LIVE Database Research Articles

The Effect of Obesity on Postoperative Analgesia Practices and Complications Following Endoscopic Sinus Surgery: A Propensity Score-Matched Cohort Study.

Despite growing concern regarding over-prescription of narcotic pain medication following ambulatory surgery, little is known about the analgesic prescribing practices following endoscopic sinus surgery (ESS) in obese patients in comparison to non-obese patients. To compare the rates of opioid versus non-opioid prescriptions, the need for steroids, and post-operative adverse events between obese and non-obese adult patients undergoing ESS. Using TriNetX Live database, we identified all patients aged ≥18 years who underwent ESS (n = 1303) between 2014 and 2022 across several healthcare institutions across the state of Tennessee. We 1:1 propensity score-matched obese (BMI ≥ 30 kg/m2) and non-obese (18.5 kg/m2 ≤ BMI < 30 kg/m2) cohorts for age, gender, race, and comorbidities including asthma, nicotine dependence, and sleep apnea. Rates of prescriptions and post-operative adverse events between cohorts were analyzed using risk ratios (RR) and confidence intervals (CI). A toal of 532 obese patients were compared to 532 propensity score-matched non-obese patients in the first 14 post-operative days following ESS. The obese cohort was significantly more likely to be prescribed analgesics generally (RR = 1.72; 95% CI = 1.20-2.47), non-opioid analgesics (RR = 1.73; 95% CI = 1.19-2.50), and opioid analgesics (RR = 1.64; 95% CI = 1.14-2.36) than non-obese patients. There was no difference in rates of antibiotic or antiemetic prescription, prednisone/methylprednisolone, dexamethasone, ED visits, critical care service, epistaxis, transfusion, anemia, revision sinus surgery, mechanical ventilation, CPAP, or inhalation airway treatments. Obese patients undergoing ESS were significantly more likely to be prescribed non-opioid and opioid analgesia in the first 14 days post-operatively compared to non-obese patients. There were no differences in post-operative adverse events or other prescriptions. Otolaryngologists should be aware that obese patients are at increased risk of opioid induced airway obstruction and steroid induced hyperglycemia, especially in patients with comorbid sleep apnea or diabetes. Emphasis on non-opioid analgesics and multimodal pain management should be advocated for this population.

Antimicrobial Prescription Patterns for Acute Sinusitis 2015-2022: A Comparison to Published Guidelines.

Acute rhinosinusitis (ARS) is one of the most encountered conditions in primary care and otolaryngology clinics. However, little is known about how antibiotic prescription practices following a diagnosis of ARS compare to guidelines set forth by the American Academy of Otolaryngology in 2015. To investigate the epidemiology of ARS and the corresponding antibiotic prescribing practices by physicians and compare to published guidelines. Using the TriNetX Live database, we identified all patients diagnosed with ARS using the ICD10 code J01 between April 2015 and December 2022 across the state of Tennessee. After investigating the demographics of this cohort, we compared the first prescribed antibiotic within one day of ARS diagnosis to published guidelines. Antibiotics were grouped into their respective classes. Of 81 310 patients diagnosed with ARS identified in the specified time frame, 66% were Female, 49% were African American, 44% were White, and the mean age was 47 ± 20 years. The six most common initial antibiotics prescribed for ARS were erythromycins/macrolides [14 609 (25.8%)], amoxicillin/clavulanate [14 322 (25.3%)], amoxicillin [9300 (16.4%)], third generation cephalosporins [7733 (13.6%)], quinolones [3648 (6.4%)] and tetracyclines [2235 (3.9%)]. Of this cohort, 56 719 patients (69.8%) of patients were prescribed an antibiotic within one day of diagnosis. Despite published guidelines recommending amoxicillin with or without clavulanic acid as first-line treatment for ARS, only 42.2% of prescribed antibiotics followed this guideline in our cohort. While accounting for patients with penicillin allergy, the second-most represented antibiotics were erythromycins/macrolides, which are specifically recommended against due to high rates of S. Pneumoniae resistance. Our results suggest that further investigation into the causes of erythromycin/macrolide prescriptions as first line treatment for ARS and practices at other institutions should be conducted. In addition, building awareness around published ARS guidelines for physicians may be useful in improving antibiotic stewardship in treatment of ARS.

