End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification

As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1\% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods

Residues and dissipation kinetics of two imidacloprid nanoformulations on bean (Phaseolus vulgaris L.) under field conditions

The current study investigates the dissipation kinetics of two imidacloprid (IMI) nanoformulations (entitled: Nano-IMI and Nano-IMI/TiO2) on common bean (Phaseolus vulgaris) seeds under field conditions and compares them with 35% Suspension Concentrate (SC) commercial formulation. To do so, it sprays P. vulgaris plants at 30 and 60 g/ha within green bean stage, sampling them during the 14-day period after the treatment. Following extraction and quantification of IMI residues, dissipation data have been fitted to simple-first order kinetic model (SFOK) and to first-order double-exponential decay (FODED) models, with 50% and 90% dissipation times (DT50 and DT90, respectively) assessed along the pre-harvest interval (PHI). With the exception of Nano-IMI at 60 g/ha, other decline curves are best fitted to the FODED model. In general, dissipation is faster for Nano-IMI (at 30 g/ha: DT50 = 1.09 days, DT90 = 4.30 days, PHI = 1.23 days; at 60 g/ha: DT50 = 1.29 days, DT90 = 4.29 days, PHI = 2.95 days) and Nano-IMI/TiO2 (at 30 g/ha: DT50 = 1.15 days, DT90 = 4.40 days, PHI = 1.08 days; at 60 g/ha: DT50 = 0.86 days, DT90 = 4.92 days, PHI = 3.02 days), compared to 35% SC (at 30 g/ha: DT50 = 1.58, DT90 = 6.45, PHI = 1.93; at 60 g/ha: DT50 = 1.58 days, DT90 = 14.50 days, PHI = 5.37 days). These results suggest the suitability of Nano-IMI and Nano-IMI/TiO2 application at both rates in terms of their residues on P. vulgaris seeds

Impact of dendritic polymers on nanomaterials

For many years scientists have employed dendritic polymers (dendrimers and hyperbranched polymers) in association with other nanomaterials (such as graphene, carbon nanotubes, proteins and peptides, as well as metallic nanoparticles) to synthesize hybrid nanomaterials with improved biocompatibility, biodegradability, functionality, physicochemical properties and the capability of carrying other molecules. However, more recent studies demonstrate that one of the less noticed effects and newly observed facets of dendritic polymers is their role in changing the structure (shape, size and sheet multiplicity) of the obtained hybrid nanomaterials, upon covalent and noncovalent interactions. In this review, we intend to have a more specialized look at these reports and discuss the ‘whys’ and ‘hows’ of this phenomenon

Investigating fish purchase patterns and preferences among the consumers of Sari

The present study aimed at looking into the fish consumption preferences and purchase patterns among 266 households of Sari in order to identify the fish market at Mazandaran province. To do so, a questionnaire was provided to be administered among consumers to state their preferences towards the type of fish species, purchased forms of fish as well as fish purchasing pattern in their family. Descriptive statistics as well as referential analysis was conducted through Friedman Test. Results showed that meat consumption priority among the households of Sari constituted the consumption of poultry meat, lamb, fish and beef, respectively. As to the investigation of fish purchase pattern, it was that almost two-thirds of households in Sari bought fish less than once a month and only a few percentage of them (4.1 percent) purchased them on a weekly basis. It was also revealed that consumers in Sari prefer marine fish more than farmed fish, and cold-water fish more than warm-water fish. Fresh, live and canned fish were the preferred forms of purchase for the consumers in Sari, and other forms of fish such as frozen, smoked and salted fish (total 7.9 percent) were rarely preferred by the consumers. Findings of the current research can contribute to powerful decision-making of companies and suppliers in terms of which product should be provided to the market for more sale. Therefore, recognizing the needs and desires of consumers and understanding their purchase behavior are effective steps to meet their expectations and ultimately increasing fish consumption

The feasibility and benefits of using high-strength concrete for construction purposes in earthquake prone areas

In recent years, concrete technology has benefited from great advances and evolutions that lead to emergence of new concrete with different properties. One of the most important of these concretes is high strength concrete (HSC). The emergence of HSC has made possible to high-rise buildings and towers with architectural art and delicacy and it is expected that in the next few years, there is the possibility of using HSC in wider areas. Examining the studies on this type of concrete, this paper has deal with the feasibility and benefits of using HSC for construction purposes in earthquake prone areas. The results of this study show that in case of respecting the bylaw constrains and conformity of new bylaws with this type of concrete, it is hoped to use it as a reliable option for safe construction in seismic areas.Keywords: Concrete, High-Strength, Construction, Seismic Area, Feasiblit

Effect of Early Post Cesarean Feeding on Gastrointestinal Complications

Background: Gastrointestinal complications are the main complication in patients after cesarean section. Previous studies have reported different results about the effect of early post cesarean feeding on vomiting, nausea, flatulence and illus. Objectives: To identify the effect of early post cesarean feeding on gastrointestinal complications. Materials and Methods: This randomized controlled trial was conducted on 82 women who underwent cesarean section in Mashhad Omolbanin hospital. They were randomly assigned to two equal experimental and control groups. The experimental group started oral fluids four hours after surgery, followed by a regular diet after bowel sounds returned. Mothers in the control group received fluid intravenously during the initial 12 hours, and then if bowel sounds were heard, they were permitted to receive oral fluids and they could start a solid diet if they had defecation. Vomiting and flatulence were assessed with a visual analog scale. Nausea was assessed with an observation questionnaire and illus was assessed via bowel sounds, gas passing and defecation 4, 12, 24, 36 and 48, hours post surgery in the two groups. Also, they were studied for the time of gas passing, bowel sound return, defecation, sitting, walking and breast-feeding. Data were analyzed using the chi-square, Fisher's exact test, t-test and Man-Whitney U test. Results: No mother experienced nausea, vomiting and illus. Flatulence severity 4 and 12 hours after surgery was similar in both groups (P = 0.856, P = 0.392). However, flatulence severity 24, 36 and 48 hours after surgery, was less in the experimental group (P = 0.030, P = 0.016, P = 0.001). Also, bowel sound return, time of gas passing, defecation, sitting and walking were less in the experimental group (P = 0.001). Conclusion: This study showed that early feeding decreased post cesarean gastrointestinal complications

Generating Realistic 3D Brain MRIs Using a Conditional Diffusion Probabilistic Model

Training deep learning models on brain MRI is often plagued by small sample size, which can lead to biased training or overfitting. One potential solution is to synthetically generate realistic MRIs via generative models such as Generative Adversarial Network (GAN). However, existing GANs for synthesizing realistic brain MRIs largely rely on image-to-image conditioned transformations requiring extensive, well-curated pairs of MRI samples for training. On the other hand, unconditioned GAN models (i.e., those generating MRI from random noise) are unstable during training and tend to produce blurred images during inference. Here, we propose an efficient strategy that generates high fidelity 3D brain MRI via Diffusion Probabilistic Model (DPM). To this end, we train a conditional DPM with attention to generate an MRI sub-volume (a set of slices at arbitrary locations) conditioned on another subset of slices from the same MRI. By computing attention weights from slice indices and using a mask to encode the target and conditional slices, the model is able to learn the long-range dependency across distant slices with limited computational resources. After training, the model can progressively synthesize a new 3D brain MRI by generating the first subset of slices from random noise and conditionally generating subsequent slices. Based on 1262 t1-weighted MRIs from three neuroimaging studies, our experiments demonstrate that the proposed method can generate high quality 3D MRIs that share the same distribution as real MRIs and are more realistic than the ones produced by GAN-based models