Discrepant Data Research Articles

Cosmological constraints from the eBOSS Lyman-α forest using the PRIYA simulations

We present new cosmological parameter constraints from the eBOSS Lyman-α forest survey. We use a new theoretical model and likelihood based on the PRIYA simulation suite. PRIYA is the first suite to resolve the Lyman-α forest in a (120 Mpc/h)3 volume, using a multi-fidelity emulation technique. We use PRIYA to predict Lyman-α forest observables with ≲ 1% interpolation error over an 11 dimensional (9 simulated, 2 in post-processing) parameter space. We identify an internal tension within the flux power spectrum data. Once the discrepant data is removed, we find the primeval scalar spectral index measured at a pivot scale of k 0 = 0.78 Mpc-1 to be nP = 1.009+0.027 -0.018 at 68% confidence. This measurement from the Lyman-α forest flux power spectrum alone is in reasonable agreement with Planck, and in tension with earlier eBOSS analyses. The amplitude of matter fluctuations is σ 8 = 0.733+0.026 -0.029 at 68% confidence, in agreement with Dark Energy Survey weak lensing measurements and other small-scale structure probes and in tension with CMB measurements from Planck and ACT. The effective optical depth to Lyman-α photons from our pipeline is in good agreement with earlier high resolution measurements. We find a linear power at z = 3 and k = 0.009 s/km of Δ2 L = 0.302+0.024 -0.027 with a slope n eff = -2.264+0.026 -0.018. Our flux power spectrum only chains prefer a low level of heating during helium reionization. When we add IGM temperature data we find nP = 0.983 ± 0.020 and σ 8 = 0.703+0.023 -0.027. Our chains prefer an early and long helium reionization event, as suggested by measurements from the helium Lyman-α forest. In the near future we will use our pipeline to infer cosmological parameters from the DESI Lyman-α data.

The impact of prior ACL reconstruction on total knee arthroplasty outcomes: a retrospective matched cohort study.

Discrepant data exists regarding the outcomes following total knee arthroplasty (TKA) with a prior anterior cruciate reconstruction (ACLR). The purpose of our study was to compare surgical and medical outcomes in the patients with prior ACLR undergoing TKAs compared to a matched control group of the patients who had undergone TKAs without prior ACLR. We hypothesized that the patients with prior ACLR would have inferior clinical outcomes. We retrospectively queried the PearlDiver-database for patients who underwent TKA following ACLR from 2011 to 2020. We used propensity-score matching to create two cohorts. The two-sided independent t-test and Chi-Squared test were used. We identified 2,174 patients who had prior ACLR before the TKAs. There were another 1,348,870 patients who did not have ACLR before the TKAs. After matching, each group had 2,171 patients. The ACLR-TKA group had significantly lower rates of aseptic revision at 2 years (1.2% vs. 4.0%, OR 0.3, p < 0.01), PJI requiring antibiotic spacer at 2 years (0.3% vs. 0.8%, OR 0.35, p = 0.02), and MUA at 90 days (0.4% vs. 7.5%, OR 0.05, p < 0.01). The rate of wound disruption was lower for the ACLR-TKA group at 90 days (p = 0.03) as were several medical complications including AKI at 90 days (p < 0.01), DVT at 90 days (p < 0.01), pneumonia at 90 days (0.04), and required blood transfusion at 90 days (p < 0.01). These results differed from our expectations. Within the limitations of the study, we are unable to determine the factors for the lower complications in the ACLR-TKA group. The data from this study are different from what had been reported in the previous studies.

