed with incident abnormal uterine bleeding between December 2020 and October 13, 2021 by vaccination status (never vaccinated, vaccinated in the 60 days before diagnosis, vaccinated >60 days before diagnosis). Furthermore, we conducted detailed chart review of patients diagnosed with abnormal uterine bleeding within 1 to 60 days of COVID-19 vaccination in the same time period. RESULTS: In monthly populations ranging from 79,000 to 85,000 female health system members, incidence of abnormal uterine bleeding diagnosis per 100,000 person-days ranged from 8.97 to 19.19. There was no significant change in the level or trend in the incidence of abnormal uterine bleeding diagnoses between the prepandemic (January 2019-January 2020) and post-COVID-19 vaccine (December 2020-December 2021) periods. A comparison of clinical characteristics of 2717 abnormal uterine bleeding cases by vaccination status suggested that abnormal bleeding among recently vaccinated patients was similar or less severe than abnormal bleeding among patients who had never been vaccinated or those vaccinated >60 days before. There were also significant differences in age and race of patients with incident abnormal uterine bleeding diagnoses by vaccination status (Ps<.02). Never-vaccinated patients were the youngest and those vaccinated >60 days before were the oldest. The proportion of patients who were Black/African American was highest among never-vaccinated patients, and the proportion of Asian patients was higher among vaccinated patients. Chart review of 114 confirmed postvaccination abnormal uterine bleeding cases diagnosed from December 2020 through October 13, 2021 found that the most common symptoms reported were changes in timing, duration, and volume of bleeding. Approximately one-third of cases received no diagnostic workup; 57% had no etiology for the bleeding documented in the electronic health record. In 12% of cases, the patient mentioned or asked about a possible link between their bleeding and their recent COVID-19 vaccine. CONCLUSION: The availability of COVID-19 vaccination was not associated with a change in incidence of medically attended abnormal uterine bleeding in our population of over 79,000 female patients of reproductive age. In addition, among 2717 patients with abnormal uterine bleeding diagnoses in the period following COVID-19 vaccine availability, receipt of the vaccine was not associated with greater bleeding severity.

Menstrual cycle characteristics, perceived impact on performance, and barriers to communication: Perspectives of high-performance adolescent athletes in Singapore.

The purpose of this study was to examine the menstrual cycle (MC) characteristics, explore the impact on performance, and identify barriers to and facilitators of MC-related communication among high-performance female adolescent athletes in Singapore. Ninety athletes (15.4 ± 1.8 years) from multiple sports completed an online questionnaire. Eighty-four athletes were postmenarcheal (menarcheal age 11.9 ± 1.3 years), including two who were using an oral contraceptive pill (OCP). Secondary amenorrhea, current or history of, was self-reported in 16% of athletes. Sixty-two percent and 67% of non-OCP athletes perceived that the MC affected their ability to train and compete, respectively. Athletes preferred speaking to a parent (85%) and a female figure (67%) about MC-related concerns. Through thematic analysis, three barriers to communication were constructed: (1) pervasive menstrual stigma, (2) constraints of the training environment, and (3) the low value placed on MC-related conversations. Two facilitators of communication were constructed: (1) respect athletes' individual experiences as menstruating girls and (2) foster a safe space for MC-related conversations. Findings demonstrated that menstrual irregularities are common in adolescent athletes and screening for MC disorders, particularly primary amenorrhea should be undertaken in this population, with clear support pathways for management including symptom mitigation. To support athletes in raising MC-related concerns when needed, structured communication pathways that consider individual preferences and involve a (female) point of contact should be established within the training environment. Improving menstrual health literacy among adolescent athletes before any misinformation or negative perceptions are firmly established may contribute to longevity in their athletic careers.

The burden of menstrual irregularities among women living with HIV in Nigeria: a comprehensive review.

