Level Data Research Articles

Oceanographic conditions of spring migration of European anchovy as a factor of formation of its spawning stock in the Sea of Azov

The aim of the work is to determine the influence of sea water circulation and temperature on the formation of the spawning stock of anchovy in the Sea of Azov.Materials and methods: Fishery and biological data on the spawning stock of the Azov anchovy were collected in surveys and bioresources monitoring carried out by the Azov-Black Sea branch of the VNIRO («AzNIIRKH») in 2000–2021. Satellite sea level data were used in the analysis of sea water circulation. These data, as well as satellite information on sea surface temperature for March-June 2000–2021 for the north-eastern part of the Black Sea and the southern part of the Azov Sea were taken from the website of the Copernicus Marine Service information system. Graphical and statistical methods of analyzing multi-year changes in the data were used in the work. The novelty of the performed work lies in the study of oceanographic conditions of spring migration of anchovy to the Sea of Azov as factors determining the size of its spawning stock in this sea.Result: intensive growth of the spawning stock of anchovy in the Sea of Azov was observed in 2008, 2010 and 2011. This was facilitated by: predominance of north-western geostrophic currents in the north-eastern part of the Black Sea in April 2008–2011, weakening of the Azov currents in the Kerch Strait in April 2007–2010 and predominance of the Black Sea currents in May 2004–2012, Decrease in the meridional gradient of water temperature in the Kerch Strait in April 2007–2010.Practical significance: the possibility of using oceanographic information for April-May to predict the size of the spawning and commercial stock of anchovy in the Sea of Azov.

Predicting storm surge extremes on the Southeast Brazilian Coast: Long-term projections with neural networks

Extreme sea level events, resulting from the confluence of tides and storm surges, pose a significant threat to coastal populations and economies. The escalating risks associated with these events are exacerbated by climate change, manifesting in heightened storm intensity, increased frequency, and rising sea levels. Precise estimation of the probability of extreme storm surges is crucial for effective coastal management and adaptation. However, utilizing historical storm data is challenging due to data scarcity and the imperative to consider potential non-stationarity induced by climate change in predicting such events. This study addresses these challenges by introducing two neural network-based machine learning systems: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM). Leveraging local and remote atmospheric and oceanic conditions, these systems project storm surges until 2060, incorporating climate projections. Trained and evaluated using sea level data from Imbetiba Port in Macaé, Rio de Janeiro, Brazil, the models utilize dynamic regionalization data from the RegCM4 and WW3 models, forced by HadGEM2-ES and MPI climate models. Both neural network models exhibited similar performance patterns, demonstrating high agreement in predicting storm surge heights with a 100-year return value, based on Imbetiba Port data. Projections utilized peaks-over-threshold (POT) methods, and extremes were calculated using a generalized Pareto distribution (GPD). Long-term projections indicated a 28 % increase (MLP ANN) and a substantial 70 % increase (LSTM RNN) in estimating extreme values, surpassing the observed storm surge of 0.67 m. Projected mean values were 0.86 m for the MLP network and 1.15 m for the LSTM network, providing valuable insights into the potential amplification of extreme sea level events in the studied region.

Effects of community water fluoridation on child dental caries in remote Northern Territory, Australia: a difference-in-difference analysis.

