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ObjectiveIn the present study, we aimed to assess the cognition of post-COVID-19 condition (PCC) participants in relation to their subjective sleep quality (Pittsburgh Sleep Quality Index, PSQI) and to analyse possible moderators of this effect, such as quality of life (European Quality of Life-5 Dimensions, EQ-5D), fatigue (Chadler Fatigue Questionnaire, CFQ), cognitive reserve (Cognitive Reserve Questionnaire, CRC), and subjective cognitive complaints (Memory Failures of Everyday Questionnaire, MFE-30).MethodsWe included 373 individuals with PCC and 126 healthy controls (HCs) from the NAUTILUS Project (NCT05307549 and NCT05307575) who were assessed with a comprehensive neuropsychological battery and various questionnaires.ResultsWe found that PCC participants with poor sleep quality had a 4.3% greater risk of immediate verbal memory deficits than those with good sleep quality, as indicated by the greater odds ratio (OR) of 1.043 and confidence interval (CI) of 1.023–1.063. Additionally, their risk of immediate verbal memory disorders was multiplied by 2.4 when their EQ-5D score was low (OR 0.33; CI 0.145–0.748), and they had a lower risk of delayed visual memory deficits with a greater CRC (OR 0.963; CI 0.929–0.999). With respect to processing speed, PCC participants with poor sleep quality had a 6.7% greater risk of deficits as the MFE increased (OR 1.059; CI 1.024–1.096), and the risk of slowed processing speed tripled with a lower EQ-5D (OR 0.021; CI 0.003–0.141).ConclusionThese results indicate that poor subjective sleep quality is a potential trigger for cognitive deficits. Therapeutic strategies to maximize sleep quality could include reducing sleep disturbances and perhaps cognitive impairment in PCC individuals.

Knowing the number of individuals in a population is fundamental for the sustainable management of exploited marine resources but estimating this parameter is often extremely challenging, especially in large, highly mobile and dispersed populations. Abundance estimation traditionally relies on multiple data types that include the relationship between fishery catches and effort (Catch Per Unit Effort or CPUE), scientific research surveys and demographic models that are developed to estimate past and current stock dynamics, but uncertainty is often high. Close-kin mark-recapture (CKMR) is an alternative method for estimating abundance and other demographic parameters (e.g. population trend, survival rates, connectivity), using kinship relationships determined from genetic samples. This methodology is based on a simple concept - the larger the population the less likely to find relatives and vice versa - and was proposed two decades ago although regained considerable attention recently. Refinements in the statistical methodology and advances in high throughput sequencing methods have boosted the efficiency of genomic analysis, promising to revolutionize the field of fisheries stock assessments. In theory, it can be applied to almost any species, provided that there is sufficient information about the life-history/biology of the organism and that the populations are not so small as to be almost extinct or so large that finding relatives becomes extremely difficult. Thus, it has the potential to provide baseline data for the many exploited fish stocks that remain largely unassessed and to reduce uncertainty in those that are regularly evaluated. Here, we provide an overview of the method in the context of fisheries assessments, analyze the advances and synthetize the field studies published in the last five years. Moreover, we evaluate the readiness, viability and maturity of the method to infer demographic parameters in species spanning diverse life histories. We evaluate technical considerations and requirements for a successful application and analyze the main challenges and limitations preventing a broader implementation.

Stock assessments serve to monitor the condition of fish stocks and exploit them sustainably but require accurate data such as growth and mortality rates as input parameters. Most species fished worldwide lack the data needed to assess their status and even those closely assessed are often based on parameters that are known to contain uncertainty. This has resulted in an increased share of overfished stocks over the last half century, demanding urgently innovative methodologies that can provide novel means to reduce uncertainty of fish stocks assessments and expand the range of assessed species. CKMR has emerged recently attracting a great interest due to its potential to provide accurate demographic parameters of interest in stock assessments. The method is at the crossroads between fisheries science and genomics, requiring specialized knowledge that is usually outside of the experience of fisheries scientist and modellers, complicating the application of the method and its uptake in regular fisheries assessments. In this review, we provide useful information to perform the genomics and bioinformatics steps required to complete successfully a CKMR study. We discuss the most suitable genomics assays, considering the amount of information they provide, their easiness of use and cost of genotyping accurately the large number of individuals needed to assess most fish stocks. We provide an overview of methods of analysis and statistical methodologies that can be used to infer kinship with the accuracy required in a large population setting with sparse sampling, where most individuals are unrelated, determining a low probability of finding closely related individuals. We analyse potential sources of biases and errors and provide recommendations to facilitate the application of CKMR to a wider range of fish stocks.

