• Animal Bioscience
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• v.34 no.5
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• pp.886-894
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• 2021

Objective: An experiment was conducted to study the effect of graded concentration of digestible lysine (dLys) on performance of layers fed diets containing sub-optimal level of protein. Methods: Five diets were formulated to contain graded concentrations of dLys (0.700%, 0.665%, 0.630%, 0.593%, and 0.563%), but similar levels of crude protein (15% CP), energy (10.25 MJ ME/kg) and other nutrients. A total of 3,520 hens (26 wk of age) with mean body weight of 1,215+12.65 g were randomly divided into 40 replicate groups of 88 birds in each and housed in an open sided colony cage house. Each diet was offered ad libitum to eight replicates from 27 to 74 wk of age. The performance was compiled at every 28 d and the data for each parameter were grouped into three phases, that is early laying phase (27 to 38 wk), mid laying phase (39 to 58 wk), and late laying phase (59 to 74 wk of age) for statistical analysis. Results: Egg production, egg mass and feed efficiency (feed required to produce an egg) were significantly improved by the dLys level during the early and mid laying phases but not during the late phase. Whereas feed intake was significantly reduced by dLys concentration during mid and late laying phases but not during early laying phase. The egg weight was not affected by dLys concentration in any of the three phases. Conclusion: Based on best fitted statistical models, dietary requirements of dLys worked out to be 0.685%, 0.640%, and 0.586% during early phase, mid phase, and late egg laying phase, respectively. The calculated requirement of dLys for the respective production phases are 727 mg/b/d during the early and mid laying phases and 684 mg/b/d during the late laying phase in diets containing 15% CP.

• Earthquakes and Structures
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• v.17 no.2
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• pp.221-232
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• 2019

Material non-linearity of Reinforced Concrete (RC) framed structures is studied by modelling concrete using the Concrete Damage Plasticity (CDP) theory. The stress-strain data of concrete in compression is modelled using the Hsu model. The structures are analyzed using a finite element approach by modelling them in ABAQUS / CAE. Single bay single storey RC frames, designed according to Indian Standard (IS):456:2000 and IS:13920:2016 are considered for assessing their maximum load carrying capacity and failure behavior under the influence of gravity loads and lateral loads. It is found that the CDP model is effective in predicting the failure behaviors of RC frame structures. Under the influence of the lateral load, the structure designed according to IS:13920 had a higher load carrying capacity when compared with the structure designed according to IS:456. Ultra High Performance Fiber Reinforced Concrete (UHPFRC) strip is used for strengthening the columns and beam column joints of the RC frame individually against lateral loads. 10mm and 20mm thick strips are adopted for the numerical simulation of RC column and beam-column joint. Results obtained from the study indicated that UHPFRC with two different thickness strips acts as a very good strengthening material in increasing the load carrying capacity of columns and beam-column joint by more than 5%. UHPFRC also improved the performance of the RC frames against lateral loads with an increase of more than 3.5% with the two different strips adopted. 20 mm thick strip is found to be an ideal size to enhance the load carrying capacity of the columns and beam-column joints. Among the strengthening locations adopted in the study, column strengthening is found to be more efficient when compared with the beam column joint strengthening.

• Journal of Dental Anesthesia and Pain Medicine
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• v.21 no.1
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• pp.1-14
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• 2021