A no-reference image quality assessment model based on neighborhood component analysis and Gaussian process

In this paper, a human visual system (HVS) based no-reference image quality assessment model (NR-IQA) is proposed. This is an objective NR-IQA model that uses perceptual features such as structure and orientation to compute the loss of naturalness in the image. An adaptive feature extraction process is modeled to capture the underlying significant features of the images along with the noise. Moreover, the optimal subset of features is derived using the neighborhood component analysis and further used as inputs to the Gaussian process for regression. The various experimentations are carried out on LIVE, TID2008, and TID2013 databases to test the effectiveness of the proposed approach. It is observed that the presented model exhibits a competitive performance in comparison with the existing IQA approaches. Moreover, the computation complexity and run-time show the effectuality of the proposed approach. Further, the experiments show that the predicted score matches with human perceptions. Thus, the proposed model is more accurate, less complex, independent of distortions, and well-suited for real-time applications.

Feature rectification and enhancement for no-reference image quality assessment

The assessment of image is a critical task in computer vision, particularly in a no-referenced context, which presents numerous challenges. Though Deep neural networks have exhibited excellent performance on no-reference quality assessment (NR-IQA), the distortion-aware NR-IQA methods are limited to specific distortion type and struggle to handle realistic distortion scenarios. In this work, we propose a feature rectification and enhancement convolutional neural network namely FREIQA for NR-IQA. Our approach targets to fully utilize the multi-stage semantic features to produce a well fused and rectified score vector for effective image quality regression. Specifically, the multi-stage semantic features extracted by a pre-trained backbone would be rectified with a multi-level channel attention module before integrating to a global score vector. The fused score vector then will be gradually optimized through the perception feature provided by the multi-stage semantic feature, and finally sent to quality regression network to obtain the image quality score. Experimental results on public benchmarks including LIVE, TID2013, CSIQ, LIVEC, KADID-10k, KonIQ-10k and Waterloo exploration database show that our FREIQA achieve the state-of-the-art performance.

Toward a Live Homogeneous Database of Solar Active Regions Based on SOHO/MDI and SDO/HMI Synoptic Magnetograms. I. Automatic Detection and Calibration

Recent studies indicate that a small number of rogue solar active regions (ARs) may have a significant impact on the end-of-cycle polar field and the long-term behavior of solar activity. The impact of individual ARs can be qualified based on their magnetic field distribution. This motivates us to build a live homogeneous AR database in a series of papers. As the first of the series, we develop a method to automatically detect ARs from 1996 onward based on SOHO/MDI and SDO/HMI synoptic magnetograms. The method shows its advantages in excluding decayed ARs and unipolar regions and being compatible with any available synoptic magnetograms. The identified AR flux and area are calibrated based on the cotemporal SDO/HMI and SOHO/MDI data. The homogeneity and reliability of the database are further verified by comparing it with other relevant databases. We find that ARs with weaker flux have a weaker cycle dependence. Stronger ARs show a weaker cycle 24 compared with cycle 23. Several basic parameters, namely, the location, area, and flux of negative and positive polarities of the identified ARs, are provided in the paper. This paves the way for ARs’ new parameters quantifying the impact on the long-term behavior of solar activity to be presented in the subsequent paper of the series. The constantly updated database covering more than two full solar cycles will be beneficial for the understanding and prediction of the solar cycle. The database and the detection codes are accessible online.