EML: Emotion-Aware Meta Learning for Cross-Event False Information Detection

Modern social media’s development has dramatically changed how people obtain information. However, the wide dissemination of various false information has severely detrimental effects. Accordingly, many deep learning-based methods have been proposed to detect false information and achieve promising results. However, these methods are unsuitable for new events due to the extremely limited labeled data and their discrepant data distribution to existing events. Domain adaptation methods have been proposed to mitigate these problems. However, their performance is suboptimal because they are not sensitive to new events due to they aim to align the domain information between existing events, and they hardly capture the fine-grained difference between real and fake claims by only using semantic information. Therefore, we propose a novel Emotion-aware Meta Learning (EML) approach for cross-event false information early detection, which deeply integrates emotions in meta learning to find event-sensitive initialization parameters that quickly adapt to new events. Emotion-aware meta learning is non-trivial and faces three challenges: 1) How to effectively model semantic and emotional features to capture fine-grained differences. 2) How to reduce the impact of noise in meta learning based on semantic and emotional features. 3) How to detect the false information in a zero-shot detection scenario, i.e., no labeled data for new events. To tackle these challenges, firstly, we construct the emotion-aware meta tasks by selecting claims with similar and opposite emotions to the target claim other than usually used random sampling. Secondly, we propose a task weighting method and event-adaptation meta tasks to further improve the model’s robustness and generalization ability for detecting new events. Finally, we propose a weak label annotation method to extend EML to zero-shot detection according to the calculated labels’ confidence. Extensive experiments on real-world datasets show that the EML achieves superior performances on false information detection for new events.

High adherence to intermittent and continuous use of a contraceptive vaginal ring among women in a randomized controlled trial in Kigali, Rwanda.

The introduction of female-initiated drug-delivery methods, including vaginal rings, have proven to be a promising avenue to address sexually transmitted infections and unintended pregnancies, which disproportionally affects women and girls in sub-Saharan Africa. Efficient uptake of existing and new technologies such as vaginal rings requires in depth understanding of product adherence. This remains a major challenge as data on adherence to vaginal rings from African countries is limited. In this study, we explored adherence of contraceptive vaginal ring (NuvaRing®) use in Kigali, Rwanda using a mixed methods approach. We collected quantitative and qualitative data at multiple time points from women participating in a clinical trial exploring the safety and acceptability of either intermittent or continuous use of the NuvaRing®. Various adherence categories were used including monthly and cumulative adherence measurement. The quantitative data were analysed using R and the qualitative data were analysed using a deductive, content-analytical approach based on categories related to the quantitative adherence measures. All data were compared and triangulated. Data from 120 enrolled participants showed that self-reported adherence was high at every study visit in both study groups. At first study visit 80% of the intermittent ring users and 79.7% of the continuous ring users reported perfect adherence (assessed as "the ring was never out"). Reporting of ring expulsions and removals were highest (28.3%) at the beginning of the trial. Self-reported perfect ring adherence increased during the study and reports of ring expulsions and removals declined as familiarity with this contraceptive method increased. The percentage of women with perfect cumulative adherence was non-significantly higher in the intermittent (61.7%) than in the continuous use group (54.3%). The low rate of discrepant adherence data after triangulation (6%) is in line with the perception of the participants as adherent throughout the study. Self-reported adherence in both study groups was high with removals and expulsions being within the expected product range. Comprehensive adherence data triangulation allowed for a deeper understanding of context-driven behaviour that shaped adherence patterns and challenges. Our data categorisation and triangulation approach has shown potential for implementation in future vaginal ring studies aiming to better understand and measure adherence.

Research on the method of eliminating differences between small-sample databases based on cloud computing

Abstract As computer technology continues to advance, more and more people are using databases, leading to variations in data backup and transfer between databases. This paper proposes a small sample inter-database discrepancy data elimination method based on cloud computing architecture as a way to solve the problem of data synchronization discrepancy between different databases. The semantic features of the data in the database are represented in the form of a directed graph, and the semantic Gaussian marginalized data fusion system is constructed by combining the rectangular window function of Gaussian marginalization to realize the fusion filtering processing of discrepant data. Then, the particle swarm discriminant tree algorithm is used to extract the features of the difference data between the small sample databases, and the KL transform is used to compress the difference data to improve its confidence level. The rough weighted average single dependency method is introduced to detect and identify the difference data between small sample databases and combined with the artificial intelligence algorithm to construct the principal component feature set of the difference data in the small sample databases, thus realizing the elimination of the difference data between small sample databases. When the proportion of difference data is increased from 0.05% to 1.00%, the leakage alarm rate and false alarm rate of this paper’s method for the difference data between small sample databases are 0.113% and 0.099%, respectively. When eliminating the inter-database discrepancy data, its time consumption is between 0.06μs and 0.3μs, and the average value of the removal rate of discrepancy data can reach 95.54%. Small sample databases that utilize cloud computing technology can utilize a variety of differential data elimination algorithms to ensure high-quality migration and synchronized backup of inter-database data.

