Search

Pesticides are commonly applied in agriculture to manage pests, parasites, weeds, and other crop diseases to maximise product output value and minimise or reduce yield losses. Pesticides are widely and indiscriminately used in crop production in most countries. Carbofuran is a highly toxic insecticide commonly used to protect crops in agricultural areas. Exposure to carbofuran can cause harmful effects on both the ecological environment and human health, particularly on non-target species such as birds and aquatic organisms. Carbofuran continues to be used, although it has been banned in some countries. This review paper highlights carbofuran usage, its residue, toxicity, and mechanisms of degradation pathways in water, soil, and food, especially in agriculture. It has been shown that hydrolysis, photolysis, and microbial degradation are the main pathways for carbofuran degradation. Carbofuran phenol is significantly less toxic than all photo-metabolites, while 3-ketocarbofuran and 3-hydroxycarbofuran are practically as harmful as the parent compound. Hence, more study is needed on degradation pathways that may thoroughly minimise the toxicity of this deadly pesticide. [ABSTRACT FROM AUTHOR]

BACKGROUND: In response to the COVID-19 pandemic, new evidence-based strategies have emerged for reducing transmission of respiratory infections through management of indoor air. OBJECTIVES: This paper reviews critical advances that could reduce the burden of disease from inhaled pathogens and describes challenges in their implementation. DISCUSSION: Proven strategies include assuring sufficient ventilation, air cleaning by filtration, and air disinfection by germicidal ultraviolet (UV) light. Layered intervention strategies are needed to maximize risk reduction. Case studies demonstrate how to implement these tools while also revealing barriers to implementation. Future needs include standards designed with infection resilience and equity in mind, buildings optimized for infection resilience among other drivers, new approaches and technologies to improve ventilation, scientific consensus on the amount of ventilation needed to achieve a desired level of risk, methods for evaluating new air-cleaning technologies, studies of their long-term health effects, workforce training on ventilation systems, easier access to federal funds, demonstration projects in schools, and communication with the public about the importance of indoor air quality and actions people can take to improve it. https://doi.org/10.1289/EHP13878, Introduction The status of the Coronavirus disease (COVID-19) pandemic as a public health emergency ended in May 2023, more than 3 y after the first cases were reported in Wuhan, [...]

The study looks at the impact that the three dimensions of financial inclusion (FI) (i.e., access, usage, quality) may have on poverty alleviation. In doing so, the study relies on demand and supply-side data to measure Nigeria's FI. The demand-side data were derived from the 2021 Global Findex data, and the supply side data were sourced from the IMF Access survey database (2004–2021). The supply-side data were analysed using the ordinary least squares regression (OLS), while the demand-side data were analysed using the probit regression model. The study outcomes revealed a negative and significant relationship between financial access and poverty rate, further indicating that those who use financial services are less likely to experience poverty. The study recommends that financial service providers tailor their financial products to align with the educational level of the target population to encourage savings. [ABSTRACT FROM AUTHOR]

In modern society, children actively engage with social media platforms, and Ghana is not an exception to this trend. People have, however, placed an excessive amount of focus on the negative implications of using social media, which has overshadowed the positive effects that it has on children. This study examines the use of social media by children, including their preferred platforms, as well as the positive and negative consequences associated with their usage. This was a cross-sectional study that employed convenient and snowball sampling procedures for data collection and analysis. The study was conducted on 400 children aged 8 to 17 in Tema. The Uses and Gratifications Theory (UGT) is the study's underlying theory. The association between demographic characteristics and correlates of social media usage was examined using bivariate analysis, whereas the qualitative data analysis was guided by thematic analysis. The results show that 87.5% of children have ever used social media, with males being more likely than females to have done so (91.7% vs. 83.5%). The main reason social media platforms are used by children (87%) is for entertainment. TikTok has the highest usage percentage, making up 88.7% of all platforms, according to the findings. Some benefits of social media include communication, entertainment, and resources for creativity, research, and academic work. Also, the detrimental consequences of social media usage comprise exposure to sexually explicit material, addiction in children, and diminished academic achievement in children who become too engrossed in its usage. The study demonstrated that social media is widely used among children in Ghana. TikTok is the most popular social media platform among children, and it is their preferred platform. Parents are urged to encourage their children to use social media platforms for beneficial purposes, and media industry stakeholders should develop and implement initiatives to increase the benefits of social media for children. Regulation and supervision are necessary to mitigate the adverse consequences of children's social media usage. [ABSTRACT FROM AUTHOR]