BACKGROUND: Menstrual irregularities significantly distress women living with HIV (WLHIV), impacting their reproductive health and quality of life. Although the underlying mechanism remains inconclusive, studies have outlined possible contributory factors. This narrative review explores the burden of menstrual irregularities and associated hormonal dysregulation among women living with HIV in Nigeria. It synthesises data from studies to present an overview of the prevalence, patterns, potential etiology, and impacts of menstrual irregularities among WLHIV. MAIN BODY: A literature search across electronic databases such as PubMed, Google Scholar, and Web of Science was conducted, and information was extracted and synthesized to delineate the burden of menstrual irregularities in WLHIV. Eligibility criteria included original studies assessing the prevalence, aetiology, and impact of menstrual abnormalities among WLHIV in Nigeria. A narrative data synthesis approach utilized common themes and key concept extraction, including identifying patterns in the literature to present specific trends such as prevalence, patterns, etiology, and determinants. Menstrual irregularities were found to be prevalent among Nigerian WLHIV, varying from 29 to 76% across different regions, exceeding reports of similar studies in developed nations. Similarly, menstrual disorders including amenorrhea, oligomenorrhea, and polymenorrhea, were attributed to factors like HIV acquisition, antiretroviral therapy, low body mass index, and hormonal imbalances. Low CD4 count and high viral load with associated complications have been identified as major contributing factors. Distortion of the hypogonadal-pituitary-ovarian axis by viral-induced pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-1 (IL-1), interleukin-6 (IL-6), and interferon-gamma (IFN-γ) may disrupt the hormonal balance necessary for regular menstrual cycles. Fluctuating levels of follicle-stimulating hormone (FSH), luteinising hormone (LH), estradiol, and prolactin have been reported among WLHIV. Although adherence to antiretroviral therapy has offered immense relief, its direct therapeutic effects on menstrual irregularities are inconclusive.. CONCLUSIONS: This study highlights the burden of menstrual disorders among WLHIV. It underscores the interplay between clinical, therapeutic, and client-associated factors as determinants of these abnormalities. Exploring associated complications like secondary infertility, reduced bone mineral density, and resultant osteoporosis, mirrors the significant impact of menstrual and hormonal irregularities on the reproductive health and quality of life of WLHIV.

The Characteristics of Long-Wave Irregularities in High-Speed Railway Vertical Curves and Method for Mitigation.

Track geometry measurements (TGMs) are a critical methodology for assessing the quality of track regularities and, thus, are essential for ensuring the safety and comfort of high-speed railway (HSR) operations. TGMs also serve as foundational datasets for engineering departments to devise daily maintenance and repair strategies. During routine maintenance, S-shaped long-wave irregularities (SLIs) were found to be present in the vertical direction from track geometry cars (TGCs) at the beginning and end of a vertical curve (VC). In this paper, we conduct a comprehensive analysis and comparison of the characteristics of these SLIs and design a long-wave filter for simulating inertial measurement systems (IMSs). This simulation experiment conclusively demonstrates that SLIs are not attributed to track geometric deformation from the design reference. Instead, imperfections in the longitudinal profile's design are what cause abrupt changes in the vehicle's acceleration, resulting in the measurement output of SLIs. Expanding upon this foundation, an additional investigation concerning the quantitative relationship between SLIs and longitudinal profiles is pursued. Finally, a method that involves the addition of a third-degree parabolic transition curve (TDPTC) or a full-wave sinusoidal transition curve (FSTC) is proposed for a smooth transition between the slope and the circular curve, designed to eliminate the abrupt changes in vertical acceleration and to mitigate SLIs. The correctness and effectiveness of this method are validated through filtering simulation experiments. These experiments indicate that the proposed method not only eliminates abrupt changes in vertical acceleration, but also significantly mitigates SLIs.

Graft Paste to Tackle Nasal Dorsal Irregularities: A Comparative, Prospective, Double Blinded Study.

BACKGROUND: Preventing dorsal irregularities, especially in noses with high humps, is still a challenging process. Classic treatment with diced grafts may itself result in dorsal irregularities. OBJECTIVES: It was aimed to investigate the effectiveness of graft paste in preventing and correcting the dorsal irregularities. METHODS: A total of 60 patients were included in this study. While diced cartilage was used in group A, graft paste was used in group B. Hump heights and collected graft volume were recorded. To evaluate aesthetic outcomes, preoperative and postoperative ROE questionnaire and postoperative physical examination were performed. RESULTS: Although the hump height of group A (5.9 ± 1.02 mm) was greater than that of group B (5.6 ± 1.15 mm), the collected graft volume in group B was statistically higher (P < 05) (0.26 ± 0.05 cc and 0.16 ± 0.13 cc, respectively). Group B showed higher postoperative ROE scores (84.67 ± 8.9) compared to group A (80.15 ± 7.6). While the mean physical examination score for group A was 1.12 ± 0.96, this value was 0.62 ± 0.71 for group B (P < 05). None of the patients of group B had visible irregularities, but two patients of group A had. CONCLUSION: The graft paste is a safe and reliable method to prevent and treat the dorsal irregularities. The paste has a soft and cohesive structure which makes it to ideal for filling the irregularities and the dead spaces on the surface of the dorsum. Graft paste was associated with a better aesthetic outcome compared to diced cartilage. LEVEL OF EVIDENCE I: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors https://www.springer.com/00266 .