A Bad Data Resilient Multisensor Fusion Framework for Hybrid State Estimation

This paper proposes a novel estimation fusion approach for hybrid multi-stage power system state estimation. Hybrid estimation fusion architectures allow the incorporation of new classes of sensors without doing away with pre-existing estimators. The fusion module is responsible to produce final estimates based on the optimal merging of the individual estimators' outcomes. The proposed fusion formulation is based on the Maximum Correntropy Criterion (MCC), an optimization criterion borrowed from the information theoretic area that has been recently applied to conventional power system state estimation and enables the automatic suppression of bad data effects. In this paper, the correntropy-based formulation is used to replace the traditional minimum variance fusion methodology in the second stage of the hybrid estimation algorithm. To assess the applicability of the novel method, a three-sensor, MCC-based fusion architecture is evaluated through simulations conducted on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE</i> 14-bus, 30-bus, 57-bus, and 118-bus test systems. In the absence of bad data, the results confirm the expected compatibility between the proposed MCC-based fusion and the conventional minimum variance formulation. In addition, extensive experiments considering distinct bad data scenarios under several operational conditions have been performed. They illustrate a novel functionality imparted by the proposed approach to the fusion module, namely, the ability to automatically reject discrepant data. Therefore, MCC fusion provides an extra layer of protection against the harming effects of individual estimates contaminated with gross errors.

Effect of Rainfall and Technification on Arabica Coffee Productivity in Minas Gerais

Arabica coffee is the main agricultural product of the state of Minas Gerais and its production has great social and economic importance in the state. Several factors affect coffee productivity, such as climate, fertilization, soil, cultivars, management, disease incidence, and the presence of irrigation. More than 80% of the coffee crops in the state are rainfed, and are dependent on rainfall. The availability of water, especially during flowering and grain filling, is of great importance for productivity and the consequent economic return of the activities. Thus, the objective of this study was to evaluate the effect of rainfall from September to January on Arabica productivity in coffee-growing municipalities in Minas Gerais based on monthly accumulations limited to 210 mm. Municipal agricultural production data from IBGE from 2012 to 2019 for processed Arabica coffee and monthly rainfall data with 2.5 minutes spatial resolution, made available byWorldClim for the period 2010-2018, were used for this purpose. The ratio of productivity per accumulated rainfall was calculated for the coffee municipalities to determine the level of municipal technification and to filter out discrepant data. The municipalities were divided into technification groups and linear regression curves were determined for each group. For low and medium tech municipalities, a high correlation was obtained between rainfall from September to January, with monthly accumulations limited to 210 mm, and Arabica coffee productivity. In high tech municipalities, there was no significant correlation due to the presence of irrigation. The equations obtained showed the potential to estimate the effect of rainfall during the analyzed period on the productivity of municipalities in all regions of Minas Gerais and even productivity estimates based only on monthly rainfall.

Reviewing Redshifts and the Zero Point Energy

One key topic in astronomy is the redshift of light from distant galaxies. Currently there are two main models for what is observed, but discrepant data reveal that neither approach gives an adequate answer to the matter. In 1985 a suggestion was made that the answer to this problem may be found in the light emitted from the atoms within the galaxies themselves, rather than something happening to light in transit. That suggestion is examined with a more complete understanding of the vacuum Zero Point Energy (ZPE) that has emerged recently, since, on one view, it controls the electromagnetic properties of space. A review is undertaken of both the redshift and its problems and also the way our understanding of the ZPE has developed. The facts emerging from these reviews are then applied to suggest a different cause for the redshift, and a concordant reason for redshift quantization.

Impact of domiciliary administration of NSAIDs on COVID-19 hospital outcomes: an unCoVer analysis.

Background: Effective domiciliary treatment can be useful in the early phase of COVID-19 to limit disease progression, and pressure on hospitals. There are discrepant data on the use of non-steroidal anti-inflammatory drugs (NSAIDs). Aim of this study is to evaluate whether the clinical outcome of patients who were hospitalized for COVID-19 is influenced by domiciliary treatment with NSAIDs. Secondary objective was to explore the association between other patient characteristics/therapies and outcome. Methods: A large dataset of COVID-19 patients was created in the context of a European Union-funded project (unCoVer). The primary outcome was explored using a study level random effects meta-analysis for binary (multivariate logistic regression models) outcomes adjusted for selected factors, including demographics and other comorbidities. Results: 218 out of 1,144 patients reported use of NSAIDs before admission. No association between NSAIDs use and clinical outcome was found (unadj. OR: 0.96, 95%CI: 0.68-1.38). The model showed an independent upward risk of death with increasing age (OR 1.06; 95% CI 1.05-1.07) and male sex (1.36; 95% CI 1.04-1.76). Conclusion: In our study, the domiciliary use of NSAIDs did not show association with clinical outcome in patients hospitalized with COVID-19. Older ages and male sex were associated to an increased risk of death.

