Intravenous Acetaminophen Does Not Provide Adequate Postoperative Analgesia in Dogs Following Ovariohysterectomy

(1) Objective: To investigate the analgesic effects of intravenous acetaminophen after intravenous administration in dogs presenting for ovariohysterectomy. (2) Methods: 14 ASA I client-owned female entire dogs. In this randomized, blinded, clinical study, dogs were given meperidine and acepromazine intramuscularly before induction of anesthesia with intravenous propofol. Anesthesia was maintained with isoflurane in oxygen. Intravenous acetaminophen 20 mg/kg or 0.9% NaCl was administered postoperatively. Pain assessments were conducted using the Glasgow Pain Scale short form before premedication and at 10, 20, 60, 120, and 180 min post-extubation or until rescue analgesia was given. The pain scores, times, and incidences of rescue analgesia between the groups was compared. Blood was collected before and 2, 5, 10, 20, 40, and 80 min after acetaminophen administration. Acetaminophen plasma concentration was quantified by liquid chromatography-mass spectrometry. The acetaminophen plasma concentration at the time of each pain score evaluation was subsequently calculated. (3) Results: There was no significant difference in pain scores at 10 min, highest pain scores, or time of rescue analgesia between groups. In each group, 3 dogs (43%) received rescue analgesia within 20 min. (4) Conclusions: Following ovariohysterectomy in dogs, there was no detectable analgesic effect of a 20 mg/kg dosage of intravenous acetaminophen administered at the end of surgery.

EVALUATION METHODOLOGY ON TRAJECTORY OF INBOUND SINGLE SHIP USING SIMILARITY MEASUREMENT BETWEEN PLANAR CLOUDS

In order to evaluate the ship trajectory more reasonable based on the quantitative information. This paper presents a new approach to evaluate the inward-port single ship trajectory quantitatively based on ship-handling simulator. First, a ship tracking points generating algorithm is proposed to generate sufficient tracking points in order to address the issue that the sample information is not enough on the ship simulator. Second, three reference tracking belts are established based on the sample data and cloud drop contribution degrees for the scenario that the collected samples information are enough. Finally, a quantitative score evaluation method that combines the qualitative information and the quantitative information is proposed, the similarity measurement results verify that the MES algorithm is more reasonable, the evaluation results of inward-port single ship trajectory illustrative that the proposed method is effective when applied to quantitative evaluation problems.

Influence model with opinions and trust score evaluations under the leader-follower environment

A group decision-making process is introduced by utilizing the influence model together with a matrix of interpersonal influences and an opinion matrix. The opinion matrix is constructed with the opinions/advice from one group of experts towards the other. Experts are divided into two groups, one which has more experienced, skilled and qualified persons is known as the group of opinion leaders and the other is known as the group of opinion followers. Sometimes, decision-makers are ordinary agents and their opinion formation is profoundly influenced by opinion leaders. The truthfulness of opinion leaders and the interpersonal influences of decision-makers is also taken into account. Also, a modified definition of trust score evaluation is presented with the understanding of the fact that the maximum trust which a decision-maker can do upon some opinion leader is his/her truthfulness. On the basis of this definition, a trust score matrix is constructed and the influence model is modified to take into account that matrix.

Synthesis and Acivity of Chiral Amino Alcohols and Amides as New Cholinesterase Inhibitors for Alzheimer's Disease

A series of biological active compounds 1–14 have been synthesized and used as potential inhibitors for AChE and BuChE. Potential inhibitor efficacy of these molecules to the target enzymes have been searched in vitro and theoretical by dock and molecular dynamic calculations. The results show that chiral amino alcohol compounds 6, 7 and 9 exhibited good value for medication scores. Among the tested compounds the best inhibition activities have been obtained with compounds 6 for AChE and BuChE by 87.68 and 92.46 % values, respectively at 50µg/mL concentration. The anticipated value of 6 also justified superb correlation with invitro statistics and it could be taken into consideration as drug candidate molecule for designing of novel drug. Potential inhibitory outcomes of those molecules on the right track proteins were investigated the use of Docking and Molecular Dynamics calculations. Dock score evaluation and Lipinski parameters have been proven those ligands are ability inhibitors against applicable enzymes. Our findings endorse that related compounds can be applied as a capacity supply of anti-alzheimer active molecules for designing novel products.

Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut

In evaluating agricultural products, knowing the specific product varieties is important for the producer, the industrialist, and the consumer. Human labor is widely used in the classification of varieties. It is generally performed by visual examination of each sample by experts, which is very laborious and time-consuming with poor sensitivity. There is a need in commercial hazelnut production for a rapid, non-destructive and reliable variety classification in order to obtain quality nuts from the orchard to the consumer. In this study, a convolutional neural network, which is one of the deep learning methods, was preferred due to its success in computer vision. A total of 17 widely grown hazelnut varieties were classified. The proposed model was evaluated by comparing with pre-trained models. Accuracy, precision, recall, and F1-Score evaluation metrics were used to determine the performance of classifiers. It was found that the proposed model showed a better performance than pre-trained models in terms of performance evaluation criteria. The proposed model was found to produce 98.63% accuracy in the test set, including 510 images. This result has shown that the proposed model can be used practically in the classification of hazelnut varieties.