Assessing the impact of habitat and captivity status on volatilome profiles of the illegally traded shingleback, Tiliqua rugosa

Volatilomics is the study of the total biogenic volatile organic compounds (BVOCs) produced by an organism. This field has been used to assess organism and ecosystem health, as well as determine BVOC biomarkers for forensic purposes, including the detection of human remains, ignitable liquid residues and illicit drugs. For volatilomics to be applied in wildlife-victim casework (e.g. the illegal wildlife trade) a large reference database must be collected across each targeted species range. Adequate sample sizes must be collected from different habitats spanning across the species range of the targeted species to evaluate volatilome variability associated with different environmental and dietary characteristics. This will allow for assessment of chemical diversity and the determination of BVOC biomarkers that are relevant to wildlife forensic cases (e.g. detection, species identification, geographic origin assessment). This study collected the first live animal volatilome database, using the highly trafficked and widely distributed Australian shingleback lizard (Tiliqua rugosa). Optimised thermal desorption and analysis methods were used to examine 127 wild shingleback volatilome samples collected from sites across New South Wales, South Australia and Western Australia and 28 volatilome samples from captive shinglebacks. The results demonstrated that volatilome profiles and chemical diversity differed across each sampling region, potentially related to habitat and diet changes. At least 7 volatilome samples were required to capture chemical diversity in a sampling region. Forty-four tentatively identified BVOCs were shared across all sampling regions and captive animals which may aid in detection purposes. Bioregion-specific BVOCs were also identified, which will also aid in geographic assignment of confiscated individuals. This work demonstrates the importance of sample sizes in capturing chemical diversity within Bioregions prior to downstream volatilome analysis for the establishment of wildlife forensic databases and biomarker selection.

Unifying Dual-Attention and Siamese Transformer Network for Full-Reference Image Quality Assessment

Image Quality Assessment (IQA) is a critical task of computer vision. Most Full-Reference (FR) IQA methods have limitation in the accurate prediction of perceptual qualities of the traditional distorted images and the Generative Adversarial Networks (GANs) based distorted images. To address this issue, we propose a novel method by Unifying Dual-Attention and Siamese Transformer Network (UniDASTN) for FR-IQA. An important contribution is the spatial attention module composed of a Siamese Transformer Network and a feature fusion block. It can focus on significant regions and effectively maps the perceptual differences between the reference and distorted images to a latent distance for distortion evaluation. Another contribution is the dual-attention strategy that exploits channel attention and spatial attention to aggregate features for enhancing distortion sensitivity. In addition, a novel loss function is designed by jointly exploiting Mean Square Error (MSE), bidirectional Kullback–Leibler divergence, and rank order of quality scores. The designed loss function can offer stable training and thus enables the proposed UniDASTN to effectively learn visual perceptual image quality. Extensive experiments on standard IQA databases are conducted to validate the effectiveness of the proposed UniDASTN. The IQA results demonstrate that the proposed UniDASTN outperforms some state-of-the-art FR-IQA methods on the LIVE, CSIQ, TID2013, and PIPAL databases.

Image Quality Score Distribution Prediction via Alpha Stable Model

Based on potentially subjective and diverse image quality scores given by a group of subjects, we propose to predict the distribution of image quality scores rather than the mean opinion score (MOS) of image quality. Therefore, in this paper, we use an alpha stable model to parameterize the image quality score distribution (IQSD), and propose an objective method to predict the alpha-stable-model-based IQSD. First, the LIVE database is re-recorded. Specifically, we invite a large group of subjects (187 valid subjects) to evaluate the quality of all 808 images in the LIVE database, with their scores forming reliable IQSDs. All images in the LIVE database and their collected subjective quality scores form a new image quality assessment database, named the SJTU IQSD database. We then propose a framework and algorithm to predict the alpha-stable-model-based IQSD, in which quality features are extracted from the structural and natural statistical information of each image, and support vector regressors are trained to predict the alpha stable model parameters. Experiments carried out on the SJTU IQSD database verify the feasibility of using the alpha stable model to describe the IQSD, and the experimental results show that the alpha-stable-model-based IQSD can reflect a large amount of subjective information on image quality. We also prove that the objective alpha-stable-model-based IQSD prediction method is effective. The code and the SJTU IQSD database can be downloaded at ‘https://github.com/YixuanGao98/Image-Quality-Score-Distribution-Prediction-via-Alpha-Stable-Model.git’.

