Novel coronavirus species of public health and veterinary importance have emerged in the first two decades of the twenty-first century, with bats identified as natural hosts for progenitors of many coronaviruses. Targeted wildlife surveillance is needed to identify the factors involved in viral perpetuation within natural host populations, and drivers of interspecies transmission. We monitored a natural colony of Egyptian rousette bats at monthly intervals across two years to identify circulating coronaviruses, and to investigate shedding dynamics and viral maintenance within the colony. Three distinct lineages were detected, with different seasonal temporal excretion dynamics. For two lineages, the highest periods of coronavirus shedding were at the start of the year, when large numbers of bats were found in the colony. Highest peaks for a third lineage were observed towards the middle of the year. Among individual bat-level factors , only reproductive status showed significant effects on excretion probability, with reproductive adults having lower rates of detection, though factors were highly interdependent. Analysis of recaptured bats suggests that viral clearance may occur within one month. These findings may be implemented in the development of risk reduction strategies for potential zoonotic coronavirus transmission. These conditions result in more frequent contact between humans and animals (both domestic and wildlife species), increasing the rate at which zoonotic viruses spillover to new host populations3. The COVID-19 pandemic exemplifies emergence of new disease with global and lasting ramifications. This pandemic was preceded in recent history by the emergence of two other human coronaviruses, Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV)4. Research suggests that both emergent and endemic human coronaviruses may have originated in animal reservoirs (domestic and wildlife)9. Coronaviruses are able to adapt to new host species as a result of recombination, mutational errors during replication, and increased opportunities for transmission to new hosts12. The latter are largely driven by human activities, such as disruption of natural ecosystems with livestock grazing, agriculture, and wildlife hunting and trade3.Human population growth is accompanied by urban expansion, agricultural encroachment into natural landscapes, and increased pressure on husbandry practices11, with expanding species records being described for the Alpha- and Betacoronavirus genera14. A number of coronaviruses identified in bats were determined to be genetically similar to known human viruses , particularly among African bat species16. However, much of the identified viral diversity from African bat species remain unclassified14. Understanding of the disease ecology of bat coronaviruses that can be used to develop prevention and mitigation strategies to address the risk of spillover has been very limited globally25. Population size, density, roost type, and age composition (maturity) have been implicated as important factors involved in coronavirus prevalence and maintenance within bat populations25, though the impact of these factors on coronavirus maintenance are not in agreement by all studies. For example, though there has been a general consensus from research suggesting higher rates of coronavirus excretion from young bats, the involvement of reproductive activities (such as lactation or pregnancy) as a contributor to viral amplification or persistence within the colony is less certain20. Whether coronaviruses elicit significant effects to overall body condition has not universally been found to be true, and may depend on the coronavirus species under investigation20. Additionally, coronavirus maintenance in wild bats has mostly been described without differentiating among multiple but distinct coronaviruses that may be concurrently circulating within a population. Increased contact rates among dense host populations may also potentially result in a larger number of infected individuals, and may cause more viral shedding from a colony, increasing spillover risks. Understanding which factors are involved in viral maintenance within bat hosts and which drivers lead to viral amplification within a population are often based on assumptions concerning available literature derived from different viral families, host species, or ecologies. Context and virus-specific knowledge is essential in developing approaches to potentially disrupt transmission events.Large genetic diversities of coronaviruses have been reported from various families of bats, rodents and birdsRousetttus aegyptiacus (Egyptian rousette fruit bat) is an ecologically important fruit bat species that provides essential ecosystem services  with significant economic benefits27. Their range spans throughout sub-Saharan and Northern Africa, as well as the Middle East, South East Asia and the Western Palaearctic region28. Uniquely among fruit bats in Africa, these bats roost in caves and form large colonies  and often co-roost with insectivorous bat species. R. aegyptiacus have also been identified as hosts of several viruses, including Marburg virus, paramyxoviruses (Sosuga virus and henipa-related viruses), Lagos bat lyssavirus, adeno-, rota-, and influenza A viruses34. Bats from the Rousettus genus have been reported to host two species from the Nobecovirus subgenus (Betacoronavirus), with sequences of Rousettus bat coronavirus HKU9 (first reported from Asia) also described from R. aegyptiacus. HKU9 and genetically similar nobecoviruses have been highlighted as a coronavirus group to monitor for emergence due to possible spillover adaptation (from reported recombination and mutation), as they occur within multiple, widely distributed fruit bat species in the Old-World14. Using a spillover risk ranking tool, HKU9 and several similar African nobecoviruses have also been ranked among the top 40 viruses posing the greatest spillover risk35. Though these viruses are also present in African fruit bat species, their excretion dynamics within R. aegyptiacus populations have not been well described.R. aegyptiacus from a two-year longitudinal study of a maternity colony in South Africa. The colony is located in a cave that is also inhabited by several other bat species , and wildlife (Cape porcupines and African rock pythons); with large-spotted genets and vervet monkeys frequently caught