The delivery of therapeutical molecules through the skin, particularly to its deeper layers, is impaired due to the stratum corneum layer, which acts as a barrier to foreign substances. Thus, for the past years, scientists have focused on the development of more efficient methods to deliver molecules to skin distinct layers. Microneedles, as a new class of biomedical devices, consist of an array of microscale needles. This particular biomedical device has been drawing attention due to its ability to breach the stratum corneum, forming micro-conduits to facilitate the passage of therapeutical molecules. The microneedle device has several advantages over conventional methods, such as better medication adherence, easiness, and painless self-administration. Moreover, it is possible to deliver the molecules swiftly or over time. Microneedles can vary in shape, size, and composition. The design process of a microneedle device must take into account several factors, like the location delivery, the material, and the manufacturing process. Microneedles have been used in a large number of fields from drug and vaccine application to cosmetics, therapy, diagnoses, tissue engineering, sample extraction, cancer research, and wound healing, among others. [ABSTRACT FROM AUTHOR]

Background: Stress urinary incontinence (SUI), though a prevalent condition among women, is undertreated in primary care. EHealth with pelvic floor muscle training is an evidence-based alternative to care-as-usual. It is unknown, however, how eHealth usage is related to treatment outcome, and this knowledge is required for general practitioners to implement eHealth in their practice. This study examines the relation between usage of eHealth for SUI and treatment outcomes by examining log data. Baseline factors were also explored for associations with treatment success. Method: In this pre-post study, women with SUI participated in "Baasoverjeblaas.nl", a web-based intervention translated from the Swedish internet intervention "Tät®-treatment of stress urinary incontinence". Usage was based on log data and divided into three user groups (low, intermediate and high). Online questionnaires were sent before, after treatment and at six-months follow-up. The relation between usage and the primary outcome − treatment success (PGI-) − was studied with a binomial logistic regression analysis. Changes in the secondary outcomes − symptom severity (ICIQ-UI SF) and quality of life (ICIQ-LUTSqol) − were studied per user group with linear mixed model analysis. Results: Included were 515 users with a mean age of 50.5 years (12.0 SD). The majority were low users (n = 295, 57.3%). Treatment success (PGI-I) was reached by one in four women and was more likely in high and intermediate users than in low users (OR 13.2, 95% CI 6.1–28.5, p < 0.001 and OR 2.92, 95% CI 1.35–6.34, p = 0.007, respectively). Symptom severity decreased and quality of life improved significantly over time, especially among high users. The women's expected ability to train their pelvic floor muscles and the frequency of pelvic floor muscle exercises at baseline were associated with treatment success. Conclusion: This study shows that usage of eHealth for SUI is related to all treatment outcomes. High users are more likely to have treatment success. Treatment success is more likely in women with higher expectations and pelvic floor muscle training at baseline. These findings indicate that general practitioners can select patients that would be more likely to benefit from eHealth treatment, and they can enhance treatment effect by stimulating eHealth usage. Trial registration: Landelijk Trial Register NL6570; https://onderzoekmetmensen.nl/nl/trial/25463. [ABSTRACT FROM AUTHOR]

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game changer for the incremental progress in traditional numerical weather prediction (NWP) known as the "quiet revolution" of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made by increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allows forecasts that require much lower computational costs and that are highly competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable accuracy for both global metrics and extreme events, when verified against both the operational IFS analysis and synoptic observations. Overly smooth forecasts, increasing bias with forecast lead time, and poor performance in predicting tropical cyclone intensity are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training. SIGNIFICANCE STATEMENT: We compare the traditional approach of generating weather forecasts with a new approach based on machine learning (ML). The traditional approach is based on resolving physical equations with numerical methods and can be quite expensive to run on supercomputers. ML models are trained on a very large dataset that combines observations and physically based models to derive our best estimate of the state of the atmosphere hour after hour, over the past decades. In this work, we assess the forecast performance with statistical tools and a focus on extreme events. Our results suggest that ML models, which are much less expensive to run than standard methods, could have a promising future in numerical weather prediction. Our study also points out weaknesses and limitations that would require further investigations and future ML model improvements. KEYWORDS: Forecast verification/skill; Operational forecasting; Machine learning, 1. Introduction Numerical weather prediction (NWP) is the dominant approach for weather forecasting. A weather forecast is the result of the numerical integration of partial differential equations starting from the [...]

CMIP6 simulations showed a weak human contribution to 2022-like Central Andes spring droughts, due to compensating impacts between anthropogenic greenhouse gases and aerosols. The Central Andes is a semiarid mountainous [...]