The Frequency of Differentiated Thyroid Cancer Recurrence in 2302 Patients With Excellent Response to Primary Therapy.

Discrepant data on the recurrence rate of differentiated thyroid cancer (DTC) are reported. To evaluate the frequency and risk factors of true recurrence in DTC patients with excellent responses (ExR) to initial therapy. A retrospective analysis of the 2302 consecutive DTC patients with ExR to primary therapy, treated during 24 years at single center. The percentage of recurrence and cumulative recurrence rate (CRR) were analyzed. Risk factors for recurrence for patients with papillary thyroid cancer (PTC) were investigated and methods for establishing a diagnosis of recurrence were evaluated. Of DTC patients, 32 (1.4%) experienced recurrence. PTC patients with recurrence were more likely to have younger age (P = .0182), larger tumor size (P = .0013), lymph node metastases (P = .0013), incomplete resection (P = .0446), higher ATA risk (P = .0002), and had more frequently been treated with 131I (P = .0203). CRRs at 5, 10, 15, 20, and 24 years after surgery were 1.2%, 1.9%, 2.5%, 2.9%, and 2.9%, respectively. The CRRs according to histological type were highest for poorly differentiated thyroid cancer (PDTC), lower for oncocytic (OTC) and follicular thyroid cancer (FTC), and lowest for PTC. Most recurrences occurred within the first 5 years of observation. The most effective method for detecting local recurrence was ultrasonography with fine needle aspiration cytology, and for distant metastases, 18F-FDG PET. True recurrence is rare in DTC patients. PTC patients with ExR to primary therapy and N0/Nx can be dismissed from oncological follow-up. Despite ExR to primary therapy, DTC patients with N1, and PDTC, OTC, FTC should remain under oncological follow-up.

Predictors and Pathophysiology of Axial Postural Abnormalities in Parkinsonism: A Scoping Review.

Postural abnormalities involving the trunk are referred to as axial postural abnormalities and can be observed in over 20% of patients with Parkinson's disease (PD) and in atypical parkinsonism. These symptoms are highly disabling and frequently associated with back pain and a worse quality of life in PD. Despite their frequency, little is known about the pathophysiology of these symptoms and scant data are reported about their clinical predictors, making it difficult to prompt prevention strategies. We conducted a scoping literature review of clinical predictors and pathophysiology of axial postural abnormalities in patients with parkinsonism to identify key concepts, theories and evidence on this topic. We applied a systematic approach to identify studies, appraise quality of evidence, summarize main findings, and highlight knowledge gaps. Ninety-two articles were reviewed: 25% reported on clinical predictors and 75% on pathophysiology. Most studies identified advanced disease stage and greater motor symptoms severity as independent clinical predictors in both PD and multiple system atrophy. Discrepant pathophysiology data suggested different potential central and peripheral pathogenic mechanisms. The recognition of clinical predictors and pathophysiology of axial postural abnormalities in parkinsonism is far from being elucidated due to literature bias, encompassing different inclusion criteria and measurement tools and heterogeneity of patient samples. Most studies identified advanced disease stage and higher burden of motor symptoms as possible clinical predictors. Pathophysiology data point toward many different (possibly non-mutually exclusive) mechanisms, including dystonia, rigidity, proprioceptive and vestibular impairment, and higher cognitive deficits.