Response to: Correction of Tear Trough Deformity with Minced Orbital Fat Grafts During Transconjunctival Lower Blepharoplasty.

Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Machine learning framework for simulation of artifacts in paranasal sinuses diagnosis using CT images.

In the medical field, diagnostic tools that make use of deep neural networks have reached a level of performance never before seen. A proper diagnosis of a patient's condition is crucial in modern medicine since it determines whether or not the patient will receive the care they need. Data from a sinus CT scan is uploaded to a computer and displayed on a high-definition monitor to give the surgeon a clear anatomical orientation before endoscopic sinus surgery. In this study, a unique method is presented for detecting and diagnosing paranasal sinus disorders using machine learning. The researchers behind the current study designed their own approach. To speed up diagnosis, one of the primary goals of our study is to create an algorithm that can accurately evaluate the paranasal sinuses in CT scans. The proposed technology makes it feasible to automatically cut down on the number of CT scan images that require investigators to manually search through them all. In addition, the approach offers an automatic segmentation that may be used to locate the paranasal sinus region and crop it accordingly. As a result, the suggested method dramatically reduces the amount of data that is necessary during the training phase. As a result, this results in an increase in the efficiency of the computer while retaining a high degree of performance accuracy. The suggested method not only successfully identifies sinus irregularities but also automatically executes the necessary segmentation without requiring any manual cropping. This eliminates the need for time-consuming and error-prone human labor. When tested with actual CT scans, the method in question was discovered to have an accuracy of 95.16 percent while retaining a sensitivity of 99.14 percent throughout.

The role of anti-Mullerian hormone and other correlates in patients with polycystic ovary syndrome.

BACKGROUND: Anti-Müllerian hormone (AMH) has recently emerged as a promising biomarker for the detection of polycystic ovarian morphology. In polycystic ovary syndrome (PCOS), an elevated level of AMH has been suggested to add value to the Rotterdam criteria in cases of diagnostic uncertainty. In this study, we evaluated the correlation between AMH and PCOS, and the potential role of AMH in PCOS diagnosis. METHODS: A case-control study was performed on a total of 200 females, 100 of which were diagnosed with PCOS as per Rotterdam revised criteria (2003) and 100 as the control (non-PCOS group). Patient medical records were therefore retrieved for clinical, biochemical and ultrasound markers for PCOS diagnosis. Sensitivity, specificity, area under receiver operating characteristic (AUROC) curve, and multivariate linear regression models were applied to analyze our data. RESULTS: Mean serum levels of LH and AMH, and LH/FSH ratio were significantly different between compared groups. In the PCOS group, the mean serum AMH level was 6.78 ng/mL and LH/FSH ratio was 1.53 while those of controls were 2.73 ng/mL and 0.53, respectively (p < .001). The most suitable compromise between 81% specificity and 79% sensitivity was obtained with a cutoff value of 3.75 ng/mL (26.78 pmol/L) serum AMH concentration for PCOS prediction, with an AUROC curve of 0.9691. CONCLUSION: Serum AMH cutoff level of 3.75 ng/mL was identified as a convenient gauge for the prediction of PCOS and an adjuvant to the Rotterdam criteria.

Morphological and Spectral Features of Ionospheric Structures at E- and F-Region Altitudes over Poker Flat Analyzed Using Modeling and Observations.