Community water fluoridation (CWF) is a cost-effective intervention to reduce dental caries at population level. This Australian study used a difference-in-difference (DiD) analysis to measure dental caries in children exposed to CWF in the Northern Territory (NT), Australia. Oral health data obtained from the NT Department of Health contained 64 399 person-year observations from 2008 to 2020, totalling 24 546 children aged 1-17 years. Drinking water quality data for fluoride levels, held by the Power and Water Corporation, were obtained for 50 remote communities and linked to the oral health dataset. The DiD analysis used a treatment group and two control groups to compare the effects of CWF on dental caries outcomes in children, measured using the decayed, missing and filled teeth (dmft/DMFT) index. The treatment group consisted of records from children residing in five remote NT communities that implemented CWF in 2014.The control 1 group included records of children residing in communities with naturally occurring fluoride in drinking water supplies at levels at or above the Department of Health policy threshold of 0.5 mg/L. The control 2 group included records of children residing in communities with naturally occurring fluoride in drinking water supplies below the level recommended by the Department of Health policy (<0.5 mg/L). The data were grouped into time periods prior to the inception of CWF in five remote communities in 2014 (pre-intervention) and after 2014 (post-intervention). Our results demonstrated that dental caries was significantly decreased for children in the treatment group following the implementation of CWF at a greater magnitude than both control groups for the same time period. Overall, children assigned to the treatment group exhibited a decline in the number of teeth affected by caries by an average of 0.28 (p=0.001). Notably, children of ages 7-10 years and 11-17 years experienced significantly greater post-intervention declines in average dmft/DMFT, by 0.32 (p=0.051) and 0.40 (p=0.012) fewer affected teeth respectively. While dental caries disproportionately impacts Aboriginal children in remote and very remote NT, it is clear that CWF produces population-level reductions in overall dental caries for these populations. Additionally, our study demonstrates the application of the DiD method in a public health policy evaluation. Our findings suggest that the longstanding policy position of the NT Department of Health on CWF has supported improvements in oral health among child populations that experience high levels of dental caries in remote NT communities.

Impacts of the Mindset Teams Programme on Teacher Outcomes and Pupil Educational Attainment: Secondary Data Analysis

We evaluated the impact of a growth mindset intervention on pre-specified teacher outcomes and pupil academic attainment and attendance. Delivered over 12 months, the Mindset Teams intervention provides teachers with formal and self-directed online learning, and an opportunity to implement evidence-based growth mindset practice in the classroom setting. The intervention aims to influence teacher practices to improve pupil resilience for learning and bring about positive impacts on pupil attainment and health outcomes. Impacts on teachers were explored through pre- and post-intervention survey data (570 and 301 respectively). Pupil attainment and attendance data from 1220 schools, 72 of which received the intervention, across years 2017–2021 (minus 2020), were analyzed using weighted mixed effects models, using a Difference-in-differences framework. Bayes factors were calculated as a measure of strength of evidence. Results suggest positive impacts of the intervention on teacher outcomes and indicate a moderate effect (+3% (95%CI +1%, +6%)) of programme delivery on writing attainment, but no evidence of effect for other outcomes. Findings highlight important issues for further research, specifically the need to explore programme impacts using pupil or classroom level data.

Identification of factors associated with vancomycin-induced acute kidney injury: A retrospective analysis using the Common Data Model.

Previous findings on predictors of vancomycin-induced acute kidney injury (AKI) are inconsistent. We aimed to identify the predictors of vancomycin-induced AKI using the Observational Medical Outcome Partnership Common Data Model. We analyzed data from patients treated with vancomycin between January 1, 2012, and May 31, 2022, who were positive for Staphylococcus aureus and had undergone oxacillin susceptibility tests. After excluding patients without data for vancomycin or baseline serum creatinine levels, 116 patients were included in the final dataset. Data up to the third measured vancomycin concentration were collected for each patient. Logistic regression models were used to estimate the odds ratio and 95% confidence interval for each variable associated with vancomycin-induced AKI. High baseline serum creatinine levels, intensive care unit admission, and concurrent renal disorders were significantly associated with vancomycin-induced AKI. Although high trough levels or area under the curve values were not significantly associated with vancomycin-induced AKI, both were significantly higher in patients with AKI than in those without AKI at the second vancomycin concentration measurement. The proportion with trough levels >20mg/L was higher in patients with AKI than in those without AKI at the third measurement. Our findings revealed that underlying renal disease and intensive care unit admission are more significantly associated with vancomycin-induced AKI than vancomycin pharmacokinetic parameters or dosage, likely due to vancomycin concentration-based dosage adjustment in clinical settings. Our findings may help develop strategies for reducing the incidence of vancomycin-induced AKI; however, further prospective studies are essential.

