Optimization of the damped quantum search

The damped quantum search proposed in [A. Mizel, Phys. Rev. Lett., 102 150501 (2009)] was analyzed by calculating the highest possible probability of finding the target state in each iteration. A new damping parameter that depends on the number of iterations was obtained, this was compared to the critical damping parameter for different values of target to database size ratio. The result shows that the range of the new damping parameter as a function of the target to database size ratio increases as the number of iterations is increased. Furthermore, application of the new damping parameter per iteration on the damped quantum search scheme shows a significant improvement on some target to database size ratio (i.e. greater than or equal to 50% maximum percentage difference) over the critically damped quantum search

Discharge Education Protocol to Improve Patient Satisfaction

Background: Discharge education that is patient-centered and started in a timely manner provides patients with the knowledge to care for themselves in the home setting and prevent adverse outcomes from occurring. Methods: A discharge education protocol was implemented on a cardiology inpatient unit. The registered nurses were given a discharge checklist that focused on education and discharge planning with the patient within 24 hours of admission. Patients completed a survey that measured satisfaction of staff education. Surveys were collected for 10 weeks pre-intervention and compared with surveys 10 weeks post-intervention. Data was collected on the time of discharge with the intention the patients would discharge by noon. Results: Post-implementation scores in each category were lower when compared to preimplementation due to means being higher pre-implementation. Discharge time improved from 1506 to 1428 post-implementation with the use of the discharge checklist. Discussion: Checklists have been found to help formalize and standardize the discharge process, despite the lack of significant change in pre-and post-implementation, discharge time improved. Implications for Practice: Staff stated it would be beneficial to place the checklist in the electronic medical record to serve as a daily reminder and have education provided inperson rather than via email

STABILIZATION OF THE COLLAGEN TRIPLE HELIX WITH A RUTHENIUM(II) ANCHOR

Two collagen analogues based on a (Pro-Hyp-Gly)7 core were given metal-binding ability by linking histidine to either the N- or C- termini using a six-carbon spacer. Circular dichroism studies confirmed that the isomers, dubbed HPOG and POGH, form triple-helices which cooperatively unwind at Tm = 34.8 and 35.6 °C, respectively. Three strands of the HPOG peptide were bound to a ruthenium ion, by heating in the presence of tris(pyrazol-1-yl)borato ruthenium(II) (Tp-Ru). The strands of the N-Anchored complex unwind at a temperature about 10 °C higher, but do so over a broader temperature span. Thus, immobilization of collagen on a metal ion hub appears to increase helix stability while decreasing cooperativity of folding/unfolding. DFT calculations suggest that the loss of cooperativity arises from the disruption of interstrand hydrogen bonding. Other spacer groups, or other metal ions, may be necessary to promote optimal approach of strands to each other

Fermentation Utilization of Cassava. The Butyl-Acetonic Fermentation

The cassava plant belongs to the family Euphorbiaccaee and is botanically known as Manihot utilissima Pohl. It is also called tapioca or manioc although the word tapioca is often used to designate certain forms of cassava products. The cassava is a plant possessing quite unusual characteristics. It has no known pests nor enemies. It grows in most soils, resists extreme droughts, and propagates easily although its growth is restricted to tropical regions. The plant itself is a perennial shrub which attains a height of six to twelve feet at the age of one year. At the base of its stem it produces a cluster of long fleshy roots. The starch content of the fresh cassava root is 25 to 30 per cent; these roots furnish the cheapest source of starch known

HOW CHARGED RESIDUES INFLUENCE THE THERMAL STABILITY OF COLLAGEN: A STUDY WITH NATURAL AND NON-NATURAL AMINO ACIDS

Triple-helical collagens are key structural proteins in mammals. Their ubiquity and diverse functions drive our interest into understanding their behavior at a fundamental level. This thesis describes a reductionist approach using novel collagen-related peptides (CRPs), into which one or more electrical charges have been imparted at known positions. One series of CRPs includes fluorescent pyrene tags at their N-termini, directly adjacent to the charged residues lysine (Lys, K) and glutamic acid (Glu, E). When in close contact, the fluorophores form excimers that emit low-energy light. Monitoring of the excimer intensity shows that nucleation of collagen peptides is critically dependent on the charge location. Another series of CRPs features pH-independent (permanent) positive charge close to the peptide backbone, via a synthetic proline derivative called "Map." When in close contact, repulsion between Map residues overwhelms the natural tendencies of the peptides to fold. CD and fluorescence investigations into the thermodynamic and kinetic behaviors of these CRPs have been supplemented with computational analyses, to shed light on the deleterious role of charge in trimer formation

Estimating animal pose using deep learning a trained deep learning model outperforms morphological analysis

INTRODUCTION: Analyzing animal behavior helps researchers understand their decision-making process and helper tools are rapidly becoming an indispensable part of many interdisciplinary studies. However, researchers are often challenged to estimate animal pose because of the limitation of the tools and its vulnerability to a specific environment. Over the years, deep learning has been introduced as an alternative solution to overcome these challenges. OBJECTIVES: This study investigates how deep learning models can be applied for the accurate prediction of animal behavior, comparing with traditional morphological analysis based on image pixels. METHODS: Transparent Omnidirectional Locomotion Compensator (TOLC), a tracking device, is used to record videos with a wide range of animal behavior. Recorded videos contain two insects: a walking red imported fire ant (Solenopsis invicta) and a walking fruit fly (Drosophila melanogaster). Body parts such as the head, legs, and thorax, are estimated by using an open-source deep-learning toolbox. A deep learning model, ResNet-50, is trained to predict the body parts of the fire ant and the fruit fly respectively. 500 image frames for each insect were annotated by humans and then compared with the predictions of the deep learning model as well as the points generated from the morphological analysis. RESULTS: The experimental results show that the average distance between the deep learning-predicted centroids and the human-annotated centroids is 2.54, while the average distance between the morphological analysis-generated centroids and the human-annotated centroids is 6.41 over the 500 frames of the fire ant. For the fruit fly, the average distance of the centroids between the deep learning- predicted and the human-annotated is 2.43, while the average distance of the centroids between the morphological analysis-generated and the human-annotated is 5.06 over the 477 image frames. CONCLUSION: In this paper, we demonstrate that the deep learning model outperforms traditional morphological analysis in terms of estimating animal pose in a series of video frames