Risk factors associated with third- and fourth-degree perineal lacerations in singleton vaginal deliveries: a comprehensive United States population analysis 2016-2020.

Perineal lacerations are a common complication of vaginal birth, affecting approximately 85 % of patients. Third-and fourth-degree perineal lacerations (3/4PL) remain a significant cause of physical and emotional distress. We aimed to perform an extensive assessment of potential risk factors for 3/4PL based on a comprehensive and current US population database. Retrospective population-based cohort analysis based on the US Centers for Disease Control and Prevention Natality Live Birth online database between 2016-2020. Baseline characteristics were compared between women with 3/4PL and without 3/4PL by using Pearson's Chi-squared test with statistical significance set at p<0.05. Bonferroni correction was used to account for multiple comparisons. Multivariable logistic regression was performed to evaluate the association between a variety of potential risk factors and the risk of 3/4P. Asians/Pacific Islanders had the highest risk of 3/4PL (2.6 %, aOR 1.74). Gestational hypertension and preeclampsia were associated with increased risk of 3/4PL (aOR1.28 and 1.34, respectively), as were both pre-gestational and gestational diabetes (aOR 1.28 and 1.46, respectively). Chorioamnionitis was associated almost double the risk (aOR 1.86). Birth weight was a major risk factor (aOR 7.42 for greater than 4,000 g), as was nulliparity (aOR 9.89). We identified several maternal, fetal, and pregnancy conditions that are associated with an increased risk for 3/4PL. As expected, nulliparity and increased birth weight were associated with the highest risk. Moreover, pregestational and gestational diabetes, hypertensive disorders of pregnancy, Asian/Pacific Islander race, and chorioamnionitis were identified as novel risk factors.

Diabetes and Hypertensive Disorders of Pregnancy and the Risk of Third- and Fourth-Degree Perineal Lacerations: A Large United States Cohort 2016–2020 [ID: 1377605

INTRODUCTION: The objective of this study was to assess the effect diabetes and hypertensive disorders of pregnancy have on the rate of third- and fourth-degree perineal laceration (3/4 PL). METHODS: This represents a retrospective cohort based on the Centers for Disease Control and Prevention Natality Live Birth database (2016–2020). We compared the rates of 3/4 PL in term, singleton vaginal births across the United States based on maternal gestational hypertension, preeclampsia, pregestational diabetes, and gestational diabetes. We used Pearson χ2 and multivariate logistic regression to control for confounding variables. Statistical significance was determined as P&lt;.05, and results were displayed as adjusted odds ratios (aORs) with 95% CI. RESULTS: Of the 11,677,671 who met inclusion criteria, 139,174 (1.2%) women experienced 3/4 PL. Gestational hypertension and preeclampsia were associated with increased rates of 3/4 PL (1.53% with aOR 1.28, 95% CI 1.25–1.30, and 1.61% with aOR 1.31, 95% CI 1.06–1.17, respectively). Additionally, both pregestational diabetes and gestational diabetes were associated with higher rates (1.44% with aOR 1.45, 95% CI 1.35–1.56, and 1.44% with aOR 1.27, 95% CI 1.25–1.30, respectively). CONCLUSION: Based on this current U.S. database, we identified several medical complications of pregnancy that are associated with an increased risk for 3/4 PL. Specifically, hypertensive disorders of pregnancy (gestational hypertension and preeclampsia), pregestational as well as gestational diabetes were all associated with significantly increased risks. Understanding these associations may contribute to reduction in the rates of 3/4 PL in gravidas with these conditions.