A comparison of sea-level rise and storm-surge overwash effects on groundwater salinity of a barrier island

Coastal fresh groundwater is threatened by salinization due to both sea-level rise and storm-surge overwash. Most studies of subsurface saltwater intrusion focus on sea-level rise, but storms that are becoming more frequent and intense with climate change could have more imminent and permanent effects on aquifer salinity. Few studies directly compare saltwater intrusion due to sea-level rise with saltwater intrusion due to storm-surge overwash, and no studies address this issue on barrier islands. In this study, a hydrological model simulating coupled, variable-density, surface and subsurface flow and salt transport was developed and calibrated to water level and specific conductance data at Assateague Island, MD. The calibrated model was used to calculate the mass of salt and the total volume of aquifer salinized in 2080 due to both sea-level rise and more frequent storm surges. The results suggest that the total mass of salt that intrudes into the aquifer due to changes in the 2-year storm surge height is approximately equal to that of sea-level rise (∼300 kg), and the surges salinize nearly the entire aquifer, exceeding the volume salinized by sea-level rise (∼80 %). The influence of aquifer properties and unsaturated zone thickness was investigated by reducing aquifer hydraulic conductivity, resulting in greater salinization from storm-surge overwash (100 % of aquifer volume) than from sea-level rise (∼20 %). Higher storm surges are expected to overtop the dunes on Assateague by 2080 causing a 75 % decline in areas above the 2-year storm event while sea-level rise will only inundate ∼ 40 % of the land area on Assateague. This analysis suggests that groundwater is likely more vulnerable to storm-surge overwash than to sea-level rise induced salinization, even in systems where sea-level rise is expected to be most severe.

Using ensemble learning method and binary decision tree algorithm for drought intensity level classification

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

Outburst floods and their impact on Chinese Neolithic cultures during the 4.2 ka BP event: Evidence from Dayeze Lake in the lower reaches of the Yellow River

Abrupt climate fluctuations between 4.2 and 3.5 ka BP, and in particular the 4.2 ka BP event, exerted a profound impact on the global ancient civilizations. However, hydroclimatic conditions within the Asian Summer Monsoon (ASM) regions during this event remain uncertain. Here we report a multi-proxy analysis of a sediment core obtained from Dayeze Lake in the lower reaches of the Yellow River in monsoonal China, to elucidate the hydroclimatic variabilities over the past 4.2 ka. Core intervals (4.2–3.5, 1.1–0.6 cal ka BP) containing high levels of sand fraction, low-frequency magnetic susceptibility and zirconium, and low levels of organic matter and calcium oxide, reflect outburst floods from the Yellow River. This is supported by the widespread fluvial deposits and slackwater deposits preserved in archaeological sites and loess-paleosol profiles from the middle and lower reaches of the Yellow River. In contrast, various climate records, such as lake level, pollen, and tree ring data, from the marginal regions of the ASM reflect widespread droughts during the same time period. This finding highlights the significant spatial heterogeneity of hydroclimate within ASM regions associated with the 4.2 ka BP event, which may be related to migrations of the monsoonal rain belt and the West Pacific subtropical high, as well as frequent El Niño–Southern Oscillation events. Referring to archaeological data, we conclude that the decline and collapse of Chinese Neolithic cultures were related to a pattern of droughts in the marginal regions of the ASM and floods in the middle and lower reaches of the Yellow River.

Abstract C139: Cancer related excess death and potential life years lost among Black Americans