High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 Data

Rice has always been one of the major food sources for human beings, and the monitoring and planning of cultivation areas to maintain food security and achieve sustainable development is critical for this crop. Traditional manual ground survey methods have been recognized as being laborious, while remote-sensing technology can perform the accurate mapping of paddy rice due to its unique data acquisition capabilities. The recently emerged Google Earth Engine (GEE) cloud-computing platform was found to be capable of storing and computing the resources required for the rapid processing of massive quantities of remote-sensing data, thereby revolutionizing traditional analysis patterns and offering unique advantages for large-scale crop mapping. Since the phenology of paddy rice depends on local climatic conditions, and considering the vast expanse of China with its outstanding geospatial heterogeneity, a zoning strategy was proposed in this study to separate the monsoon climate zone of China into two regions based on the Qinling Mountain–Huaihe River Line (Q-H Line), while discrepant basic data and algorithms have been adopted to separately map mid-season rice nationwide. For the northern regions, optical indices have been calculated based on Sentinel-2 images, growth spectral profiles have been constructed to identify phenological periods, and rice was mapped using One-Class Support Vector Machine (OCSVM); for the southern regions, microwave sequences have been constructed based on Sentinel-1 images, and rice was mapped using Random Forest (RF). By applying this methodological system, mid-season rice at 10 m spatial resolution was mapped on the GEE for the entire Chinese monsoon region in 2021. According to the accuracy evaluation coefficients and publicly released local statistical yearbook data, the relative error of the mapped areas in each province was limited to 10%, and the overall accuracy exceeded 85%. The results could indicate that mid-season rice can be mapped more accurately and efficiently on a China-wide scale with relatively few samples based on the proposed zoning strategy and mapping methods. By adjusting the parameters, the time interval for mapping could also be further extended. The powerful cloud-computing competence of the GEE platform was used to map rice on a large spatial scale, and the results can help governments to ascertain the distribution of mid-season rice across the country in a short-term period, which would be well suited to meeting the increasingly efficient and fine-grained decision-making and management requirements.

421 The role of itch resolution and skin clearance in patient-reported atopic dermatitis severity and quality of life: real-world insights from TARGET-DERM AD

Abstract Severity of atopic dermatitis (AD) itch and lesions is associated with poor quality of life. However, there is limited evidence describing the combined impact of itch and skin severity on patient outcomes in AD. This study aims to assess the independent and combined effects of itch and skin severity on patient-reported symptoms and quality-of-life outcomes. The study included adult participants (age ≥18 years) with AD enrolled in TARGET-DERM AD, an observational, longitudinal study of more than 3158 participants across 43 academic/community centers in the USA and Canada. Itch severity was assessed by the Patient-Reported Outcome Measurement Information System Itch-Severity, specifically the item evaluating ‘itch at its worst’, a 0–10 numeric rating scale. A score of 0 or 1 was interpreted as no/minimal itch. Skin severity was assessed by the validated Investigators Global Assessment of AD (vIGA-AD), with a score of 0 or 1 representing clear/almost clear skin (vIGA-AD 0/1). The association of AD symptoms representing patient-reported clear/almost clear disease [Patient-Oriented Eczema Measure (POEM) 0–2] and no impact of AD on quality of life [Dermatology Life Quality Index (DLQI) 0/1] with itch and skin severity was assessed using descriptive statistics and logistic regression models that included main and interaction effects for itch and skin severity. Among adult participants (n = 1795; 59% female; 60% non-Hispanic White; mean age 44.5 years), vIGA-AD, POEM, Worst Itch and DLQI data at enrollment were available for 95% (1702), 44% (792), 43% (783) and 43% (783) of participants, respectively. The proportion reporting POEM 0–2 and DLQI 0/1 was highest among those with no/minimal worst itch (73% POEM 0–2; 72% DLQI 0/1) and clear/almost clear skin (46% POEM 0–2; 45% DLQI 0/1), with decreasing proportions observed at greater itch and skin severity levels. Among those with both no/minimal itch and clear/almost clear skin, 87.5% (42/48) and 95.8% (46/48) reported POEM 0–2 and DLQI 0/1, respectively. Logistic regression results suggest that no/minimal itch and clear/almost clear skin are significantly associated with POEM 0–2 and DLQI 0/1, though the interaction effect was not statistically significant in despite all models assessed. Relative to those who did not have either no/minimal itch or clear/almost clear skin, the odds ratio of reporting POEM 0–2 and DLQI 0/1 was highest among those with both no/minimal itch and clear/almost clear skin (82.9 for POEM 0–2; 35.4 for DLQI 0/1), followed by no/minimal itch only (15.9 for POEM 0–2; 10.5 for DLQI 0/1) and clear/almost clear skin only (5.9 for POEM 0–2; 3.8 for DLQI 0/1). Complete or almost complete relief of itch and skin lesions is associated with greater odds of achieving ideal states in PROs (POEM and DLQI) with itch relief exhibiting a relatively higher impact than skin clearance. These results underscore the importance of assessing and documenting both itch severity and skin clearance to support shared decision-making. Moreover, clearance of both itch and skin lesions should be considered when setting treatment goals in patients with AD. Future research should include analyzing discrepant data (e.g. patients with no/minimal itch, but some skin lesions) to more precisely identify which aspect drives the patient-reported outcomes.