Hypertensive Disorders of Pregnancy During the COVID-19 Pandemic: A Population-Based Study [ID: 1380265

INTRODUCTION: We previously reported that the prevalence of hypertensive disorders of pregnancy (HDP) increased during the COVID-19 pandemic. We aimed to ascertain potential sociodemographic factors contributing to this association. METHODS: This was a retrospective analysis of the U.S. Centers for Disease Control and Prevention Natality Live Birth database (2016–2020). The prevalence of HDP, defined as a composite of gestational hypertension, preeclampsia, and eclampsia, was compared between the COVID-19 pandemic group (births from April 1, 2020, to December 31, 2020; n=2,728,674) and the pre-pandemic group (births from January 1, 2016, to February 29, 2020; n=15,967,803). A comparison of the prevalence of HDP among maternal sociodemographic factors and pregnancy characteristics between the two groups was also performed using χ2 test. Multivariable logistic regression was used to adjust for potential confounders. Statistical significance was set at P&lt;.05. RESULTS: We detected an overall increase in odds of HDP during the COVID-19 pandemic period (8.7% versus 7.1%, adjusted odds ratio 1.25, 95% CI 1.24–1.26). The highest prevalence of HDP during the pandemic was seen in similar sociodemographic subgroups as in the pre-pandemic group, such as older maternal age, patients with some college credit, private insurance, and non-Hispanic Black race. A marked increase in HDP prevalence was noted during the pandemic in most maternal characteristic subgroups evaluated, with the greatest noted in Asian individuals and tobacco users. Interestingly, chronic hypertension, which was more prevalent during the pandemic period (1.2% versus 0.9%, P≤.001), was associated with a lower prevalence of HDP during that time. CONCLUSION: The prevalence of HDP increased in the United States during the COVID-19 pandemic among all sociodemographic factors evaluated. Although chronic hypertension was more prevalent during the pandemic period, it had a lower effect on the development of HDP during that time.

Chorioamnionitis Is an Independent Risk Factor for Third- and Fourth-Degree Perineal Lacerations in the United States [ID: 1377609

INTRODUCTION: The objective was to determine the effect chorioamnionitis has on the risk of third- and fourth-degree perineal lacerations (3/4 PL). METHODS: This represents a retrospective cohort based on the Centers for Disease Control and Prevention Natality Live Birth database (2016–2020). We compared the rates of 3/4 PL in term, singleton vaginal births across the United States based on diagnosed intrapartum chorioamnionitis. We used Pearson chi-square and multivariate logistic regression to control for confounding variables. Statistical significance was determined as P&lt;.05, and results were displayed as adjusted odds ratios (aORs) with 95% CI. RESULTS: Of the 11,677,671 who met inclusion criteria, 139,174 (1.2%) women experienced 3/4 PL. Intrapartum chorioamnionitis was associated with a staggering 3.96% rate of 3/4 PL compared to a 1.15% rate in women without chorioamnionitis (aOR 1.85, 95% CI 1.80–1.90). CONCLUSION: Based on this current U.S. database we identified chorioamnionitis as a major risk factor for 3/4 PL. The potential explanation may be due to dysfunctional labor, increased perineal edema secondary to protracted labor, or increased friability of tissue. Understanding this association may assist in better counseling and potentially reduce the rates of 3/4 PL in gravidas with chorioamnionitis.

Risk factors and adverse outcomes associated with syphilis infection during pregnancy