Abstract Introduction: Cancer represents the second leading cause of death in the United States and disproportionately affects Black Americans. In recent years, cancer-related mortality rates have declined for both Black and White Americans. However, mortality rates and rate ratios alone are insufficient for characterizing the burden of cancer disparities between racial groups. Among the most prevalent cancers in the US, we evaluated trends in excess mortality and years of potential life lost between Black and White Americans. Methods: This was a serial cross-sectional study using US national level data from CDC Wonder between the years 1999-2020. We included White and non-Hispanic Black Americans with a cause of death from breast, prostate, lung, colon, bladder, or uterine cancer. We measured age-adjusted excess death as well as excess years of potential life lost for each cancer site among Black and White Americans, stratified by sex when applicable. Annual mortality rates for each cancer site were sourced from the CDC Cancer Statistic Data Visualization tool. Results: The age adjusted mortality rate for all cancers declined between 1999-2020 for Black (257.2 to 166.8 per 100,000) and White Americans (200.9 to 149.3 per 100,000). Site specific cancer mortality rates declined in Black and White Americans for breast, prostate, lung, colon, and bladder cancer, but increased for uterine cancer. Among the most prevalent cancer sites, there were a total of 115,43 excess deaths with 2,093,327 excess years potential life lost for Black men and 59,134 excess deaths with 1,508,052 excess years potential life lost for Black women over a 22-year period. The trend of annual excess death and years potential life lost has slowly increased for Black men with colon cancer as well as Black women with breast and uterine cancer. Annual excess death and years potential life lost in Black men with prostate cancer decreased until 2013 after which it has slowly increased. Conclusion: Despite declining mortality rates in most cancers in the United States, Black Americans still experience a staggering number of excess deaths and years potential life lost due to cancer. Furthermore, the discordance between declining mortality rates and increasing excess death or years potential life lost in breast, prostate, colon, and uterine cancer highlight that racial disparities in cancer care and outcomes are likely worsening among Black Americans. Action is needed to support interventions and organizations committed to addressing cancer health inequities among Black Americans. Citation Format: Daniel S. Carson, Navya Gunaje, Sarah K. Holt, Nana Frimpong, Jenney R. Lee, Liz Sage, Erika M Wolff, Yaw A. Nyame. Cancer related excess death and potential life years lost among Black Americans [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C139.

Butterfly species vary in sex‐specific sodium accumulation from larval diets

Abstract Sodium is essential for animals, and its heterogeneous distribution can cause a range of phenomena, from sodium‐seeking behaviours to impacting their performance. Although sodium content in soils and plants is relatively well documented, data for higher trophic levels are limited. Knowledge of the variation in sodium in lower trophic levels could have implications for understanding the behaviour and physiology of species at higher levels. We investigated the variation in tissue sodium concentration between males and females of four butterfly species. Puddling behaviour of Lepidoptera suggests sodium needs of males are generally greater than females, thus, we predicted males would accumulate more sodium than females on a given diet. Larvae were reared on plants (for Battus philenor, Chlosyne lacinia and Danaus plexippus) and an artificial diet (for Pieris rapae) under Low Na (no added sodium) and High Na (sodium added) conditions. Among species and sexes, we quantified and compared adult absolute tissue sodium concentrations and bioconcentration factors, which indicate net sodium accumulation or excretion relative to individuals' diets. On average, individuals on low‐sodium diets had higher bioconcentration values across all species. Male butterflies accumulated significantly higher sodium concentrations than females in two sodium treatments for B. philenor, and P. rapae and only in the High Na treatment for C. lacinia. However, in D. plexippus, individuals accumulate sodium in the High Na treatment, but males and females responded in the same way. Our study revealed sex‐ and species‐specific patterns of butterfly sodium accumulation, which could be linked to variations in behaviour and/or performance. Differences in sodium content across species have implications for variation in predation and trophic‐level interactions, an interesting avenue for future ecological and evolutionary research.

Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2

BackgroundAs precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method.ResultsThe S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics.ConclusionsTaken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

Assessment of the HIV Enhanced Access Testing in the Emergency Department (HEATED) program in Nairobi, Kenya: a quasi-experimental prospective study

