Influence of seed varieties and harvesting regimes on growth indices, yields and nutritional values of hydroponics maize fodder

This study was conducted to assess the influence of seed varieties and harvesting regimes on growth indices, yields and nutritional values of hydroponics maize fodder in other to ensure sustainable fodder for livestock production. The experiment was 2 x 3 factorial scheme fitted into a completely randomized design (CRD), comprising of two (2) varieties of maize seeds (OBA 98 and Local white maize) and three (3) harvesting regimes (6th, 8th and 10th day). Growth indices, yields, nutritional values were assessed. Results shows a significant (P<0.05) effects of maize seed varieties and harvesting regimes on the growth indices, yields, nutritional values. The OBA 98 maize hydroponic fodder (OHF) had the highest (P<0.05) agronomic indices, yields, nutrients (CP (16.36 %), EE (4.41), CF (7.23), ash (7.13) and NFE (64.88)) than Local maize hydroponic fodder (LHF). The highest significant (P<0.05) contents of the nutrients was observed at 10th day harvesting, while least (P<0.05) was obtained at 6th day harvesting except NFE. The OHF had higher (P<0.05) neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL) and hemicellulose (HEM). The cellulose (CEL) were similar (P>0.05) in OHF and LHF. Similar (P>0.05) ADL, HEM and CEL were recorded across the harvesting regimes. The OHF and 10th day harvesting regime had highest (P<0.05) mineral, tannin, phytate and oxalate contents. Conclusively, OHF had superior growth indices, yields and nutritional values, 10th day harvesting was better than 6th and 8th day. Hence, OBA 98 seed variety and 10th day harvesting regime is recommended for better hydroponics maize fodder production

Assessment of Resident Doctors’ Perception of Postgraduate Medical Education in Nigeria Using the SPEED Tool: A Pilot Study

Background: Obtaining feedback from trainees is important in the evaluation and evolution of Postgraduate Medical Education (PME), and policies made based on their felt needs would go a long way in making residency training a worthwhile experience. This pilot study aimed to assess resident doctors’ perception of the training content, atmosphere, and organization using the Scan of Postgraduate Educational Environment Domains (SPEED) tool. Methodology: This was a cross-sectional study conducted amongst resident doctors at Babcock University Teaching Hospital (BUTH) in Nigeria, between May and August 2019. A self-administered questionnaire was used to collect participants’ sociodemographic data, their perception of PME in their respective departments, as well as the strengths and weaknesses of the training programmes. Validity and reliability indices were assessed, and descriptive, inferential, and correlational analyses were run where appropriate. Results: The mean score for the resident doctors’ perception of training content, atmosphere, and organization was 4.0 ± 0.4, 4.2 ± 0.5 and 3.69 ± 0.60 respectively, out of a maximum of 5, indicating a positive perception of training in BUTH. The major strengths perceived by most residents were good inter-personal relations between residents and their trainers, as well as conducive learning and work environment; while the weaknesses include poor remuneration and limited staffing which hampers rotations. Conclusion: Resident doctors in BUTH mostly had a positive outlook on their training. This study serves as a reference point for local policy change (in BUTH), and a framework from which future studies on PME can emerge

Dark blood ischemic LGE segmentation using a deep learning approach

The extent of ischemic scar detected by Cardiac Magnetic Resonance (CMR) with late gadolinium enhancement (LGE) is linked with long-term prognosis, but scar quantification is time-consuming. Deep Learning (DL) approaches appear promising in CMR segmentation. Purpose: To train and apply a deep learning approach to dark blood (DB) CMR-LGE for ischemic scar segmentation, comparing results to 4-Standard Deviation (4-SD) semi-automated method. Methods: We trained and validated a dual neural network infrastructure on a dataset of DB-LGE short-axis stacks, acquired at 1.5T from 33 patients with ischemic scar. The DL architectures were an evolution of the U-Net Convolutional Neural Network (CNN), using data augmentation to increase generalization. The CNNs worked together to identify and segment 1) the myocardium and 2) areas of LGE. The first CNN simultaneously cropped the region of interest (RoI) according to the bounding box of the heart and calculated the area of myocardium. The cropped RoI was then processed by the second CNN, which identified the overall LGE area. The extent of scar was calculated as the ratio of the two areas. For comparison, endo- and epi-cardial borders were manually contoured and scars segmented by a 4-SD technique with a validated software. Results: The two U-Net networks were implemented with two free and open-source software library for machine learning. We performed 5-fold cross-validation over a dataset of 108 and 385 labelled CMR images of the myocardium and scar, respectively. We obtained high performance (> ∼0.85) as measured by the Intersection over Union metric (IoU) on the training sets, in the case of scar segmentation. With regards to heart recognition, the performance was lower (> ∼0.7), although improved (∼ 0.75) by detecting the cardiac area instead of heart boundaries. On the validation set, performances oscillated between 0.8 and 0.85 for scar tissue recognition, and dropped to ∼0.7 for myocardium segmentation. We believe that underrepresented samples and noise might be affecting the overall performances, so that additional data might be beneficial. Figure1: examples of heart segmentation (upper left panel: training; upper right panel: validation) and of scar segmentation (lower left panel: training; lower right panel: validation). Conclusion: Our CNNs show promising results in automatically segmenting LV and quantify ischemic scars on DB-LGE-CMR images. The performances of our method can further improve by expanding the data set used for the training. If implemented in a clinical routine, this process can speed up the CMR analysis process and aid in the clinical decision-making

Variabilidade e tendencias interanuais no fitoplancto mariño das costas de Galicia

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Capítulo 7. Biomasa y producción primaria.

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Uric Acid Induces Renal Inflammation via Activating Tubular NF-κB Signaling Pathway

Inflammation is a pathologic feature of hyperuricemia in clinical settings. However, the underlying mechanism remains unknown. Here, infiltration of T cells and macrophages were significantly increased in hyperuricemia mice kidneys. This infiltration of inflammatory cells was accompanied by an up-regulation of TNF-α, MCP-1 and RANTES expression. Further, infiltration was largely located in tubular interstitial spaces, suggesting a role for tubular cells in hyperuricemia-induced inflammation. In cultured tubular epithelial cells (NRK-52E), uric acid, probably transported via urate transporter, induced TNF-α, MCP-1 and RANTES mRNA as well as RANTES protein expression. Culture media of NRK-52E cells incubated with uric acid showed a chemo-attractive ability to recruit macrophage. Moreover uric acid activated NF-κB signaling. The uric acid-induced up-regulation of RANTES was blocked by SN 50, a specific NF-κB inhibitor. Activation of NF-κB signaling was also observed in tubule of hyperuricemia mice. These results suggest that uric acid induces renal inflammation via activation of NF-κB signaling