Electron density irregularities in the ionosphere modify the phase and amplitude of trans-ionospheric radio signals. We aim to characterize the spectral and morphological features of E- and F-region ionospheric irregularities likely to produce these fluctuations or "scintillations". To characterize them, we use a three-dimensional radio wave propagation model-"Satellite-beacon Ionospheric scintillation Global Model of upper Atmosphere" (SIGMA), along with the scintillation measurements observed by a cluster of six Global Positioning System (GPS) receivers called Scintillation Auroral GPS Array (SAGA) at Poker Flat, AK. An inverse method is used to derive the parameters that describe the irregularities by estimating the best fit of model outputs to GPS observations. We analyze in detail one E-region and two F-region events during geomagnetically active times and determine the E- and F-region irregularity characteristics using two different spectral models as input to SIGMA. Our results from the spectral analysis show that the E-region irregularities are more elongated along the magnetic field lines with rod-shaped structures, while the F-region irregularities have wing-like structures with irregularities extending both along and across the magnetic field lines. We also found that the spectral index of the E-region event is less than the spectral index of the F-region events. Additionally, the spectral slope on the ground at higher frequencies is less than the spectral slope at irregularity height. This study describes distinctive morphological and spectral features of irregularities at E- and F-regions for a handful of cases performed using a full 3D propagation model coupled with GPS observations and inversion.

Oscillations and variability in neuronal systems: interplay of autonomous transient dynamics and fast deterministic fluctuations.

Neuronal systems are subject to rapid fluctuations both intrinsically and externally. These fluctuations can be disruptive or constructive. We investigate the dynamic mechanisms underlying the interactions between rapidly fluctuating signals and the intrinsic properties of the target cells to produce variable and/or coherent responses. We use linearized and non-linear conductance-based models and piecewise constant (PWC) inputs with short duration pieces. The amplitude distributions of the constant pieces consist of arbitrary permutations of a baseline PWC function. In each trial within a given protocol we use one of these permutations and each protocol consists of a subset of all possible permutations, which is the only source of uncertainty in the protocol. We show that sustained oscillatory behavior can be generated in response to various forms of PWC inputs independently of whether the stable equilibria of the corresponding unperturbed systems are foci or nodes. The oscillatory voltage responses are amplified by the model nonlinearities and attenuated for conductance-based PWC inputs as compared to current-based PWC inputs, consistent with previous theoretical and experimental work. In addition, the voltage responses to PWC inputs exhibited variability across trials, which is reminiscent of the variability generated by stochastic noise (e.g., Gaussian white noise). Our analysis demonstrates that both oscillations and variability are the result of the interaction between the PWC input and the target cell's autonomous transient dynamics with little to no contribution from the dynamics in vicinities of the steady-state, and do not require input stochasticity.

Menstrual cycle irregularity during examination among female medical students at King Abdulaziz University, Jeddah, Saudi Arabia.

BACKGROUND: Menstrual irregularity is defined as any differences in the frequency, irregularity of onset, duration of flow, or volume of blood from the regular menstrual cycle. It is an important medical issue that many medical students suffer from. The study aimed to determine the menstrual cycle abnormalities women experienced during exams and to investigate the most common types of irregularities among female medical students at King Abdulaziz University in Jeddah, Saudi Arabia. METHODS: A cross-sectional study was conducted among female medical students between September and October 2021 at King Abdulaziz University in Jeddah, Saudi Arabia. For this study, the estimated sample size (n = 450) was derived from the online Raosoft sample size calculator. Thus, 450 female medical students from second to sixth year were selected through stratified random sampling. A validated online questionnaire collected data about demographics, menstrual irregularities during exams, type of irregularities, menstrual history, family history of menstrual irregularities, premenstrual symptoms, medication use, medical and family consultations, and absenteeism. The chi-squared test (χ2) was used to analyze the associations between variables. RESULTS: A total of 48.2% of participants had menstrual irregularities during exams. The most common irregularity was dysmenorrhea (70.9%), followed by a lengthened cycle (45.6%), and heavy bleeding (41.9%). A total of 93% of medical students suffered from premenstrual symptoms and 60.4% used medication such as herbal medication and home remedies to relieve menstrual irregularities, and 12.1% of the students missed classes due to menstrual irregularities. A non-significant relationship was found between menstrual irregularities during exams and students' demographics, academic year, and age at menarche, while oligomenorrhea, a heavier than normal bleed, a longer than normal cycle, and missing classes due to menstrual irregularities were significantly higher among single students as opposed to married students. CONCLUSION: The results showed that female medical students have a significant frequency of menstruation abnormalities during exams period. Colleges should raise awareness among medical students about coping with examination stress and seeking medical care for menstrual abnormalities.

Relationship between depressive symptoms and self-reported menstrual irregularities during adolescence: evidence from UDAYA, 2016.