Background Persons seeking emergency injury care are often from higher-risk and underserved key populations (KPs) and priority populations (PPs) for HIV programming. While facility-based HIV Testing Services (HTS) in Kenya are effective, emergency department (ED) delivery is limited, despite the potential to reach underserved persons. Methods This quasi-experimental prospective study evaluated implementation of the HIV Enhanced Access Testing in Emergency Departments (HEATED) at Kenyatta National Hospital ED in Nairobi, Kenya. The HEATED program was designed as a multi-component intervention employing setting appropriate strategies for HIV care sensitization and integration, task shifting, resource reorganization, linkage advocacy, skills development and education to promote ED-HTS with a focus on higher-risk persons. KPs included sex workers, gay men, men who have sex with men, transgender persons and persons who inject drugs. PPs included young persons (18–24 years), victims of interpersonal violence, persons with hazardous alcohol use and persons never HIV tested. Data were obtained from systems-level records, enrolled injured patient participants and healthcare providers. Systems and patient-level data were collected during a pre-implementation period (6 March − 16 April 2023) and post-implementation (period 1, 1 May − 26 June 2023). Additional, systems-level data were collected during a second post-implementation (period 2, 27 June − 20 August 2023). HTS data were evaluated as facility-based HIV testing (completed in the ED) and distribution of HIV self-tests independently, and aggregated as ED-HTS. Evaluation analyses were completed across reach, effectiveness, adoption, implementation and maintenance framework domains. Results All 151 clinical staff were reached through trainings and sensitizations on the HEATED program. Systems-level ED-HTS among all presenting patients increased from 16.7% pre-implementation to 23.0% post-implementation periods 1 and 2 (RR = 1.31, 95% CI: 1.21–1.43; p < 0.001). Among 605 enrolled patient participants, facilities-based HTS increased from 5.7% pre-implementation to 62.3% post-implementation period 1 (RR = 11.2, 95%CI: 6.9–18.1; p < 0.001). There were 440 (72.7%) patient participants identified as KPs (5.6%) and/or PPs (65.3%). For enrolled KPs/PPs, facilities-based HTS increased from 4.6% pre-implementation to 72.3% post-implementation period 1 (RR = 13.8, 95%CI: 5.5–28.7, p < 0.001). Systems and participant level data demonstrated successful adoption and implementation of the HEATED program. Through 16 wk post-implementation a significant increase in ED-HTS delivery was maintained as compared to pre-implementation. Conclusions The HEATED program increased overall ED-HTS and augmented delivery to KPs/PPs, suggesting that broader implementation could improve HIV services for underserved persons already in contact with health systems.

Hydrological model calibration in data-deficient basins using satellite altimetry and a hydrodynamic model

Large basins with insufficient hydrological station distribution and lacking sufficient meteorological data, such as water level and discharge, pose significant challenges in developing reliable hydrological models. These models are crucial for addressing water resource-related challenges such as climate change (e.g., floods and droughts) and human-induced activities (e.g., dams) impacts. Recent advancements in satellite altimetry offer an efficient alternative by providing water level data; however, integrating this data into hydrological models remains an ongoing challenge. Here we demonstrate that a properly developed hydrodynamic model can effectively translate the satellite-observed water levels at various river cross-sections (hereafter referred to as virtual stations (VSs)) into the required discharge data, facilitating reliable hydrological modeling for data-deficient basins. Our results, based on the assessment of water level data derived from a hydrodynamic model and satellite altimetry, showed high consistency (NSE > 0.96) in the Lancang-Mekong River (LMR) basin as a case study. We found that the distribution of stations along the mainstream, in terms of their number and distance from each other, can significantly impact the performance of hydrological modeling processes. The results revealed that when a large stretch of the river is not monitored by a station, a large uncertainty occurs in runoff generation, at least for that segment of the river. However, the inclusion of virtual stations in the modeling process improved the overall performance, indicating the advantages of satellite altimetry data in better modeling of data-deficient basins. Our findings revealed that virtual stations can be effectively utilized in physically based and spatially distributed hydrological models to develop a sub-basin modeling approach that addresses the regional contribution of sub-basins to downstream river flow. The integrated modeling framework holds potential for hydrological analysis and water resources management for basins lacking sufficient gauging data and provides insight into how the distribution of stations can influence the calibration of hydrological models.

Development of a General Composite Scale (GENCOMS) for Progressive Neurodegenerative Diseases and Implications for the Assessment of Disease-Modifying Therapies.

