3,274 research outputs found

    Seed yield and oil content of some sunflower (Helianthus annuus L.) hybrids irrigated at different growth stages

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    This research was carried out to determine the effects of irrigation applied at different growth stages on yield, yield components and oil content of sunflower during 2002 and 2003. Sunflower cultivars Sanbro, Tarsan-1018 and Ozdemirbey were used as materials in the experiment which was designed in a split plot of randomized complete blocks with three replications. Seven irrigation schedules; I0 = nonirrigated (control), I1 = irrigation at vegetative stage, I2 = irrigation at heading stage, I3 = irrigation at flowering stage, I4 = I1 + I3 (two irrigations) I5 = I1 + I2 + I3 (three irrigations) and I6 = I1 + I2 + I3 + irrigation at milking stage were applied. According to the results of the research, plant height and head diameter by cultivars and irrigations ranged between 106 to 183 cm and 12.5 to 19.3 cm, respectively. Irrigations at all growth stages increased seed yield by 43.1% in 2002 and 77.2% in 2003. The results revealed that three irrigations should be scheduled at vegetative, bud formation and flowering stages. Under severe conditions of water scarcity, it would be better if irrigation is applied at flowering stage.Key words: Sunflower, irrigation, yield, oil ratio

    Improving Utilization of SGLT2 Inhibitors in the Inpatient Setting

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    SGLT2 inhibitors have been shown to have a significant benefit for patients with DM2 or CAD (DAPA-HF, Emperor-reduced) The usage of these medications are low compared to other Goal Directed Medical Therapy. There are multiple contributing factors as to why these medications are underutilized Our aim is to assess barriers against prescription of SGLT2-i at the time of discharge from TJUH and to increase utilization after placement on formulary

    Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

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    Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models based on Convolutional Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. Most of the current work address this problem as a binary classification task. However, including the grade estimation and quantification of predictions uncertainty can potentially increase the robustness of the model. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representational power of deep learning, with the ability to generalize from small datasets of Gaussian process models. The results show that uncertainty quantification in the predictions improves the interpretability of the method as a diagnostic support tool. The source code to replicate the experiments is publicly available at https://github.com/stoledoc/DLGP-DR-Diagnosis

    Monkeypox Outbreak: Wastewater and Environmental Surveillance Perspective

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    Monkeypox disease (MPXD), a viral disease caused by monkeypox virus (MPXV), is an emerging zoonotic disease endemic in some countries of Central and Western Africa but seldom reported outside the affected region. Since May 2022, MPXD has been reported at least in 74 countries globally, prompting the World Health Organization to declare the MPXD outbreak a Public Health Emergency of International Concern. As of July 24, 2022, 92% (68/74) of the countries with reported MPXD cases had no historical MPXD case reports. From the One Health perspective, the spread of MPXV in the environment poses a risk not only to humans but also to small mammals and may, ultimately, spread to potent novel host populations. Wastewater-based surveillance (WBS), has been extensively utilized for monitoring communicable diseases, particularly during the ongoing coronavirus disease, the COVID-19 pandemic It helped to monitor infectious disease caseloads as well as specific viral variants circulating in communities. The detection of MPXV DNA in various body fluids, including respiratory and nasal secretions, saliva, urine, feces, and semen of infected individuals, supports the possibility of using WBS as an early proxy for the detection of MPXV infections. WBS of MPXV DNA can be used to monitor MPXV activity/trends in sewerage network areas even before detecting laboratory-confirmed clinical cases within a community. However, several factors affect the detection of MPXV in wastewater including, but not limited to, routes and duration time of virus shedding by infected individuals, infection rates in the relevant affected population, environmental persistence, the processes and analytical sensitivity of the used methods. Further research is needed to identify the key factors that impact the detection of MPXV biomarkers in wastewater and improve the utility of WBS of MPXV as an early warning and monitoring tool for safeguarding human health. In this review, we shortly summarize aspects of MPXV outbreak relevant to wastewater monitoring and discuss the challenges associated with WBS.Peer reviewe

    Effects of testicular microlithiasis on Doppler parameters: report of three cases

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    BACKGROUND: Testicular microlithiasis is a rare, usually asymptomatic, non-progressive disease of the testes associated with various genetic anomalies, infertility and testicular tumors. According to our literature search, there is no specific data about Doppler findings in this disease. CASE PRESENTATION: Doppler findings of three cases of testicular microlithiasis during last two years in our institution are presented. CONCLUSIONS: Although our hypothesis was to find increased Doppler parameters due to intratesticular arterial compression, our findings suggest that there are no Doppler findings specific to testicular microlithiasis

    Further investigation of confirmed urinary tract infection (UTI) in children under five years: a systematic review.

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    Background: Further investigation of confirmed UTI in children aims to prevent renal scarring and future complications. Methods: We conducted a systematic review to determine the most effective approach to the further investigation of confirmed urinary tract infection (UTI) in children under five years of age. Results: 73 studies were included. Many studies had methodological limitations or were poorly reported. Effectiveness of further investigations: One study found that routine imaging did not lead to a reduction in recurrent UTIs or renal scarring. Diagnostic accuracy: The studies do not support the use of less invasive tests such as ultrasound as an alternative to renal scintigraphy, either to rule out infection of the upper urinary tract (LR- = 0.57, 95%CI: 0.47, 0.68) and thus to exclude patients from further investigation or to detect renal scarring (LR+ = 3.5, 95% CI: 2.5, 4.8). None of the tests investigated can accurately predict the development of renal scarring. The available evidence supports the consideration of contrast-enhanced ultrasound techniques for detecting vesico-ureteric reflux (VUR), as an alternative to micturating cystourethrography (MCUG) (LR+ = 14.1, 95% CI: 9.5, 20.8; LR- = 0.20, 95%CI: 0.13, 0.29); these techniques have the advantage of not requiring exposure to ionising radiation. Conclusion: There is no evidence to support the clinical effectiveness of routine investigation of children with confirmed UTI. Primary research on the effectiveness, in terms of improved patient outcome, of testing at all stages in the investigation of confirmed urinary tract infection is urgently required

    Student performance assessment using clustering techniques

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    The application of informatics in the university system management allows managers to count with a great amount of data which, rationally treated, can offer significant help for the student programming monitoring. This research proposes the use of clustering techniques as a useful tool of management strategy to evaluate the progression of the students’ behavior by dividing the population into homogeneous groups according to their characteristics and skills. These applications can help both the teacher and the student to improve the quality of education. The selected method is the data grouping analysis by means of fuzzy logic using the Fuzzy C-means algorithm to achieve a standard indicator called Grade, through an expert system to enable segmentation.Universidad de la Costa, 2 Universidad Nacional Experimental Politécnica “Antonio José de Sucre”, Universidad Simón Bolívar, Corporación Universitaria Latinoamericana, Corporación Universitaria Minuto de Dios
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