Effects of supplemental microbial phytase enzyme on performance and phytate phosphorus digestibility of a corn-wheat-soybean meal diet in broiler chicks

This experiment was conducted to investigate the effects of supplemental phytase in a corn-wheatsoybean meal basal diet on phosphorus (P) digestibility and performance of broiler chicks. 378 one-day old broiler chicks (Ross 308) were allocated to 3×3 factorial arrangements with three levels of phytase enzyme (0, 500 and 1000 FTU/kg) and three levels of non-phytate P (100, 80 and 60% of NRC requirements). Broiler chicks received experimental diets from 7 to 49 days of age. Phytase significantly (P < 0.05) improved body weight gain and feed intake. Tibial ash and P contents increased significantly by phytase supplementation. Ileal P digestibility increased and P excretion reduced by added phytase. The greatest response due to supplemental phytase regarding P digestibility and utilization was obtained at the lowest dietary non-phytate P (NPP) level (60% of NRC requirements). Difference between various levels of added phytase (500 and 1000 FTU/kg) regarding P excretion and utilization was not significant. The interactions between supplemental phytase and dietary NPP for P utilization, tibial ash and P contents were significant (P < 0.05). The results indicate that, supplemental microbial phytase (500 FTU/kg of diet) added to diet containing NPP lower than NRC requirements (60%) can improve growth performance, tibial ash and phytate P utilization in broiler chickens.Key words: Broiler, phytase, phosphorus digestibility, growth performance, tibia

Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

© 2018 Journal of Pathology Informatics. Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images

Effect of frozen storage on texture and color of fish burgers produced from Sarm surimi

Surimi is a high quality myofibrillar protein concentrate that is obtained from cheap and underutilized fish species. In this research, surimi burger was prepared from Sarm (Scomberiodes commersonnianus) surimi (60%) and other ingredients. Some quality attributes of surimi burger were investigated during 3 months of frozen storage at -20°C. Fat value was determined in fresh raw surimi and surimi burger. Physical properties such as color stability (L, a and b values) and textural hardness before and after cooking were determined for surimi burgers during frozen storage at -20°C. Results showed that the hardness of surimi burgers and cooked samples were 768gf and 204gf, respectively at-the beginning of storage, and it was increased at the end of storage (921gf for surimi burger and 462gf for the cooked sample). Hardness showed significant difference through storage (P0.05)

A combined convolutional and recurrent neural network for enhanced glaucoma detection.

Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection

Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding

Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images

Multi-material additive manufacturing of low sintering temperature Bi2Mo2O9 ceramics with Ag floating electrodes by selective laser burnout

Additive manufacturing (AM) of co-fired low temperature ceramics offers a unique route for fabrication of novel 3D radio frequency (RF) and microwave communication components, embedded electronics and sensors. This paper describes the first-ever direct 3D printing of low temperature co-fired ceramics/floating electrode 3D structures. Slurry-based AM and selective laser burnout (SLB) were used to fabricate bulk dielectric, Bi2Mo2O9 (BMO, sintering temperature = 620–650°C, εr = 38) with silver (Ag) internal floating electrodes. A printable BMO slurry was developed and the SLB optimised to improve edge definition and burn out the binder without damaging the ceramic. The SLB increased the green strength needed for shape retention, produced crack-free parts and prevented Ag leaching into the ceramic during co-firing. The green parts were sintered after SLB in a conventional furnace at 645°C for 4 h and achieved 94.5% density, compressive strength of 4097 MPa, a relative permittivity (εr) of 33.8 and a loss tangent (tan δ) of 0.0004 (8 GHz) for BMO. The feasibility of using SLB followed by a post-printing sintering step to create BMO/Ag 3D structures was thus demonstrated

A simple way to distinguish bed clothing contamination in a whole body bone scan: a case report

Whole body bone scan with Technetium-99m MDP (methylene diphosphate) can detect bony lesions due to altered osteoblastic activity

Variations of Plantaris, Palmaris Longus, and Fifth Superficial Flexor Tendons: a Cadaver Study

Abstract: Background & Aims: The Plantaris muscle is located in the superficial posterior compartment of the leg and is absent in some individuals. The present study was undertaken to evaluate the absence variation of Plantaris muscle and its possible relation with Palmaris longus and fifth superficial flexor digitorum of the hand. Methods: 83 fresh cadavers, referred to legal medicine center, Kerman, Iran from April 2010 to November 2011 were enrolled in the study. Dissection was performed using mini incisions and the results were recorded in a questionnaire including presence or absence, length and width of the Plantaris and Palmaris longus tendons, and the presence or absence of the fifth superficial flexor. The suitability of the Palmaris and Plantaris tendons was assessed using the previously described criteria and according to the criteria that are proposed by us. Results: 8 of the 83 cadavers showed variations. Plantaris tendon was present in 80 (96.3%) and absent in 3 (3.7%), of which was absent bilaterally in two (2.3%) and unilaterally in one (1.2%). 6 cadavers (7.2%) were deficient in Palmaris longus tendon, 4 bilateral (4.8%) and 2 (2.3%) unilateral. Bilateral absence of fifth flexor digitorum superficialis was observed in 2 cadavers (2.3%) and one showed unilateral absence (1.2%). There was no statistically significant relationship (p<0.05) among the presence of Palmaris longus, plantaris and fifth superficial flexor. The plantaris tendon by far showed better characteristics for graft than the Palmaris tendon. Conclusion: According to the results of the present study, considering the Plantaris tendon for graft harvest seems logic, noting its high percentage of presence and better characteristics Keywords: Plantaris Tendon, Palmaris Longus Tendon, Fifth Superficial flexor Tendon, Graf