Singh Joyner (irontaiwan3)

Ebola Virus Disease (EVD) outbreaks have a significant impact on the health and wellbeing, and livelihoods of communities. EVD response interventions particularly affect the food value chain, and income security of pig farmers in agro-pastoral communities. Despite the enormous effort of EVD response interventions, there is paucity of information towards EVD among those involved in the pig value chain, as well as the effect of EVD outbreaks on the pig value chain. This study therefore, assessed the knowledge, perceptions on the occurrence of Ebola and its effects on the pig value chain in the agro-pastoral district of Luweero, Central Uganda. A cross sectional study was conducted in two parishes of Ssambwe and Ngalonkulu, Luwero district. A total of 229 respondents were included in the study. selleckchem Structured questionnaires, key informant interviews and focus group discussions were conducted to collect data. Quantitative data was analysed using SPSS version 22 while qualitative data was analysed using thematic chain on EVD in order to minimize the negative economic impacts associated with EVD outbreaks. This study showed that EVD outbreak negatively affected the pig value chain i.e., the demand and supply of pigs and pork. Therefore, there is need to sensitize the stakeholders in the pig value chain on EVD in order to minimize the negative economic impacts associated with EVD outbreaks. Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation. The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation. With an increase in the global popularity of coffee, caffeine is one of the most consumed ingredients of modern times. However, the consumption of massive amounts of caffeine can lead to severe hypokalemia. A 29-year-old man without a specific past medical history was admitted to our hospital with recurrent episodes of sudden and severe lower-extremity weakness. Laboratory tests revealed low serum potassium concentration (2.6-2.9 mmol/L) and low urine osmolality (100-130 mOsm/kgH O) in three such prior episodes. Urinary potassium/urinary creatinine ratio was 12 and 16 mmol/gCr, respectively. The patient was not under medication with laxatives, diur