Improvement in Outcomes of Major Obstetric Hemorrhage Through Systematic Change

Nickel Oxide (NiO) is an important transition metal oxide with cubic lattice structure. NiO is thermally stable that is suitable for tremendous applications in the field of optic, ceramic,glass, electro-chromic coatings, plastics, textiles, nanowires, nanofibers, electronics,energy technology, bio-medicine, magnetism and so on. In this present study, NiO nanoparticles were successfully synthesized by sol-gel technique. Nano-sols were prepared by dissolving Nickel-Chloride [NiCl2.6H2O] in NaOH solvent and were converted into nano structured gel on precipitation. A systematic change in preparation parameters like calcination temperature, time, pH value has been noticed in order to predict the influence on crystallite size. Then the prepared samples were characterized by the X-ray Diffraction Spectroscopic (XRD), UV-VIS Spectroscopy, Fourier Transform Infra-Red Spectroscopy (FTIR), Energy Dispersive X-ray Spectroscopy (EDX), Scanning Electron Microscopy (SEM) and Particle Size Analyzer (PSA). From XRD, the average crystalline-size has been calculated by Debye-Scherrer Equation and it was found to be 12.17 nm and the band gap energy of Nickel oxide (NiO) from UV studies reveals around 3.85 eV. Further, EDX and FTIR studies, confirm the presences of NiO nanoparticles. The SEM study exhibits the spherical like morphology of Nickel oxide (NiO). Further from PSA, the mean value of NiO nanoparticles has been determined.

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Differences in Life‐Saving Obstetric Hemorrhage Treatments for Women with Abortion Versus Nonabortion Etiologies in Tanzania

Studies in Family Planning ◽

10.1111/sifp.12101 ◽

2019 ◽

Vol 50 (4) ◽

pp. 375-393

Author(s):

Lauren Smith ◽

Michelle Skaer Therrien ◽

Kim G. Harley ◽

Selemani Mbuyita ◽

Zacharia Mtema ◽

...

Keyword(s):

Obstetric Hemorrhage ◽

Life Saving

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Benzodiazepines: the time for systematic change is now

Addiction ◽

10.1111/add.15488 ◽

2021 ◽

Author(s):

Roy Robertson ◽

Andrew McAuley ◽

Catriona Matheson

Keyword(s):

Systematic Change

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Anatomy of Motor Learning. II. Subcortical Structures and Learning by Trial and Error

Journal of Neurophysiology ◽

10.1152/jn.1997.77.3.1325 ◽

1997 ◽

Vol 77 (3) ◽

pp. 1325-1337 ◽

Cited By ~ 294

Author(s):

M. Jueptner ◽

C. D. Frith ◽

D. J. Brooks ◽

R.S.J. Frackowiak ◽

R. E. Passingham

Keyword(s):

Motor Learning ◽

Response Times ◽

Cerebellar Nuclei ◽

Selection Task ◽

Systematic Change ◽

Trial And Error ◽

Subcortical Structures ◽

Learning Condition ◽

Positron Emission ◽

New Learning

Jueptner, M., C. D. Frith, D. J. Brooks, R.S.J. Frackowiak, and R. E. Passingham. Anatomy of motor learning. II. Subcortical structures and learning by trial and error. J. Neurophysiol. 77: 1325–1337, 1997. We used positron emission tomography to study motor learning by trial and error. Subjects learned sequences of eight finger movements. Tones generated by a computer told the subjects whether any particular move was correct or incorrect. A control condition was used in which the subjects generated moves, but there was no feeback to indicate success or failure, and so no learning occured. In this condition (free selection) the subjects were required to make a finger movement on each trial and to vary the movements randomly over trials. The subjects had a free choice of which finger to move on any one trial. On this task there was no systematic change in responses over trials and no change in the response times. Two other conditions were included. In one the subjects repetitively moved the same finger on all trials and in a baseline condition the subjects heard the pacing tones and auditory feedback but made no movements. Comparing new learning with the free selection task, there was a small activation in the right prefrontal cortex. This may reflect the fact that in new learning, but not free selection, the subjects rehearse past moves and adapt their responses accordingly. The caudate nucleus was strongly activated during new learning. It is suggested that this activity may be related either to mental rehearsal or to reinforcement of the movements as a consequence of the outcomes. The putamen was activated anteriorly on the free selection task and more posteriorly when the subjects repetitively made the same movement. It is suggested that the differences in the location of the peak activation in the striatum may represent the operation of different corticostriatal loops. The cerebellar nuclei (bilaterally) and vermis were more active in the new learning condition than during the performance of the free selection task. There was no difference in the activation of the cerebellum when the free selection task was compared with repetitive performance of the same movement. We tentatively suggest that the basal ganglia may be involved in the specification of movement on the basis of memory of either the movements or the outcomes, but that the cerebellum may be more directly involved in changes in the parameters of movement execution.

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Sensorimotor priors in nonstationary environments

Journal of Neurophysiology ◽

10.1152/jn.00605.2012 ◽

2013 ◽

Vol 109 (5) ◽

pp. 1259-1267 ◽

Cited By ~ 15

Author(s):

Devika Narain ◽

Robert J. van Beers ◽

Jeroen B. J. Smeets ◽

Eli Brenner

Keyword(s):

Environmental Variables ◽

A Priori ◽

Past Research ◽

Systematic Change ◽

Recent Experience ◽

Environmental Variance ◽

Adaptive Models ◽

Priori Information ◽

Human Nervous System ◽

Better Than

In the course of its interaction with the world, the human nervous system must constantly estimate various variables in the surrounding environment. Past research indicates that environmental variables may be represented as probabilistic distributions of a priori information (priors). Priors for environmental variables that do not change much over time have been widely studied. Little is known, however, about how priors develop in environments with nonstationary statistics. We examine whether humans change their reliance on the prior based on recent changes in environmental variance. Through experimentation, we obtain an online estimate of the human sensorimotor prior (prediction) and then compare it to similar online predictions made by various nonadaptive and adaptive models. Simulations show that models that rapidly adapt to nonstationary components in the environments predict the stimuli better than models that do not take the changing statistics of the environment into consideration. We found that adaptive models best predict participants' responses in most cases. However, we find no support for the idea that this is a consequence of increased reliance on recent experience just after the occurrence of a systematic change in the environment.

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