The reliable assessment of treatment outcomes for disease-modifying therapies (DMT) in neurodegenerative disease is challenging. The objective of this paper is to describe a generalized framework for developing composite scales that can be applied in diverse, degenerative conditions, termed "GENCOMS." Composite scales optimize the sensitivity for detecting clinically meaningful effects that slow disease progression. The GENCOMS method relies on robust natural history data and/or placebo arm data from DMT trials. Validated scales that are core to the disease process have been identified, and item level data obtained to standardize the response outcomes from 0 (best possible score) to 1 (worst possible score). A partial least squares regression analysis was conducted with temporal change as the dependent variable and change scores in standardized items as the explanatory variables. The derived model coefficients constitute a weighted sum of items that most effectively measure disease progression. The resultant composite scale was optimized to detect disease progression and can be examined in a range of slow or fast progressing populations. The scale can be used in studies with comparable patient populations as an endpoint optimized to measure disease progression and therefore ideally suited to assess treatment effects in DMTs. The methodology presented here provides a generalizable framework for developing composite scales in the assessment of neurodegenerative disease progression and evaluation of DMT effects. By objectively selecting and weighting items from previously validated measures based solely on their sensitivity to disease progression, this methodology allows for the creation of a more responsive measurement of clinical decline. This heightened sensitivity to clinical decline can be utilized to detect modest yet meaningful treatment effects in the early stages of neurogenerative diseases, when it is optimal to begin a DMT.

Algorithmic trading and mini flash crashes: Evidence from Austria

We use stock-day level data on the market share of algorithmic trading to analyze whether algorithmic trading affects the frequency of mini flash crashes in the Austrian stock market. We use an instrumental variables approach and the Petrin and Train (2010) control function approach to address endogeneity concerns. We find no evidence that algorithmic trading significantly affects the probability of the occurrence of mini flash crashes.

Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure

BackgroundUltrasound can detect fluid in the alveolar and interstitial spaces of the lung using the presence of artifacts known as B-lines. The aim of this study was to determine whether a deep learning algorithm generated B-line severity score correlated with pulmonary congestion and disease severity based on clinical assessment (as identified by composite congestion score and Rothman index) and to evaluate changes in the score with treatment. Patients suspected of congestive heart failure underwent daily ultrasonography. Eight lung zones (right and left anterior/lateral and superior/inferior) were scanned using a tablet ultrasound system with a phased-array probe. Mixed effects modeling explored the association between average B-line score and the composite congestion score, and average B-line score and Rothman index, respectively. Covariates tested included patient and exam level data (sex, age, presence of selected comorbidities, baseline sodium and hemoglobin, creatinine, vital signs, oxygen delivery amount and delivery method, diuretic dose).ResultsAnalysis included 110 unique subjects (3379 clips). B-line severity score was significantly associated with the composite congestion score, with a coefficient of 0.7 (95% CI 0.1–1.2 p = 0.02), but was not significantly associated with the Rothman index.ConclusionsUse of this technology may allow clinicians with limited ultrasound experience to determine an objective measure of B-line burden.

Waiting Times for Sea Level Variations in the Port of Trieste: A Computational Data-Driven Study

We report here a series of detailed statistical analyses on the sea level variations in the Port of Trieste using one of the largest existing data catalogues that covers more than a century of measurements. We show that the distribution of waiting times, which are defined here akin to econophysics, namely the series of shortest time spans between a given sea level L and the next sea level of at least L + δ in the catalogue, exhibits a distinct scale-free character for small values of δ. For large values of δ, the shape of the distribution depends largely on how one treats the periodic components embedded in the sea level dataset. We show that direct analyses of the raw dataset yield distributions similar to the exponential distribution, while pre-processing the sea level data by means of a local averaging numerical recipe leads to Pareto-Tsallis distributions.

The causal and mediation effect of chronic obstructive pulmonary disease on lung cancer subtypes: a two-sample mendelian randomization study.