The prevalence of syphilis infection in pregnancy is increasing at an alarming rate. This study aimed to evaluate sociodemographic risk factors and adverse pregnancy outcomes associated with syphilis infection during pregnancy in a current US population of live births. This was a retrospective analysis of the Centers for Disease Control and Prevention, Natality Live Birth database for the years 2016 to 2019. All live births were eligible for inclusion. Deliveries with missing data on syphilis infection were excluded. We analyzed the database comparing pregnancies complicated by maternal infection with syphilis with those without infection. Several maternal sociodemographic factors and adverse pregnancy and neonatal outcomes were compared between the 2 groups. Multivariable logistic regression was performed to evaluate the association of these factors with syphilis infection in pregnancy, and adverse pregnancy and neonatal outcomes while adjusting for potential confounders. Data were presented as adjusted odds ratios with 95% confidence intervals. Of the 15,341,868 births included, 17,408 (0.11%) were complicated by maternal infection with syphilis. Concurrent infection with gonorrhea was associated with the highest risk of syphilis in pregnancy (adjusted odds ratio, 7.24; 95% confidence interval, 6.79-7.72). Low educational attainment (less than high school: adjusted odds ratio, 4.40; 95% confidence interval, 3.93-4.92), non-Hispanic Black race/ethnicity (adjusted odds ratio, 3.81; 95% confidence interval, 3.65-3.98), and Medicaid insurance (adjusted odds ratio, 2.13; 95% confidence interval, 2.03-2.23) were also associated with a significantly increased risk of infection. Syphilis infection was associated with an increased risk for preterm birth (<37 weeks: adjusted odds ratio, 1.25; 95% confidence interval, 1.20-1.31; <32 weeks: adjusted odds ratio, 1.26; 95% confidence interval, 1.16-13.7), low birthweight (adjusted odds ratio, 1.34; 95% confidence interval, 1.28-1.40), congenital malformations (adjusted odds ratio, 1.43; 95% confidence interval, 1.14-1.78), low 5-minute Apgar scores (adjusted odds ratio, 1.29; 95% confidence interval, 1.19-1.41), neonatal intensive care unit admission (adjusted odds ratio, 2.19; 95% confidence interval, 2.11-2.28), immediate ventilation (adjusted odds ratio, 1.48; 95% confidence interval, 1.39-1.57), and prolonged ventilation (adjusted odds ratio, 1.58; 95% confidence interval, 1.44-1.73). We identified several risk factors and adverse pregnancy outcomes associated with syphilis infection in pregnancy. Given the concerning rise in prevalence of pregnancy infections, public health strategies aimed at infection prevention and access to timely screening and treatment to reduce associated adverse pregnancy outcomes are urgently needed.

Rainfall Prediction Rate in Saudi Arabia Using Improved Machine Learning Techniques

Every farmer requires access to rainfall prediction (RP) to continue their exploration of harvest yield. The proper use of water assets, the successful collection of water, and the successful pre-growth of water construction all depend on an accurate assessment of rainfall. The prediction of heavy rain and the provision of information regarding natural catastrophes are two of the most challenging factors in this regard. In the twentieth century, RP was the most methodically and technically complicated issue worldwide. Weather prediction may be used to calculate and analyse the behaviour of weather with unique features and to determine rainfall patterns at an exact locale. To this end, a variety of methodologies have been used to determine the rainfall intensity in Saudi Arabia. The classification methods of data mining (DM) approaches that estimate rainfall both numerically and categorically can be used to achieve RP. This study, which used DM approaches, achieved greater accuracy in RP than conventional statistical methods. This study was conducted to test the efficacy of several machine learning (ML) approaches for forecasting rainfall, utilising southern Saudi Arabia’s historical weather data obtained from the live database that comprises various meteorological data variables. Accurate crop yield predictions are crucial and would undoubtedly assist farmers. While engineers have developed analysis systems whose performance relies on several connected factors, these methods are seldom used despite their potential for precise crop yield forecasts. For this reason, agricultural forecasting should make use of these methods. The impact of drought on crop yield can be difficult to forecast and there is a need for careful preparation regarding crop choice, planting window, harvest motive, and storage space. In this study, the relevant characteristics required to predict precipitation were identified and the ML approach utilised is an innovative classification method that can be used determine whether the predicted rainfall will be regular or heavy. The outcomes of several different methodologies, including accuracy, error, recall, F-measure, RMSE, and MAE, are used to evaluate the performance metrics. Based on this evaluation, it is determined that DT provides the highest level of accuracy. The accuracy of the Function Fitting Artificial Neural Network classifier (FFANN) is 96.1%, which is higher than that of any of the other classifiers currently used in the rainfall database.

