Autophagy enhances memory erasure through synaptic destabilization

There is substantial interest in memory reconsolidation as a target for the treatment of anxiety disorders, such as post-traumatic stress disorder. However, its applicability is restricted by reconsolidation-resistant boundary conditions that constrain the initial memory destabilization. In this study, we investigated whether the induction of synaptic protein degradation through autophagy modulation, a major protein degradation pathway, can enhance memory destabilization upon retrieval and whether it can be used to overcome these conditions. Here, using male mice in an auditory fear reconsolidation model, we showed that autophagy contributes to memory destabilization and its induction can be used to enhance erasure of a reconsolidation-resistant auditory fear memory that depended on AMPAR endocytosis. Using male mice in a contextual fear reconsolidation model, autophagy induction in the amygdala or in the hippocampus enhanced fear or contextual memory destabilization, respectively. The latter correlated with AMPAR degradation in the spines of the contextual memory-ensemble cells. Using male rats in an in vivo LTP reconsolidation model, autophagy induction enhanced synaptic destabilization in an NMDAR-dependent manner. These data indicate that induction of synaptic protein degradation can enhance both synaptic and memory destabilization upon reactivation and that autophagy inducers have the potential to be used as a therapeutic tool in the treatment of anxiety disorders

Synapse-specific representation of the identity of overlapping memory engrams

Memories are integrated into interconnected networks; nevertheless, each memory has its own identity. How the brain defines specific memory identity out of intermingled memories stored in a shared cell ensemble has remained elusive. We found that after complete retrograde amnesia of auditory fear conditioning in mice, optogenetic stimulation of the auditory inputs to the lateral amygdala failed to induce memory recall, implying that the memory engram no longer existed in that circuit. Complete amnesia of a given fear memory did not affect another linked fear memory encoded in the shared ensemble. Optogenetic potentiation or depotentiation of the plasticity at synapses specific to one memory affected the recall of only that memory. Thus, the sharing of engram cells underlies the linkage between memories, whereas synapse-specific plasticity guarantees the identity and storage of individual memories

Autophagy enhances memory erasure through synaptic destabilization

There is substantial interest in memory reconsolidation as a target for the treatment of anxiety disorders, such as post-traumatic stress disorder. However, its applicability is restricted by reconsolidation-resistant boundary conditions that constrain the initial memory destabilization. In this study, we investigated whether the induction of synaptic protein degradation through autophagy modulation, a major protein degradation pathway, can enhance memory destabilization upon retrieval and whether it can be used to overcome these conditions. Here, using male mice in an auditory fear reconsolidation model, we showed that autophagy contributes to memory destabilization and its induction can be used to enhance erasure of a reconsolidation-resistant auditory fear memory that depended on AMPAR endocytosis. Using male mice in a contextual fear reconsolidation model, autophagy induction in the amygdala or in the hippocampus enhanced fear or contextual memory destabilization, respectively. The latter correlated with AMPAR degradation in the spines of the contextual memory-ensemble cells. Using male rats in an in vivo LTP reconsolidation model, autophagy induction enhanced synaptic destabilization in an NMDAR-dependent manner. These data indicate that induction of synaptic protein degradation can enhance both synaptic and memory destabilization upon reactivation and that autophagy inducers have the potential to be used as a therapeutic tool in the treatment of anxiety disorders

Synapse-specific representation of the identity of overlapping memory engrams

Memories are integrated into interconnected networks; nevertheless, each memory has its own identity. How the brain defines specific memory identity out of intermingled memories stored in a shared cell ensemble has remained elusive. We found that after complete retrograde amnesia of auditory fear conditioning in mice, optogenetic stimulation of the auditory inputs to the lateral amygdala failed to induce memory recall, implying that the memory engram no longer existed in that circuit. Complete amnesia of a given fear memory did not affect another linked fear memory encoded in the shared ensemble. Optogenetic potentiation or depotentiation of the plasticity at synapses specific to one memory affected the recall of only that memory. Thus, the sharing of engram cells underlies the linkage between memories, whereas synapse-specific plasticity guarantees the identity and storage of individual memories

Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening

The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with presymptomatic, symptomatic, and asymptomatic infections, the reopening of societies and the control of virus spread will be facilitated by robust population screening, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are too low to detect, followed by exponential viral growth, leading to peak viral load and infectiousness and ending with declining titers and clearance. Given the pattern of viral load kinetics, we model the effectiveness of repeated population screening considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective screening depends largely on frequency of testing and speed of reporting and is only marginally improved by high test sensitivity. We therefore conclude that screening should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary. &nbsp;</p

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10&nbsp;years; 78.2% included were male with a median age of 37&nbsp;years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Acoustic Event Detection: Feature, Evaluation and Dataset Design