An increasing number of studies using marine environmental DNA (eDNA) approaches are showing its potential application in marine fisheries management by helping and simplifying some of the labor-intensive traditional surveys required to assess exploited populations and ecosystem status. eDNA approaches (i.e. metabarcoding and targeted) can support to ecosystem-based fisheries management by providing information on species composition; surveillance of invasive, rare and/or endangered species; and providing estimates of species abundance. Due to these potential uses in fisheries and conservation sciences, the number of studies applying eDNA approaches in marine habitats has expanded in the very last few years. However, a lack of consistency across studies when applying pipelines for data analyses, makes results difficult to compare among them. Such lack of consistency is partially caused by poor knowledge in the management of raw sequences data, and analytical methods allowing comparative results. Hence, we review here the essential steps of eDNA data processing and analyses to get sound, reproducible, and comparable results, providing a set of bioinformatics tools useful for each step. Altogether this review presents the state of the art of eDNA data analyses towards a comprehensive application in fisheries management promoting sustainability.

The assessment of fish stocks is often dependent on scientific trawl fisheries surveys, which are both invasive and costly. The analysis of environmental DNA (eDNA) from water samples is regarded as a non-invasive and cost-effective alternative, but meaningful performance evaluations are required for a wider application. The goal of this study was to comparatively analyze a newly developed, more sensitive real-time PCR based eDNA approach with bottom trawl fisheries catches to locally detect and quantify Atlantic cod (Gadus morhua) in the North and Baltic Seas. With a species-specificity of the qPCR assay of 100%, a minimal limit of 15 Cytochrome b eDNA copies was determined for the detection of cod. In addition, a Gaussian processing regression proved a significant correlation (95%) between eDNA (copies per L of water) and cod biomass (CPUE/Ha) found by bottom trawling. The results presented here prove the potential of eDNA analyses for quantitative assessments of commercial fish stocks in the open ocean, although additional comparative analyses are needed to demonstrate its performance under different oceanographic conditions.

Synthetic biomimetic prototissues with reduced complexity can facilitate the understanding of intricate biological processes, by allowing the role of specific physical or chemical mechanisms to be isolated. The aim of the present work is to provide a rheological description of vesicle prototissues as a biomimetic model for the flow of cellular tissues, which can be relevant for the mechanical comprehension of embryogenesis or tumor metastasis. Prototissue were obtained by the controlled assembly of Giant Unilamellar Vesicles (GUVs) mediated by the biotin-streptavidin pair, using a simple assembly protocol. Prototissues were mechanically probed in a “pipette-aspiration” inspired microfluidic chip, under controlled pressure conditions. A viscoelastic flow behavior was obtained which was well captured by a generalized Kelvin-Voigt fluid model, with inferred rheological parameters that did not show a significant dependence on the GUV-GUV adhesion strength. In addition, the flow of the vesicle prototissues exhibited a strain-stiffening behavior. Complementary flow velocimetry analysis revealed a decrease of prototissue effective permeability with the applied pressure, and enabled to identify vesicle spatial reorganizations taking place within the prototissue. Overall, our microfluidic setup makes possible the simultaneous characterization of the biomimetic prototissue at two different length scales, global and local, bridging the viscoelastic response of the overall prototissue with its structural changes between an ensemble of vesicles.