BACKGROUND: The study examined the prevalence of self-reported menstrual irregularities during adolescence and explored the association of depressive symptoms with self-reported menstrual irregularities in adolescents in two major states of Uttar Pradesh and Bihar in India. METHODS: This study is based on the data obtained from the first round of the "Understanding the lives of adolescents and young adults" (UDAYA, 2016) survey. The effective sample size for the study was 12,707 adolescent girls aged 10-19 years. A bivariate analysis with chi-square test was conducted to determine the self-reported menstrual irregularity by predictor variables. Multivariable logistic regression models were employed to examine the associations between self-reported menstrual irregularity, depressive symptoms and other explanatory variables. RESULTS: A proportion of 11.22% of adolescent girls reported menstrual irregularity and 11.40% of the participants had mild depressive symptoms. Adolescent girls with mild (AOR: 2.15, CI: 1.85-2.51), moderate (AOR: 2.64, CI: 2.03-3.42) and severe depressive symptoms (AOR: 2.99, CI: 2.19-4.10) were more likely to have menstrual irregularity as compared to those who had minimal depressive symptoms. Physically active adolescent girls were less likely to report menstrual irregularity (AOR: 0.82, CI: 0.73-0.93) than physically inactive girls. Adolescent girls who used piece of cloth for menstrual hygiene practices (AOR: 1.17; CI: 1.02-1.35) and those who used either napkin or cloth or other materials (AOR: 1.32; CI: 1.14-1.54) had higher likelihood of menstrual irregularity as compared to those who used only sanitary napkins. CONCLUSION: A significant association of depressive symptoms with self-reported menstrual irregularity among adolescent girls was observed. Therefore, while treating females with irregular menstrual cycles, clinicians may need to pay greater attention to thir mental health peoblems.

IGS ROTI Maps: Current Status and Its Extension towards Equatorial Region and Southern Hemisphere.

The International GNSS Service (IGS) diurnal ROTI maps ionospheric product was developed to characterize ionospheric irregularities occurrence over the Northern hemisphere and has been available for the community since 2014. Currently, the diurnal ROTI maps database hosted by NASA CDDIS covers the period from 2010 to now. Here, we report the ROTI maps product operational status and important changes in the product availability and access. Apart from actual ROTI maps product production, we work on the extension of ROTI maps to cover not only the Northern hemisphere but also the area of the Southern hemisphere and equatorial/low latitude region. Such extended ROTI maps are important for ionospheric irregularities climatology research and ionospheric responses to space weather. We present recent development toward the new ROTI maps product and the updated data format. To evaluate extended the ROTI maps performance, we analyzed the ability to represent key features of ionospheric irregularity occurrence over the Southern hemisphere and low latitudes. For auroral and midlatitudes, we present the cross-comparison of ROTI-derived irregularities patterns over the Northern and Southern hemispheres. For low latitudes, we examined the sensitivity of the resulted ROTI maps to detect plasma irregularities associated with equatorial plasma bubbles development for low, middle, and high solar activity periods.

XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification.

Of the various tumour types, colorectal cancer and brain tumours are still considered among the most serious and deadly diseases in the world. Therefore, many researchers are interested in improving the accuracy and reliability of diagnostic medical machine learning models. In computer-aided diagnosis, self-supervised learning has been proven to be an effective solution when dealing with datasets with insufficient data annotations. However, medical image datasets often suffer from data irregularities, making the recognition task even more challenging. The class decomposition approach has provided a robust solution to such a challenging problem by simplifying the learning of class boundaries of a dataset. In this paper, we propose a robust self-supervised model, called XDecompo, to improve the transferability of features from the pretext task to the downstream task. XDecompo has been designed based on an affinity propagation-based class decomposition to effectively encourage learning of the class boundaries in the downstream task. XDecompo has an explainable component to highlight important pixels that contribute to classification and explain the effect of class decomposition on improving the speciality of extracted features. We also explore the generalisability of XDecompo in handling different medical datasets, such as histopathology for colorectal cancer and brain tumour images. The quantitative results demonstrate the robustness of XDecompo with high accuracy of 96.16% and 94.30% for CRC and brain tumour images, respectively. XDecompo has demonstrated its generalization capability and achieved high classification accuracy (both quantitatively and qualitatively) in different medical image datasets, compared with other models. Moreover, a post hoc explainable method has been used to validate the feature transferability, demonstrating highly accurate feature representations.