This study aimed to evaluate and compare the pre-emptive analgesic efficacy of injected ketorolac to that of other agents for impacted third molar surgical removal in a healthy population. PubMed, Ovid SP, Cochrane databases were filtered from 1980 to July 2020 for potential papers using relevant MeSH terms and pre-specified inclusion and exclusion criteria independently by reviewers. Studies that compared pre-emptive intramuscular or intravenous administration of ketorolac to other agents were evaluated. The outcomes sought were self-reported postoperative pain (patient-perceived pain), median duration for rescue analgesic medication, total number of analgesics consumed in the recovery period, and global assessment (overall patient satisfaction) after the recovery period. Six studies were included in the final evaluation. The outcome of pain perception and the number of analgesics taken were significantly lower in the ketorolac group (intramuscular or intravenous) in most of the studies (n=5) than in the group of other drugs. The mean time for rescue analgesia intake was higher for the ketorolac group, and global assessment scores were also better in the ketorolac group. Although the included studies show significantly better outcomes such as postoperative pain, median time taken for rescue medication, total number of analgesics taken, and overall patient satisfaction with injected ketorolac group in comparison to injected diclofenac, dexamethasone, and tramadol, definitive conclusions cannot be made regarding the superiority of injected Ketorolac as a pre-emptive agent. A greater number of randomized control trials with a proper protocol are needed to make definitive conclusions.

• Journal of Astronomy and Space Sciences
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• v.39 no.2
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• pp.67-77
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• 2022

The elements that impact the dynamics and collaborations of waves and particles in the magnetosphere of planets have been considered here. Saturn's internal magnetosphere is determined by substantiated instabilities and discovered to be an exceptional zone of wave activity. Interchanged instability is found to be one of the responsible events in view of temperature anisotropy and energization processes of magnetospheric species. The generated active ions alongside electrons that constitute the populations of highly magnetized planets like Saturn's ring electron current are taken into consideration in the current framework. The previous and similar method of characteristics and the perturbed distribution function have been used to derive dispersion relation. In incorporating this investigation, the characteristics of electromagnetic ion cyclotron wave (EMIC) waves are determined by the composition of ions in plasmas through which the waves propagate. The effect of ring distribution illustrates non-monotonous description on growth rate (GR) depending upon plasma parameters picked out. Observations made by Cassini found appropriate for modern study, have been applied to the Kronian magnetosphere. Using Maxwellian ring distribution function of ions and detailed mathematical formulation, an expression for dispersion relation as well as GR and real frequency (RF) are evaluated. Analysis of plasma parameters shows that, proliferating EMIC waves are not developed much when propagation is parallelly aligned with magnetosphere as compared to waves propagating in oblique direction. GR for the oblique case, is influenced by temperature anisotropy as well as by alternating current (AC) frequency, whereas it is much affected only by AC frequency for parallel propagating waves.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Genomics & Informatics
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• v.19 no.3
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• pp.29.1-29.10
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• 2021

In our previous studies, we have demonstrated the association of certain variants of the thyroid-stimulating hormone receptor (TSHR), thyroid peroxidase (TPO), and thyroglobulin (TG) genes with congenital hypothyroidism. Herein, we explored the mechanistic basis for this association using different in silico tools. The mRNA 3'-untranslated region (3'-UTR) plays key roles in gene expression at the post-transcriptional level. In TSHR variants (rs2268477, rs7144481, and rs17630128), the binding affinity of microRNAs (miRs) (hsa-miR-154-5p, hsa-miR-376a-2-5p, hsa-miR-3935, hsa-miR-4280, and hsa-miR-6858-3p) to the 3'-UTR is disrupted, affecting post-transcriptional gene regulation. TPO and TG are the two key proteins necessary for the biosynthesis of thyroid hormones in the presence of iodide and H₂O₂. Reduced stability of these proteins leads to aberrant biosynthesis of thyroid hormones. Compared to the wild-type TPO protein, the p.S398T variant was found to exhibit less stability and significant rearrangements of intra-atomic bonds affecting the stoichiometry and substrate binding (binding energies, ΔG of wild-type vs. mutant: -15 vs. -13.8 kcal/mol; and dissociation constant, K_d of wild-type vs. mutant: 7.2E^-12 vs. 7.0E^-11 M). The missense mutations p.G653D and p.R1999W on the TG protein showed altered ΔG(0.24 kcal/mol and 0.79 kcal/mol, respectively). In conclusion, an in silico analysis of TSHR genetic variants in the 3'-UTR showed that they alter the binding affinities of different miRs. The TPO protein structure and mutant protein complex (p.S398T) are less stable, with potentially deleterious effects. A structural and energy analysis showed that TG mutations (p.G653D and p.R1999W) reduce the stability of the TG protein and affect its structure-functional relationship.

