Complete identification of four giant interneurons supplying mushroom body calyces in the cockroachPeriplaneta americana

The classic foundational status that Hobbes has been afforded by contemporary international relations theorists is largely the work of Hans Morgenthau, Martin Wight, and Hedley Bull. They were not unaware that they were to some extent creating a convenient fiction, an emblematic realist, a shorthand for all of the features encapsulated in the term. The detachment of international law from the law of nature by nineteenth-century positivists opened Hobbes up, even among international jurists, to be portrayed as almost exclusively a mechanistic theorist of absolute state sovereignty. If we are to endow him with a foundational place at all it is not because he was an uncompromising realist equating might with right, on the analogy of the state of nature, but instead to his complete identification of natural law with the law of nations. It was simply a matter of subject that distinguished them, the individual and the state.

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Stratification and synaptogenesis in the mushroom body of the honeybee,Apis mellifera

Journal of Morphology ◽

10.1002/jmor.10837 ◽

2010 ◽

pp. NA-NA

Author(s):

Olga Ganeshina

Keyword(s):

Apis Mellifera ◽

Mushroom Body

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Algorithmic prediction of failure modes in healthcare

International Journal for Quality in Health Care ◽

10.1093/intqhc/mzaa151 ◽

2020 ◽

Author(s):

Ayala Kobo-Greenhut ◽

Ortal Sharlin ◽

Yael Adler ◽

Nitza Peer ◽

Vered H Eisenberg ◽

...

Keyword(s):

Adverse Events ◽

Failure Modes ◽

Medical Center ◽

Hazard Analysis ◽

Working Hours ◽

Hazard Identification ◽

Patient Identification ◽

Complete Identification ◽

Before And After ◽

Fmea Method

Abstract Background Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely used prospective hazard analysis in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resource investments, and lack of complete and error-free results. Objectives To introduce the algorithmic prediction of failure modes in healthcare (APFMH) and to examine whether APFMH is leaner in resource allocation in comparison to the traditional FMEA and whether it ensures the complete identification of hazards. Methods The patient identification during imaging process at the emergency department of Sheba Medical Center was analyzed by FMEA and APFMH, independently and separately. We compared between the hazards predicted by APFMH method and the hazards predicted by FMEA method; the total participants’ working hours invested in each process and the adverse events, categorized as ‘patient identification’, before and after the recommendations resulted from the above processes were implemented. Results APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA: the former used 21 h whereas the latter required 63 h. Following the implementation of the recommendations, the adverse events decreased by 44% annually (P = 0.0026). Most adverse events were preventable, had all recommendations been fully implemented. Conclusion In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA. APFMH is suggested as an alternative to FMEA since it is leaner in time and human resources, ensures more complete hazard identification and is especially valuable during crisis time, when new protocols are often adopted, such as in the current days of the COVID-19 pandemic.

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Predictive olfactory learning in Drosophila

Scientific Reports ◽

10.1038/s41598-021-85841-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chang Zhao ◽

Yves F. Widmer ◽

Sören Diegelmann ◽

Mihai A. Petrovici ◽

Simon G. Sprecher ◽

...

Keyword(s):

Synaptic Plasticity ◽

Dopaminergic Neurons ◽

Mushroom Body ◽

Trace Conditioning ◽

Fruit Fly ◽

Olfactory Learning ◽

Shock Strength ◽

Behavioral Experiments ◽

Kenyon Cells ◽

Output Neurons

AbstractOlfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.

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