Economy of Bangladesh mostly depended on agriculture. As a small country, our population is over the limit. We are also a developing country also. For national GDP growth must be to increase the production. Every year a huge amount of agricultural production losses for the crop disease. As we know most of the farmers in our country are illiterate, have no proper knowledge about the disease, that's why they cannot manually detect the disease. I think if you properly detect the disease at the early stage we can solve the issue. We develop a model that can classify leaf disease. We focus on 5 major production crops in Bangladesh. Using computer vision technique our farmer will get the benefit. We use convolutional neural networks for classifying images. An algorithm cannot properly capture the features of existing data that's why we use a pre-trained feature extraction method that is MobileNetv2. MobileNetv2 is very useful for mobile devices. The research contains the proportions of validation accuracy of 90.38%. This approach resulted in the agriculture sector that will help a farmer to classify disease from harvest. The main goal of our model is to minimize the damage of suffering plants that can help to the growth of production. By solving this issue themselves farmers can also reduce cost. Our goal is that they can cure their crop at the right time. To achieve this goal we tend to think that we should develop a way to detect the leaf disease. We collect several kinds of cucumber leaves. And then any leave can be tasted by our model. By using our model we try to reduce the leaf disease.

Background: Diabetes is the most common risk factor for end-stage renal disease (ESRD) and has been associated with increased risk of death. In order to better understand the influence of diabetes on outcomes in hemodialysis, we examine the risk of death of diabetic participants in the HEMODIALYSIS (HEMO) study. Methods: In the HEMO study, 823 (44.6%) participants were classified as diabetic. Using the Schoenfeld residual test, we found that diabetes violated the proportional hazards assumption. Based on this result, we fit two non-proportional hazard models: Cox’s time varying covariate model (Cox-TVC) that allows the hazard for diabetes to change linearly with time and Gray’s time-varying coefficient model. Results: Using the Cox-TVC, the hazard ratio (HR) for diabetes increased with each year of follow up (p = 0.02) for all cause mortality. Using Gray’s model, the HR for diabetes ranged from 1.41 to 2.21 (p <0.01). The HR for diabetes using Gray’s model exhibited a different pattern, being relatively stable at 1.5 for the first 3 years in the study and increasing afterwards. Conclusion: Risk of death associated with diabetes in ESRD increases over time and suggests that an increasing risk of death among diabetes may be underappreciated when using conventional survival models.

The determination of left ventricular ejection fraction using cardiovascular magnetic resonance (CMR) requires a steady cardiac rhythm for electrocardiogram (ECG) gating and multiple breathholds to minimize respiratory motion artifacts, which often leads to scan times of several minutes. The need for gating and breathholding can be eliminated by employing real-time CMR methods such as through-time radial GRAPPA. The aim of this study is to compare left ventricular cardiac functional parameters obtained using current gold-standard breathhold ECG-gated functional scans with non-gated free-breathing real-time imaging using radial GRAPPA, and to determine whether scan time or the occurrence of artifacts are reduced when using this real-time approach. 63 patients were scanned on a 1.5T CMR scanner using both the standard cardiac functional examination with gating and breathholding and the real-time method. Total scan durations were noted. Through-time radial GRAPPA was employed to reconstruct images from the highly accelerated real-time data. The blood volume in the left ventricle was assessed to determine the end systolic volume (ESV), end diastolic volume (EDV), and ejection fraction (EF) for both methods, and images were rated for the presence of artifacts and quality of specific image features by two cardiac readers. Linear regression analysis, Bland-Altman plots and two-sided t-tests were performed to compare the quantitative parameters. A two-sample t-test was performed to compare the scan durations, and a two-sample test of proportion was used to analyze the presence of artifacts. For the reviewers´ ratings the Wilcoxon test for the equality of the scores´ distributions was employed. The differences in EF, EDV, and ESV between the gold-standard and real-time methods were not statistically significant (p-values of 0.77, 0.82, and 0.97, respectively). Additionally, the scan time was significantly shorter for the real-time data collection (p<0.001) and fewer artifacts were reported in the real-time images (p<0.01). In the qualitative image analysis, reviewers marginally preferred the standard images although some features including cardiac motion were equivalently rated. Real-time functional CMR with through-time radial GRAPPA performed without ECG-gating under free-breathing can be considered as an alternative to gold-standard breathhold cine imaging for the evaluation of ejection fraction in patients.

Mothers of infants in the neonatal intensive care unit (NICU) face stressors including turbulent emotions from their pregnancy/unexpected preterm delivery and their infant's unpredictable health status. The study purpose was to examine the psychological state of mothers prior to the discharge of their technology-dependent infants (eg, feeding tubes, supplemental oxygen) from the NICU to home. The study sample consisted of mothers (N = 19) of infants dependent on medical technology being discharged from a large Midwest NICU. A descriptive, correlational design using convenience sampling was employed to recruit mothers to examine associations of infant and maternal factors, resourcefulness, and stress with psychological state (depressive symptoms, posttraumatic stress symptoms). Forty-two percent of mothers were at high risk for clinical depression, with 37% in the clinical range for posttraumatic stress disorder. Increased maternal depressive symptoms were significantly associated with the increased frequency and perceived difficulty of their stress and posttraumatic stress symptoms. Increased posttraumatic stress symptoms were significantly associated solely with elevated depressive symptoms. This study identified factors associated with the mothers' increased psychological distress, providing beginning evidence for future interventions to employ prior to their technology-dependent infant's NICU discharge.