This study aims to determine the causal effect of chronic obstructive pulmonary disease (COPD) on different subtypes of lung cancer and to investigate the mediation effects of COPD between smoking and the subtypes of lung cancer. The study utilized summary level data from genome-wide association studies. It extracted independent single nucleotide polymorphisms (SNP) to serve as instrumental variables (IV). We conducted two-sample MR analyses primarily using inverse-variance weighting, as well as MR-Egger and MR-PRESSO to establish and validate the causal impact of COPD on lung cancer subtypes. Additionally, multivariable MR analysis was employed to ascertain the mediating role of COPD between smoking and lung cancers. The two-sample MR analysis demonstrated that COPD is linked to an elevated risk of lung adenocarcinoma (OR: 1.48, 95% CI 1.35-1.61, p = 0.009) and squamous cell carcinoma (OR: 1.78, 95% CI 1.62-1.93, p = 0.001). Further, using multivariable MR, it was established that COPD mediates the causal effects of smoking on lung adenocarcinoma by 56.52% (95% CI 17.51-95.52%) and 63.61% (95% CI 38.31-88.92%) in lung squamous cell carcinoma. Our study found that COPD was a risk factor for developing both lung adenocarcinoma and squamous cell carcinoma. COPD also played a crucial role in mediating the causal effects of smoking on these two subtypes of lung cancer.

PSIV-16 The role of veterinary services on nutrition schemes, management, and health practices for sheep and goats in rural Ngaka Modiri Molema District Municipality, North West Province of South Africa

Abstract Poverty is a feature of rural households, and as a result rural people usually adopt low-input agriculture to alleviate poverty. Many households in Ngaka Modiri Molema District keep animals, particularly sheep and goats, for a variety of reasons, including as source of income, milk, and meat. However, they lack the management skills that are necessary for their livestock to increase their productivity. Inadequate animal management, including poor feeding and housing standards and poor application of proper animal health procedures are compounded by a lack of agricultural experience and low-input farming methods. In addition, poor animal nutrition, sickness and a high prevalence of parasites, and low animal production output further contribute to low overall productivity. Therefore, support offered by the veterinary services, and the impact these interventions have on sheep and goat farming among rural farmers need to be investigated. The present study will seek to describe the feeding schemes and management practices adopted by sheep and goat owners, as well as the role of veterinary and extension services in the development of such practices. Health care provided to sheep and goat farmers by the veterinary services will also be determined. A survey will be conducted in Ngaka Modiri Molema District Municipality, a rural area in North West Province of South Africa. The study population will consist of all farmers, both female and male, rearing sheep and goats. Animal and farm information level data and general farm management practices will be collected via a questionnaire survey. Using the Statistical Kit for STATA (version 16), data will be examined by computing frequency, percentages, and proportions based on the characteristics of the sample population. This survey will help to provide opportunities for resource-poor farmers in rural areas to develop their flocks.

Based on PVM image gray level data to analysis the effect of N-vinyl caprolactam on hydrate growth

The usage of hydrate inhibitors is an effective method to solve the problem of flow assurance in deep-water pipelines. However, the mechanism of hydrate formation and transformation in inhibitor-containing systems is not well understood, and the research methodology needs to be innovated. In this study, a possible mechanism of wearing away during hydrate particle transformation was proposed for the first time, based on the hydrate growth process in the system containing the kinetic inhibitor N-vinyl caprolactam monomer. A method to analysed the hydrate growth and transformation dynamics through the change of gray level in the image was proposed. And the hydrate growth process was divided into three different stages based on the trend analysis of the gray level data of the images,trends in the dynamics of hydrate particle growth within the bulk phase corresponding to changes in gray level were verified. On this basis, the effects of stirring rate, subcooling and the monomer concentration of the NVCap on the growth of hydrates were discussed, and it was suggested that there might be differences in the mechanisms exhibited by the inhibitor at different growth stages of hydrates. In this study, the process of analysing the internal growth dynamics of hydrate systems through image gray levels was reported for the first time, and its trend changes corresponding to hydrate growth dynamics were verified, which proved the feasibility of this method, and this study provides an innovative research methodology and idea for the study of the dynamics of hydrate generation and transformation.