Association between Helicobacter pylori Infection and Nasal Polyps: A Systematic Review and Meta-Analysis.

Helicobacter pylori (H. pylori) has definite or possible associations with multiple local and distant manifestations. H. pylori has been isolated from multiple sites throughout the body, including the nose. Clinical non-randomized studies with H. pylori report discrepant data regarding the association between H. pylori infection and nasal polyps. The aim of this first systematic review and meta-analysis was the assessment of the strength of the association between H. pylori infection and incidence of nasal polyps. We performed an electronic search in the three major medical databases, namely PubMed, EMBASE and Cochrane, to extract and analyze data as per PRISMA guidelines. Out of 57 articles, 12 studies were graded as good quality for analysis. Male-to-female ratio was 2:1, and age ranged between 17-78 years. The cumulative pooled rate of H. pylori infection in the nasal polyp group was 32.3% (controls 17.8%). The comparison between the two groups revealed a more significant incidence of H. pylori infection among the nasal polyp group (OR 4.12), though with high heterogeneity I2 = 66%. Subgroup analysis demonstrated that in European studies, the prevalence of H. pylori infection among the nasal polyp group was significantly higher than in controls, yielding null heterogeneity. Subgroup analysis based on immunohistochemistry resulted in null heterogeneity with preserving a statistically significant difference in H. pylori infection prevalence between the groups. The present study revealed a positive association between H. pylori infection and nasal polyps.

Co-Parenting Impact on Breastfeeding: Systematic Review and Meta-Analysis.

Background: Although the beneficial effects of exclusive breastfeeding (EBF) on infants and mothers have been identified, EBF rates remain unsatisfactory. Co-parenting interventions for perinatal couples have not been systematically evaluated and analyzed for their effects on breastfeeding outcomes. Aims and Objectives: To systematically evaluate the effects of co-parenting interventions on the rate of EBF, breastfeeding knowledge, breastfeeding attitude, breastfeeding self-efficacy, parental relationship, and partner support. Methods: Randomized controlled trials and quasi-experimental studies were systematically screened in eight online databases from inception to November 2022. Trials included in this review were assessed using the Cochrane Risk of Bias Assessment Tool. Eligible trials were used to conduct a meta-analysis using Review Manager software. The I2 statistic was used to assess heterogeneity between studies. When it was not possible to conduct a meta-analysis, a descriptive analysis was used to present the findings due to insufficient data from the included studies. Results: Fifteen of the 1,869 articles reviewed met the inclusion criteria. Co-parenting interventions significantly improved the EBF rate at 16 weeks (odds ratio [OR] = 3.85, 95% confidence interval, CI [1.84 to 8.03], p < 0.001, I2 = 69%) and 6 months (OR = 2.82, 95% CI [1.47 to 5.41], p = 0.002, I2 = 85%). This study revealed that co-parenting interventions made statistically significant improvements in parental relationship (standardized mean difference [SMD] = 0.26, 95% CI [0.13 to 0.38], p < 0.001, I2 = 80%). There was no evidence of the effectiveness of interventions in terms of overall parental support (SMD = 0.75, 95% CI [-0.46 to 1.97], p < 0.001, I2 = 96%). Given the discrepant and limited research data, findings on breastfeeding knowledge, breastfeeding attitudes, and breastfeeding self-efficacy were presented descriptively. Conclusion: Co-parenting interventions effectively increase EBF rates at 16 weeks and 6 months postpartum, and improve breastfeeding knowledge, breastfeeding attitude, and parental relationships.

Translated consent documents (CDs) and use in non-industry sponsored studies (NISS).