Plagiarism vs Similarity Index: A Critical Insight

The word Plagiarism is derived from the Latin term “plagiarius”, meaning “kidnapper” [1]. Plagiarism severely violates publication ethics and professional conduct [2]. It may be defined as an unethical intentional or unintentional piracy of someone else idea/s or text without acknowledgement [1]. Intentional Plagiarism usually occurs when some educational credentials, professional promotions, or economic benefits might benefit the concerned author(s)—unintentional plagiarism results either from negligence or lack of awareness about plagiarism [3].The first incidence of plagiarism was detected in the year 1979 in a scientific paper. A later number of papers were found to be plagiarised [4]. A report published in 2018 showed an increased number of retractions of scientific papers in the last two decades due to plagiarism [5]. The primary reason for the increase in plagiarism by the scientific community could be a mandatory requirement to publish for employment and promotions. In addition, lack of skill in scientific writing and stringent policies related to plagiarism [4]. The availability of advanced text formatting tools and free access to scientific information may also reason for increased cases of plagiarism [6]. In the early days, plagiarism detection was challenging for the publisher due to the unavailability of sophisticated screening technology for reviewing manuscripts against published hard copies of the articles. Advanced tools for detecting plagiarism, such as iThenticate (Crossref), Turnitin, Grammarly, and Dupli Checker, are available to compare manuscripts with published articles [3,4]. Recently, iParadigms has developed a plagiarism detection tool for individual authors to screen individual manuscripts against an extensive live database of scholarly literature [7].

The timing database: An open-access, live repository for interval timing studies.

Interval timing refers to the ability to perceive and remember intervals in the seconds to minutes range. Our contemporary understanding of interval timing is derived from relatively small-scale, isolated studies that investigate a limited range of intervals with a small sample size, usually based on a single task. Consequently, the conclusions drawn from individual studies are not readily generalizable to other tasks, conditions, and task parameters. The current paper presents a live database that presents raw data from interval timing studies (currently composed of 68 datasets from eight different tasks incorporating various interval and temporal order judgments) with an online graphical user interface to easily select, compile, and download the data organized in a standard format. The Timing Database aims to promote and cultivate key and novel analyses of our timing ability by making published and future datasets accessible as open-source resources for the entire research community. In the current paper, we showcase the use of the database by testing various core ideas based on data compiled across studies (i.e., temporal accuracy, scalar property, location of the point of subjective equality, malleability of timing precision). The Timing Database will serve as the repository for interval timing studies through the submission of new datasets.

Temporal trend of congenital syphilis in the most populous municipality of metropolitan region II of Rio de Janeiro state.

This study aimed to explore the temporal trend in congenital syphilis, according to sociodemographic and prenatal care in the city of São Gonçalo - Rio de Janeiro, from 2007 to 2018. Ecological time series study, with data from SINAN (Information System for Notifiable Diseases) and SINASC (Information System on Live Births databases). We calculated annual incidence (per 1,000 live births) according to sociodemographic and prenatal variables. For the same variables, we calculated trends by logarithmic regression (Joinpoint Regression), estimating the annual percentage change. A total of 2,420 cases were reported from 2007 to 2018, with an increasing trend: 64.9% per year (2010-2013) and 24.9% (2013-2018). In 2018, the highest rates were in adolescents (90.6/1,000 live births), black women (87.6/1,000 live births), low-educated women (122.8/1,000 live births), and those without prenatal care (677.4/1,000 live births). The annual percentage change of these categories was, respectively, 37.3% (2010-2018), 33.5% (2012-2018), 39.9% (2014-2018), and 85.0% (2011-2015), but all categories showed a crescent trend. We identified high congenital syphilis incidences and crescent trends, especially in more vulnerable groups, pointing to social and healthcare inequalities. Prenatal care needs to be more comprehensive and qualified, primarily for young, low-educated, and black women.