• Animal Bioscience
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• v.35 no.5
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• pp.721-729
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• 2022

Objective: An experiment was conducted to determine the effects of supplementing graded concentrations of inorganic sulphur (S) without and with folic acid (FA) in maize-soybean meal diets on performance, slaughter and anti-oxidant variables, immune responses and serum protein fractions in broiler chicken. Methods: Inorganic S was supplemented at 0.05%, 0.10%, 0.15%, and 0.20% alone or in combination with FA (4 mg/kg) in basal diet (BD) containing no supplemental methionine (Met) and FA. A control group was fed with the recommended concentration of Met. Each diet was offered to 10 pens of 5 male broiler chicks (Cobb 400) and fed ad libitum from day 1 to 42. Results: The broilers fed the BD had lower body weight gain (BWG), feed efficiency (FE), higher lipid peroxidation (LP), lower activity of glutathione peroxidase (GSHPx), lower lymphocyte proliferation ratio (LPR), and reduced concentrations of total protein, albumin, and globulin in serum. Supplementation of FA and S to the BD improved the BWG (all concentrations of S) and FE (0.20% S) similar to the control group. Similarly, the combination of S and FA significantly improved the concentrations of total protein, albumin, and globulin in serum, reduced the LP and increased the activity of GSHPx and LPR. However, responses in the above parameters were related to the concentration of S in the diet. The slaughter variables and antibody titres against the Newcastle disease were not affected with the treatments. Conclusion: Based on the results, it is concluded that the combination of S (0.2%) and FA (4 mg/kg) improved the BWG and FE, similarly supplementation of these nutrients improved the concentration of protein fractions and reduced the stress (reduced LP and improved GSHPx) variables in serum and improved the cell mediated immune response (LPR) in broilers fed sub-optimal concentrations of Met in diet.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Animal Bioscience
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• v.35 no.3
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• pp.475-483
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• 2022

Objective: Methionine (Met) is involved in methyl group transfer besides protein synthesis. As the availability is limited and cost is high for synthetic Met, reductions in its inclusion in broiler diet may be possible by supplementing the low Met diets with methyl donors (MD) like betaine (Bet), folic acid (FA), vitamin B12 (B12), and biotin (Bio). An experiment was conducted to study the effects of supplementing the MD on performance (average daily gain [ADG], daily feed intake, feed efficiency [FE]), anti-oxidant variables, immune responses and serum protein concentration in broilers fed sub-optimal concentrations of dietary Met. Methods: Maize-soybean meal diet was used as control (CD). Different MD like Bet (0.2%), B12 (0.1 mg), FA (4 mg), or Bio (1.5 mg/kg) were supplemented to basal diet (BD) having no supplemental Met. The BD without MD was kept for comparison. Each diet was fed ad libitum to 10 replicates of 25 chicks in each from 1 to 42 d of age. Results: At the end of experiment, the ADG in MD group was higher than BD and lower than CD. The FE improved with FA or Bet compared to the BD. Breast meat weight was higher in Bet compared to the BD, while it was intermediate between BD and CD in other groups. The lipid peroxidation reduced with Bio, B12, or Bet, while the glutathione peroxidase activity improved with Bio or B12 compared to the BD. Lymphocyte proliferation improved with Bet compared to the BD. The serum protein concentrations increased with FA, Bio, or Bet compared to those fed BD. Conclusion: It can be concluded that the ADG can be improved partially with supplementation of MD while the FE improved with FA or Bet. Some MD also reduced the stress indices and improved immune responses compared to the BD fed broilers.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.