6553 Background: Patients (pts) from historically underrepresented groups often have limited English proficiency (LEP). One challenge specific to obtaining consent from these pts to participate in clinical trials is the need for translated CDs. CD translation leads to enrollment delays and increases costs to study sponsors. We hypothesized that NISS, for which the principal investigator pays translation costs, would consent proportionately fewer pts with a primary language other than English and pts with LEP compared to industry sponsored studies (ISS). Methods: Pts’ primary language and English proficiency were assessed for all 12,082 consent events with appropriate available data at the Jonsson Comprehensive Cancer Center from 2013 to 2018. Pts with LEP were pts (or parent/guardian for pediatric pts) who had a primary language other than English, and the electronic health record indicated that the pt required a translator within 6 months of the consent date. CD language was documented per chart review when available, and when not, languages of available IRB-approved CDs were evaluated. The proportion of consent events for pts with a primary language other than English and with LEP were compared between NISS and ISS using generalized estimating equations, and the odds of signing CDs in the pts primary language were evaluated by multivariable analyses. Discrepant CD language data was evaluated by McNemar’s test. All analyses were performed using SAS 9 (SAS Institute, Cary, NC, USA). Results: Pts with a primary language other than English represented 8.1% of consent events in ISS vs 4.4% in NISS (p&lt;0.001) and 5.5% vs 2.8% (p&lt;0.001) for pts with LEP. Pts with a primary language other than English represented 4.5% vs 1.2% of consent events utilizing CDs in the pts’ primary language in ISS vs NISS (p&lt;0.001) and 3.7% vs 0.9% (p&lt;0.001) for pts with LEP. On multivariable analyses, the odds for pts with a primary language other than English of signing CDs to a NISS was 0.74 (CI, 0.63-0.94, p=0.005), and of signing CDs in their primary language was 0.38 (CI, 0.27-0.52, p&lt;0.001) compared to pts whose primary language was English. The odds for pts with LEP of signing CD to a NISS was 0.74 (CI, 0.58-0.95, p=0.02), and signing CDs in their primary language was 0.35 (CI, 0.25-0.50, p&lt;0.001) compared to pts whose primary language was English. Of 52 pts who signed CDs for both ≥1 NISS and ≥1 ISS, 18 signed in discrepant languages, with 16 of those 18 signing translated CDs for the ISS but not the NISS (p=0.002). Conclusions: Although the role of cost as an effect driver cannot be proven in a retrospective analysis, consistent with our hypothesis, the proportion of consent events utilizing appropriately translated CDs for pts with a primary language other than English and pts LEP was lower in NISS vs ISS, driven by lower odds of signing CDs and lower frequency of signing translated CDs. Approaches that reduce translation costs for investigators should be investigated.

Overviews of reviews in the cardiovascular field underreported critical methodological and transparency characteristics: a methodological study based on the Preferred Reporting Items for Overviews of Reviews (PRIOR) statement

ObjectivesThis study aimed to evaluate the epidemiology, reporting characteristics, and adherence to the Preferred Reporting Items for Overviews of Reviews (PRIOR) statement of overviews of reviews (overviews) of interventions in the cardiovascular field. Study Design and SettingMEDLINE, Scopus, and the Cochrane Database of Systematic Reviews were searched from January 1, 2000, to October 15, 2020. An updated search was performed in MEDLINE, Epistemonikos, and Google Scholar up to August 25, 2022. Overviews of interventions published in English and primarily considering populations, interventions, and outcomes pertinent to the cardiovascular field were eligible. Study selection, data extraction, and PRIOR adherence assessment were performed by two authors independently. ResultsWe analyzed 96 overviews. Almost half (43/96 [45%]) were published between 2020 and 2022 and contained a median of 15 systematic reviews (SRs) (interquartile range, 9–28). The commonest title terminology was “overview of (systematic) reviews” (38/96 [40%]). Methods for handling SR overlap were reported in 24/96 (25%), methods for assessing primary study overlap in 18/96 (19%), handling of discrepant data in 11/96 (11%), and methods for methodological quality or risk of bias assessment of the primary studies within SRs in 23/96 (24%). Authors included data sharing statements in 28/96 (29%), complete funding disclosure in 43/96 (45%), protocol registration in 43/96 (45%), and conflict of interest statement in 82/96 (85%) overviews. ConclusionInsufficient reporting was identified in methodological characteristics unique in overviews' conduct and most transparency markers. Adoption of PRIOR from the research community could ameliorate overviews’ reporting.