Neonatal mortality by gestational age in days in infants born at term: A cohort study in Sao Paulo city, Brazil.

Birth at term comprises a period with heterogeneous neonatal outcomes that tend to be worse for infants born earlier. However, few studies have analyzed this period, in which each day can make a difference. Therefore, we aim to assess neonatal mortality (NM) according to gestational age (GA) at birth measured in days in term liveborn infants born in 2012-2017 in São Paulo, the largest city in Latin America. This population-based cohort study assessed term liveborn infants followed until the end of the neonatal period. We analyzed 7 models for NM according to GA in days: crude NM adjusted for maternal and prenatal variables, NM additionally adjusted for type of birth and type of hospital, and adjusted NM stratified by type of birth (cesarean and vaginal) and by type of hospital (public and private). We included 440,119 live infants born at 259-293 days of gestation. The median GA at birth was 274 days. In all models, NM was higher for infants born early term, decreasing in infants born full term and rising again in infants born late term. In the unadjusted model, hazard ratios of NM changed daily, decreasing from 3.34 to 1.00 on day 278 and increasing again thereafter. In the stratified analysis according to type of hospital, being born in a public hospital was associated with a reduced risk of NM for infants born at 278-283 days of pregnancy. There was a decrease in GA related to obstetric interventions, especially cesarean sections, which increased NM. The loss of days of pregnancy was larger in private hospitals. Increasing the granularity of GA to days is feasible and has the potential to drive public policies. To the best of our knowledge, this is the first Brazilian study on GA in days using a national live births database.

A living mapping review for COVID-19 funded research projects: 21 month update

Background: The coronavirus disease 2019 (COVID-19) has resulted in an unprecedented research response, demonstrating exceptional examples of rapid research and collaboration. There has however been an ongoing need for greater coordination, with limited resources for research and the shifting global pandemic. Methods: The UK Collaborative on Development Research (UKCDR) and Global Research Collaboration for Infectious Disease Preparedness (GloPID-R), two funder coordination groups have collaborated to develop a live database of funded research projects across the world relating to COVID-19. Drawing data continually from their members and further global funding bodies, as of 15 th April 2022 the database contains 16,353 projects, funded by 319 funders, taking place across 157 countries representing an investment of at least $6.2 billion. To our knowledge it is one of the most comprehensive databases. The database is aligned to the World Health Organisation and GloPID-R Global Research Roadmap: 2019 Novel Coronavirus and the UN Research Roadmap for the COVID-19 Recovery. It is being used by the WHO, governments and further policy makers, research funders and researchers. This living mapping review aims to supplement the database by providing an open accessible and frequently updated resource summarising the characteristics of the COVID-19 funded research portfolio. Both descriptive and thematic analysis are presented and updated frequently to aid interpretation of the global COVID-19 funded research portfolio. Results: In this version eight analysis we provide an updated detailed descriptive analysis of the database (on data from three months after version seven) and focus our thematic analysis on research gaps, research areas in need of coordination, study populations and research locations (with a focus on resource-limited countries). Conclusions: As the global funding response to COVID-19 plateaus, this living mapping review helps both funders and researchers to prioritise resources to areas where there is continued unmet research need.

No-Reference Framework for Image Quality Assessment

The technological advances in computer and communication devices have been an important factor in the appearance for the applications of visual communication. Image quality assessment is one of the pillars for these applications. It is a measure to assess the quality of images. There are many degradations in image quality that occur during the reproduction and transmission of the image. This paper aims to introduce a framework of No-Reference Image Quality Assessment. The framework provides a general estimation for three types of image distortions which are sharpness, blackness, and noisiness. Based on this framework, experiments have been conducted by the use of two datasets (LIVE database, CSIQ database). The results of the experiments have shown that we have introduced a more precise framework of No-Reference Image Quality Assessment.