Nanoscale mapping of electric polarizability in a heterogeneous dielectric material with surface irregularities.

Nanoscale mapping of electric polarizability in a heterogeneous dielectric material with surface irregularities is of scientific and technical significance, but remains challenging. Here, we present an approach based on intermodulation electrostatic force microscopy (EFM) in conjunction with finite element computation for precise and high-resolution mapping of polarizability in dielectric materials. Instead of using electrostatic force in conventional quantitative EFM approaches, the force gradient is acquired to achieve an unprecedented spatial resolution. In the meantime, the finite element model is applied to eliminate the interference from the heterogeneity and surface irregularity of the sample. This approach directly reveals the high polarization ability of the amorphous region in a ferroelectric, semi-crystalline polymer with significant surface roughness, i.e. poly (vinylidene fluoride-co-chlorotrifluoroethylene), in which the result is consistent with the predicted data in the latest research. This work presenting a quantitative approach to nanoscale mapping of electric polarizability with unprecedented spatial resolution may help to reveal the complex property-structure correlation in heterogeneous dielectric materials.

Ground-Based GNSS and Satellite Observations of Auroral Ionospheric Irregularities during Geomagnetic Disturbances in August 2018.

The 25-26 August 2018 space weather event occurred during the solar minimum period and surprisingly became the third largest geomagnetic storm of the entire 24th solar cycle. We analyzed the ionospheric response at high latitudes of both hemispheres using multi-site ground-based GNSS observations and measurements onboard Swarm and DMSP satellites. With the storm development, the zones of intense ionospheric irregularities of auroral origin largely expanded in size and moved equatorward towards midlatitudes as far as ~55-60° magnetic latitude (MLAT) in the American, European, and Australian longitudinal sectors. The main ionospheric trough, associated with the equatorward side of the auroral oval, shifted as far equatorward as 45-50° MLAT at both hemispheres. The interhemispheric comparison revealed a high degree of similarity in a large expansion of the auroral irregularities oval towards midlatitudes, in addition to asymmetrical differences in terms of larger intensity of plasma density gradients and structures over the Southern auroral and polar cap regions. Evolution of the intense ionospheric irregularities and equatorward expansion of the auroral irregularities oval were well correlated with increases of geomagnetic activity and peaks of the auroral electrojet index.

A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision.

This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The kinematic description, that is borrowed from the multibody dynamics analysis of railway vehicles used in computer simulation codes, is used to calculate the relative motion between the vehicle and the track, and also for the computer vision system and its calibration. The multibody framework is thus used to find the formulas that are needed to calculate the track irregularities (gauge, cross-level, alignment and vertical profile) as a function of sensor data. The TGMS has been experimentally tested in a 1:10 scaled vehicle and track specifically designed for this investigation. The geometric irregularities of a 90 m-scale track have been measured with an alternative and accurate method and the results are compared with the results of the TGMS. Results show a good agreement between both methods of calculation of the geometric irregularities.

Abnormal Gait Detection Using Wearable Hall-Effect Sensors.

Abnormalities and irregularities in walking (gait) are predictors and indicators of both disease and injury. Gait has traditionally been monitored and analyzed in clinical settings using complex video (camera-based) systems, pressure mats, or a combination thereof. Wearable gait sensors offer the opportunity to collect data in natural settings and to complement data collected in clinical settings, thereby offering the potential to improve quality of care and diagnosis for those whose gait varies from healthy patterns of movement. This paper presents a gait monitoring system designed to be worn on the inner knee or upper thigh. It consists of low-power Hall-effect sensors positioned on one leg and a compact magnet positioned on the opposite leg. Wireless data collected from the sensor system were used to analyze stride width, stride width variability, cadence, and cadence variability for four different individuals engaged in normal gait, two types of abnormal gait, and two types of irregular gait. Using leg gap variability as a proxy for stride width variability, 81% of abnormal or irregular strides were accurately identified as different from normal stride. Cadence was surprisingly 100% accurate in identifying strides which strayed from normal, but variability in cadence provided no useful information. This highly sensitive, non-contact Hall-effect sensing method for gait monitoring offers the possibility for detecting visually imperceptible gait variability in natural settings. These nuanced changes in gait are valuable for predicting early stages of disease and also for indicating progress in recovering from injury.

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.