THE EVALUATION OF GRAIN AND OIL PRODUCTION, SOME PHYSIOLOGICAL AND MORPHOLOGICAL TRAITS OF AMARANTH ‘CV. KONIZ’ AS INFLUENCED BY THE SALT STRESS IN HYDROPONIC CONDITIONS

The purpose of this study was investigation of salinity effect on some traits of Amaranth. A split plot designed with three replications with two factors: 5 salinity levels (control, 75, 150, 225, 300 mM NaCl) and applied time at 4 levels (plant establishment, branching, flowering, grain filling) in a greenhouse under hydroponic system. Application of 300 mM salinity after plant establishment led to death of amaranth. Salinity application after establishment decreased significantly plant height and number of branches as 44.9 and 31.8, respectively. Production of grain weight was not affected by 75 mM salinity, but at higher salinity showed significantly decrease. The highest decrease in grain weight obtained by applying 225 mM salt after the plant establishment and salinity at 300 mM after branching as 86.6 and 71.3 percent respectively, resulting in a decrease in both 1000 kernel weight and grain number, respectively. Salinity application increased H2O2, MDA and total phenolics contents, severely. Most of characteristics hadnot affect by 75 mM NaCl, but other concentrations had a negative effect on the growth and production of Amaranth and increasing salinity had more negative impact. In this study, the most sensitive to salinity was after plant establishment and grain filling stage was the most tolerant

Relationship between Obstructive Sleep Apnea (OSA) and Difficult Intubation

BACKGROUND AND OBJECTIVE: One of the challenges of patients who are candidates for anesthesia is difficult intubation, which leads to severe complications and even death after anesthesia. The aim of this study is to investigate the relationship between obstructive sleep apnea and difficult intubation through systematic review and meta-analysis. METHODS: In this review article, observational articles about the relationship between obstructive sleep apnea and difficult intubation were extracted without time limit by searching national and international databases and the keywords were: difficult intubation, problematic intubation, Intra tracheal-endotracheal, difficult airway OSA, OSAS, obstructive sleep apnea, sleep breathing disorder, anesthesia, and their Persian equivalents. Data were analyzed using meta-analysis and fixed effects model. In order to study the heterogeneity and contradictions in the studies, Q Cochrane and I2 indices were used, respectively. FINDINGS: Of the 72 found articles, 9 articles with a sample size of 1,126 and an average of 125 subjects were included in the study. The results of this study showed that the relationship between obstructive sleep apnea and difficult intubation is significant (OR = 3.88, CI95% = 2.69 – 5.61). In addition, the results of the analysis based on country showed that the highest and lowest odds ratios were observed in studies conducted in France and Canada, respectively. CONCLUSION: The results of this study showed that there is a correlation between obstructive sleep apnea and difficult intubation

A surrogate model based on a finite element model of abdomen for real-time visualisation of tissue stress during physical examination training

Robotic patients show great potential to improve medical palpation training as they can provide feedback that cannot be obtained in a real patient. Providing information about internal organs deformation can significantly enhance palpation training by giving medical trainees visual insight based on their finger behaviours. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, thus able to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real-time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANN) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 hrs to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that the ANN has a 92.6% accuracy. We also show that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has potential to be used as a training simulator for trainees to hone their palpation skills

A multimodal deep learning framework using local feature representations for face recognition

YesThe most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that theCurvelet transform, a newanisotropic and multidirectional transform, can efficiently represent themain structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR)framework, to add feature representations by training aDBNon top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets

Fatty Acid Binding Protein 1 Is Related with Development of Aspirin-Exacerbated Respiratory Disease

BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) refers to the development of bronchoconstriction in asthmatics following the ingestion of aspirin. Although alterations in eicosanoid metabolites play a role in AERD, other immune or inflammatory mechanisms may be involved. We aimed to identify proteins that were differentially expressed in nasal polyps between patients with AERD and aspirin-tolerant asthma (ATA). METHODOLOGY/PRINCIPAL FINDINGS: Two-dimensional electrophoresis was adopted for differential display proteomics. Proteins were identified by liquid chromatography-tandem mass spectrometry (LC-MS). Western blotting and immunohistochemical staining were performed to compare the amount of fatty acid-binding protein 1 (FABP1) in the nasal polyps of patients with AERD and ATA. Fifteen proteins were significantly up- (seven spots) or down-regulated in the nasal polyps of patients with AERD (n = 5) compared to those with ATA (n = 8). LC-MS revealed an increase in seven proteins expression and a decrease in eight proteins expression in patients with AERD compared to those with ATA (P = 0.003-0.045). FABP1-expression based on immunoblotting and immunohistochemical analysis was significantly higher in the nasal polyps of patients with AERD compared to that in patients with ATA. FABP1 was observed in epithelial, eosinophils, macrophages, and the smooth-muscle cells of blood vessels in the polyps. CONCLUSIONS/SIGNIFICANCE: Our results indicate that alterations in 15 proteins, including FABP1, may be related to the development of AERD

Role of Innate Immunity in the Pathogenesis of Chronic Rhinosinusitis: Progress and New Avenues

Chronic rhinosinusitis is a heterogeneous and multifactorial disease with unknown etiology. Aberrant responses to microorganisms have been suggested to play a role in the pathophysiology of the disease. Research has focused on the presence, detection, response to, and eradication of these potential threats. Main topics seem to center on the contribution of structural cells such as epithelium and fibroblasts, on the consequences of activation of pattern-recognition receptors, and on the role of antimicrobial agents. This research should be viewed not only in the light of a comparison between healthy and diseased individuals, but also in a comparison between patients who do or do not respond to treatment. New players that could play a role in the pathophysiology seem to surface at regular intervals, adding to our understanding (and the complexity) of the disease and opening new avenues that may help fight this incapacitating disease