Structure and Function of the Hair Cell Ribbon Synapse

Faithful information transfer at the hair cell afferent synapse requires synaptic transmission to be both reliable and temporally precise. The release of neurotransmitter must exhibit both rapid on and off kinetics to accurately follow acoustic stimuli with a periodicity of 1 ms or less. To ensure such remarkable temporal fidelity, the cochlear hair cell afferent synapse undoubtedly relies on unique cellular and molecular specializations. While the electron microscopy hallmark of the hair cell afferent synapse — the electron-dense synaptic ribbon or synaptic body — has been recognized for decades, dissection of the synapse’s molecular make-up has only just begun. Recent cell physiology studies have added important insights into the synaptic mechanisms underlying fidelity and reliability of sound coding. The presence of the synaptic ribbon links afferent synapses of cochlear and vestibular hair cells to photoreceptors and bipolar neurons of the retina. This review focuses on major advances in understanding the hair cell afferent synapse molecular anatomy and function that have been achieved during the past years

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

S,C)-Dense Coding: An optimized compression code for natural language text databases

Abstract. This work presents (s, c)-Dense Code, a new method for compressing natural language texts. This technique is a generalization of a previous compression technique called End-Tagged Dense Code that obtains better compression ratio as well as a simpler and faster encoding than Tagged Huffman. At the same time, (s, c)-Dense Code is a prefix code that maintains the most interesting features of Tagged Huffman Code with respect to direct search on the compressed text. (s, c)-Dense Coding retains all the efficiency and simplicity of Tagged Huffman, and improves its compression ratios. We formally describe the (s, c)-Dense Code and show how to compute the parameters s and c that optimize the compression for a specific corpus. Our empirical results show that (s, c)-Dense Code improves End-Tagged Dense Code and Tagged Huffman Code, and reaches only 0.5 % overhead over plain Huffman Code.

Analyzing word frequencies in large text corpora using inter-arrival times and bootstrapping

Comparing frequency counts over texts or corpora is an important task in many applications and scientific disciplines. Given a text corpus, we want to test a hypothesis, such as “word X is frequent”, “word X has become more frequent over time”, or “word X is more frequent in male than in female speech”. For this purpose we need a null model of word frequencies. The commonly used bag-of-words model, which corresponds to a Bernoulli process with fixed parameter, does not account for any structure present in natural languages. Using this model for word frequencies results in large numbers of words being reported as unexpectedly frequent. We address how to take into account the inherent occurrence patterns of words in significance testing of word frequencies. Based on studies of words in two large corpora, we propose two methods for modeling word frequencies that both take into account the occurrence patterns of words and go beyond the bag-of-words assumption. The first method models word frequencies based on the spatial distribution of individual words in the language. The second method is based on bootstrapping and takes into account only word frequency at the text level. The proposed methods are compared to the current gold standard in a series of experiments on both corpora. We find that words obey different spatial patterns in the language, ranging from bursty to non-bursty/uniform, independent of their frequency, showing that the traditional approach leads to many false positives