Biomarkers: Role and Scope in Neurological Disorders.

Neurological disorders pose a great threat to social health and are a major cause for mortality and morbidity. Effective drug development complemented with the improved drug therapy has made considerable progress towards easing symptoms associated with neurological illnesses, yet poor diagnosis and imprecise understanding of these disorders has led to imperfect treatment options. The scenario is complicated by the inability to extrapolate results of cell culture studies and transgenic models to clinical applications which has stagnated the process of improving drug therapy. In this context, the development of biomarkers has been viewed as beneficial to easing various pathological complications. A biomarker is measured and evaluated in order to gauge the physiological process or a pathological progression of a disease and such a marker can also indicate the clinical or pharmacological response to a therapeutic intervention. The development and identification of biomarkers for neurological disorders involves several issues including the complexity of the brain, unresolved discrepant data from experimental and clinical studies, poor clinical diagnostics, lack of functional endpoints, and high cost and complexity of techniques yet research in the area of biomarkers is highly desired. The present work describes existing biomarkers for various neurological disorders, provides support for the idea that biomarker development may ease our understanding underlying pathophysiology of these disorders and help to design and explore therapeutic targets for effective intervention.

How can a diverse set of integral and semi-integral measurements inform identification of discrepant nuclear data?

Nuclear data are used for a variety of applications, including criticality safety, reactor performance, and material safeguards. Despite the breadth of use-cases, the effective neutron multiplication factor, keff, of ICSBEP critical assemblies are primarily used for nuclear data validation; these are sensitive to specific energy regions and nuclides and are unable to uniquely constrain nuclear data. As a consequence, general-purpose nuclear data libraries, such as ENDF/B-VIII.0 [1], may have deficiencies that, while not apparent in criticality applications, negatively impact other applications, such as non-destructive analysis of special nuclear material [2, 3] and neutron diagnosed subcritical experiments [4]. Recent work by the Experiments Underpinned by Computational Learning for Improvements in Nuclear Data (EUCLID) project developed a machine learning tool, RAFIEKI, which uses random forests and the SHAP metric to determine which nuclear data contribute most to predicted bias between measured and simulated responses (e.g. keff). This paper contrasts RAFIEKI analysis applied to keff only against RAFIEKI analysis with keff paired with either LLNL pulsed sphere measurements or subcritical benchmarks. Two examples show that a) including pulsed sphere measurements substantially increases 9Be nuclear data importance to bias between 2 and 15 MeV, and b) including subcritical benchmarks has the potential for disentangling compensating errors between 240Pu (n,el) and (n,il) cross-sections between 0.1 and 10 MeV. These results show that RAFIEKI analysis applied to response sets that include, but go beyond, keff can aid nuclear data evaluators in identifying issues in nuclear data.

A Manifold-Based Airfoil Geometric-Feature Extraction and Discrepant Data Fusion Learning Method

The geometrical shape of airfoils and the corresponding flight conditions are crucial factors for aerodynamic coefficient prediction. The obtained geometric-features of airfoils in most existing approaches (e.g., geometrical parameter extraction, polynomial description and deep learning) are in Euclidean space. State-of-the-art studies have shown that the curves or surfaces of an airfoil form a manifold in Riemannian space. Therefore, the features extracted by existing methods are not sufficient to reflect the geometric-features of airfoils. Meanwhile, flight conditions and geometric-features are greatly discrepant with different types. The discrepancy between these two factors must be considered and evaluated to improve the aerodynamic coefficient accuracy. Motivated by the advantages of manifold theory and multi-task learning (MTL), we propose a manifold-based airfoil geometric-feature extraction and discrepant data fusion learning method (MDF) to extract geometric-features of airfoils in Riemannian space (we call them manifold-features) and further fuse the manifold-features with flight conditions to predict aerodynamic coefficients. Experimental results show that our method can extract geometric-features of airfoils more accurately than existing methods, that the average mean square error (MSE) of airfoils rebuilt based on geometric-features is reduced by 41.30%, and that while keeping the same prediction accuracy level for the lift coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{L}$</tex-math></inline-formula> , the MSE of the drag coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{D}$</tex-math></inline-formula> predicted by MDF is further reduced by 54.56%.