It takes more time to think of a silent scene, action or event than finding one that emanates sound. Not only speaking or playing music but almost everything that happens is accompanied with or results in one or more sounds mixed together. This makes acoustic event detection (AED) one of the most researched topics in audio signal processing nowadays and it will probably not see a decline anywhere in the near future. This is due to the thirst for understanding and digitally abstracting more and more events in life via the enormous amount of recorded audio through thousands of applications in our daily routine. But it is also a result of two intrinsic properties of audio: it doesn't need a direct sight to be perceived and is less intrusive to record when compared to image or video. Many applications such as context-based indexing, health monitoring and smart environments, profit from the techniques developed for AED and results are still far from perfect. For instance, automatic music transcription (AMT) usually needs some corrections by expert musicians, and voice-controlled applications often require you to repeat a voice command or a word to your mobile phone before being understood. This is due to the challenging nature of the AED task. It is more than classifying one event into one of a predefined set of classes as it is pointing out a target event from anything else. In this thesis we focus on two AED applications which, at first sight, seem to be coming from two different worlds. The first is note onset detection (NOD), an atomic component for many music applications like fingerprinting for search engines and recommender systems, digital effects or simply AMT. From its name, the target events of NOD are the starting of musical notes. For the second application, howling detection (HD), the target is more of an artifact than a desired enjoyable event as howling is that sort of beep that shows up when a closed feedback loop is created between a microphone and a loudspeaker which occurs frequently in public address (PA) systems and hearing aids (HA). A HD algorithm is expected to produce some sort of activation function signaling the resonant frequency as soon as the howling starts in order to automatically filter it out. Surprisingly, both events share a specific time-frequency pattern, hence the key idea behind the spectral sparsity feature suggested in this work. After introducing AED in Part I, inspired from the work done for NOD, a general 3-step processing scheme is sketched out for detection of pattern-specific events in audio signals. This is followed by a summary of the state-of-the-art methods used for each of the steps. Part I ends by comparing the different metrics and techniques traditionally used for NOD and HD performance evaluation pointing out how unsuitable they are to handle the imbalanced nature of the datasets used in both problems. Moreover, it suggests a framework for a more fair evaluation and more generalized results using precision-recall (PR) curves and k-fold cross-validation scores. The two main parts of the thesis reside in Parts II and III, discussing the challenges and suggesting possible solutions for the two applications of interest, NOD and HD, respectively. The contributions for each can be divided into three groups following from the problems' solution steps: feature design, annotated dataset generation and evaluation enhancement. A feature based on spectral sparsity with two flavors, normalised identification of note onset based on spectral sparsity (NINOS2) and NINOS2-Transposed (NINOS2-T), is suggested for respectively detecting note onsets and howling frequencies. When tested on a dataset of synthetically mixed musical note onsets, NINOS2 outperformed the state-of-the-art NOD feature, Logarithmic Spectral Flux (LSF), for the sustained-strings instruments, pushing the F1-score to cross the 50 % border. This group of pitched non-percussive instruments is quite challenging as they have softer onsets, i.e., slowly building-up transients. A novel pre-processing step preceding the application of the NINOS detection function, is found to contribute to the performance increase. The pre-processing consists in retaining a subset of frequencies traditionally neglected but found here to be tightly related to onsets. For HD, NINOS2-T marked a higher average area under the PR curve (PR-AUC) than all the standalone HD features found in literature, for both music and speech examples. The performance of NINOS2-T remained the highest when restricting the evaluation to early howling detection. Existing datasets for both problems are relatively limited in terms of quantity and quality. For NOD, the available datasets are mainly manually annotated by two or more experts, limiting their availability due to the expensive annotation process. Moreover the annotation is subjective and note-context dependent. A similar situation exists for HD where datasets are made of recorded and manually annotated howling or sometimes poorly simulated by sinusoidal superposition. Part II starts by introducing a MATLAB tool "Mix-Notes" which is developed for generating automatically annotated NOD datasets. In Part III, a large HD dataset is created by simulating a closed-loop system, using several acoustic impulse responses (AIRs) to cover a wide range of howling frequencies, and applying the simulated system to different music and speech input files. On top of using those datasets for testing the suggested NOD and HD features, a different NOD experiment is carried out in which a real NOD dataset is augmented using a semi-synthetic dataset, created using the "Mix-Notes" tool, for training a state-of-the-art data-driven Convolutional Neural Network (CNN) model. This is done to overcome the limited availability of annotated real datasets. When running the experiment on piano excerpts, using two different augmentation strategies, preliminary results show better and more stable performance. To ensure a fair NOD evaluation, a novel parameter, the overall time shift in annotations, is proposed in Part II. While consistently lacking in literature when reporting F1-scores, this parameter is found crucial for making results comparable for different datasets and algorithms. The best-case F1-score can vary drastically when including this overall time shift in annotations parameter and it is found beneficial to use this parameter as a tunable hyperparameter when training a deep data-driven model on datasets that are annotated differently. The performance of HD features is traditionally compared for a subset of howling candidates using the receiver operating characteristic (ROC) metric. The use of howling candidates is intended to differentiate between howling and signal components and results in a fairly well balanced dataset, yet it excludes the detection of early howling and ringing. To overcome this limitation, in Part III, we suggest a novel HD approach considering all frequency bins as howling candidates. Since this yields a highly imbalanced dataset, for which ROC evaluation has been proven unsuitable, we propose to use the PR and PR-AUC evaluation metrics instead. Moreover, the PR assessment used a grid of equidistant thresholds in order to evaluate the HD feature robustness to threshold variations. While searching for answers to the different NOD and HD problems, questions never stopped popping up. Part IV revisits some learned lessons, discusses various open questions and suggests some future steps for further research in the presented topics.status: publishe

Note onset detection using sparse over-complete representation of musical signals

status: publishe