One of the most prevalent symptoms of post-COVID condition is cognitive impairment, which results in a significant degree of disability and low quality of life. In studies with large sample sizes, attention, memory, and executive function were reported as long-term cognitive symptoms. This study aims to describe cognitive dysfunction in large post-COVID condition individuals, compare objective neuropsychological performance in those post-COVID condition individuals with and without cognitive complaints, and identify short cognitive exams that can differentiate individuals with post-COVID symptoms from controls. To address these aims, the Nautilus project was started in June 2021. During the first year, we collected 428 participants’ data, including 319 post-COVID and 109 healthy controls (18–65 years old) from those who underwent a comprehensive neuropsychological battery for cognitive assessment. Scores on tests assessing global cognition, learning and long-term memory, processing speed, language and executive functions were significantly worse in the post-COVID condition group than in healthy controls. Montreal Cognitive Assessment, digit symbol test, and phonetic verbal fluency were significant in the binomial logistic regression model and could effectively distinguish patients from controls with good overall sensitivity and accuracy. Neuropsychological test results did not differ between those with and without cognitive complaints. Our research suggests that patients with post-COVID conditions experience significant cognitive impairment and that routine tests like the Montreal Cognitive Assessment, digit symbol, and phonetic verbal fluency test might identify cognitive impairment. Thus, the administration of these tests would be helpful for all patients with post-COVID-19 symptoms, regardless of whether cognitive complaints are present or absent.Study registrationwww.ClinicalTrials.gov, identifiers NCT05307549 and NCT05307575.

Abnormal regulation of DNA methylation and its readers has been associated with a wide range of cellular dysfunction. Disruption of the normal function of DNA methylation readers contributes to cancer progression, neurodevelopmental disorders, autoimmune disease and other pathologies. One reader of DNA methylation known to be especially important is MeCP2. It acts a bridge and connects DNA methylation with histone modifications and regulates many gene targets contributing to various diseases; however, much remains unknown about how it contributes to cancer malignancy. We and others previously described novel MeCP2 post-translational regulation. We set out to test the hypothesis that MeCP2 would regulate novel genes linked with tumorigenesis and that MeCP2 is subject to additional post-translational regulation not previously identified. Herein we report novel genes bound and regulated by MeCP2 through MeCP2 ChIP-seq and RNA-seq analyses in two breast cancer cell lines representing different breast cancer subtypes. Through genomics analyses, we localize MeCP2 to novel gene targets and further define the full range of gene targets within breast cancer cell lines. We also further examine the scope of clinical and pre-clinical lysine deacetylase inhibitors (KDACi) that regulate MeCP2 post-translationally. Through proteomics analyses, we identify many additional novel acetylation sites, nine of which are mutated in Rett Syndrome. Our study provides important new insight into downstream targets of MeCP2 and provide the first comprehensive map of novel sites of acetylation associated with both pre-clinical and FDA-approved KDACi used in the clinic. This report examines a critical reader of DNA methylation and has important implications for understanding MeCP2 regulation in cancer models and identifying novel molecular targets associated with epigenetic therapies.

Very little is known about the composition and the annual cycle of zooplankton assemblages in the Red Sea, a confined water body characterized by a high biodiversity and endemism but at the same time one of the most understudied areas in the world in terms of marine biodiversity. This high diversity together with the lack of references for several of the groups poses a problem in obtaining basic information on zooplankton seasonal patterns. In the present work, we used high throughput sequencing to examine the temporal and spatial distribution of the zooplankton communities inhabiting the epipelagic zone in the central Red Sea. The analysis of zooplankton assemblages collected at two sites—coastal and offshore—twice a month at several depth strata by using MANTA, Bongo and WP2 nets provides baseline information of the seasonal patterns of the zooplankton community over 1 year. We show that the seasonal fluctuation of zooplankton communities living in the upper 100 m of the water column is driven mainly by the annual changes in seawater temperature. The 18S rRNA gene was used for metabarcoding of zooplankton assemblages revealing 630 metazoan OTUs (97% similarity) in five phyla, highlighting the richness of the Red Sea community. During colder months, communities were characterized by lower richness and higher biomass than communities found during the hot season. Throughout the year the zooplankton communities were dominated by the class Maxillopoda, mainly represented by copepods and class Hydrozoa. The rise in the water temperature favors the appearance of classes Malacostraca, Cephalopoda, Gastropoda, and Saggitoidea. The present study provides essential baseline information for future monitoring and improves our knowledge of the marine ecosystem in the Red Sea while reporting the main environmental variable structuring zooplankton assemblages in this region.