on camera traps outside the cave entrance33. Moreover, livestock (cattle and goats) from a nearby rural settlement freely roam the area, and have been encountered in the cave. The cave was previously used for traditional and religious practices by the surrounding community, though people still enter the cave. We identified the circulation of distinct coronavirus lineages and characterize the temporal, population, and individual factors associated with infection.Here, we focused on investigating the excretion dynamics of coronaviruses circulating in a population of This study was conducted as part of a broader biosurveillance study targeting several zoonotic pathogens in bats in South Africa. Permission to conduct this research was approved by the Department of Agriculture, Land Reform and Rural Development, section  20 of the Animal Disease Act (Act No. 35 of 1984) under reference number 12/11/1/1/8. For sampling bats, provincial permits were obtained  as well as ethical approval from the following committees and performed according to approved protocols: the University of Pretoria’s Animal Research Ethics Committee, Faculty of Veterinary Science, Onderstepoort, Pretoria  and the University of Pretoria Research Ethics Committee, Faculty of Health Sciences, Gezina, Pretoria, South Africa and (REC 639/2018 and EC054-14). All methods were performed in accordance with the relevant guidelines and regulations as stipulated by the institutional ethical approval boards (conforming to ARRIVE guidelines).R. aegyptiacus maternity colony located at the Matlapitsi cave  bordering Lekgalameetse Nature Reserve in Limpopo Province, South Africa36. All sampling activities were performed with personal protective equipment (PPE), including Powered Air Purifying Respirators (PAPRs), coveralls, gum boots, double layer nitrile gloves and leather gloves. All equipment, PPE and sample containers were decontaminated with a 10% liquid bleach solution (5500 ppm hypochlorite solution).Surveillance for coronaviruses was conducted at the R. aegyptiacus as described in Mortlock et al.33. Sample types used in this study include rectal swabs from individual bats as well as pooled fecal samples collected inside the cave. Small sterile swabs  lubricated with 1× sterile PBS (Lonza) were inserted into the rectum and rotated twice to collect rectal swab samples and immediately frozen in a dry shipper (MVE Vapor Shippers). During the day, fresh fecal samples (non-desiccated and deposited within the last 24 h) were collected inside the cave from rock surfaces . Three fecal deposits  were pooled as one sample and immediately frozen in the dry shipper. Soft (pulpy) fecal samples derived from Rousettus were collected with certainty, as insectivorous species in the cave bats produce hard pellets (and do not intermingle in roosting spots with Rousettus). No studies have been performed on the age structure of the bats in the cave , though fecal deposits were collected underneath roosting bats throughout the cave. Pooled fecal samples were collected in all 24 months; rectal swabs were only collected in 16 months .Surveillance was performed monthly between June 2017 and May 2019. Bats were caught at the cave entrance upon emergence using Austbat three-bank harp traps (Faunatech), with traps kept open for five hours each sampling night. All animals captured during this period were sexed and recorded on data sheets. Only a subset of a maximum of 80 bats were sampled per month, whereby equal proportions of population demographics by sex and age were randomly taken for sample collection . During months when certain population cohorts were absent from the colony , numbers of other cohort groups sampled were increased. Samples  were collected from 38. ‘Subadults’ were categorized as free-flying independent young bats with a forearm length of below 89 mm that have yet to reach sexual maturity via aforementioned criteria38. Bats deemed reproductively active  were categorized as ‘adults’ regardless of forearm length. Pups attached to female bats were not sampled due to concerns of separation. Each sampled bat also received a unique tattoo for mark-recapture39. Bats recaptured more than once during the same sampling trip per month were released and not sampled again.A set of demographic and morphological measurements were taken from each individual bat including sex, reproductive status, forearm measurements and weight of the bat. Female bats were recorded as ‘reproductively active’ via the observation of pregnancy, lactation, or tapered/sclerotized nipples; non-reproductive females (nulliparous) possessed non-sclerotized nipples and determined to not be pregnant via palpation of the abdomen. Males were recorded as either being scrotal (reproductive) or non-scrotal (non-reproductive). Age was estimated using forearm lengths, with a cut-off of 89 mm and above to designate ‘adult’ batsAlpha- and Betacoronavirus specific hemi-nested RT-PCR targeting the RNA dependent RNA polymerase (RdRp) gene was used as a surveillance assay as described in Geldenhuys et al.40, yielding a nested amplicon of 268 bp. After analyses of nested products on a 1.5% agarose gel (Lonza), all products of appropriate size were excised and purified with the Zymoclean™ Gel DNA Recovery Kit (Zymo Research). Purified amplicons were prepared for sequencing with the BigDye Terminator v3.1 Cycle Sequencing Kit (Thermo Fisher Scientific) according to manufacturer’s recommendations for 10 μl reactions and purified with the ethanol/EDTA/sodium acetate precipitation method. Sequencing was performed on the ABI 3500xl at the DNA Sequencing facility of the University of Pretoria. Sequences were submitted to Genbank with accession numbers: MZ547450-MZ547649.RNA extraction and inactivation of samples were performed in a BSL3 laboratory, after which processing for nucleic acid testing was performed under BSL2 conditions. Fecal pools and rectal swabs were resuspended in 800 µl or 400 µl (respectively) 1× PBS (Lonza) and gently mixed. A total of 200 µl suspension was inactivated with an equal volume of 2× DNA/RNA shield solution (Zymo Research). Total RNA was extracted from inactivated material using the Quick-RNA™ Miniprep Plus Kit (Zymo Research). Complementary DNA was prepared as 20 μl randomly primed reactions using 100 ng random primers (Integrated DNA Technologies) and Superscript IV (Invitrogen) according to manufacturer’s recommendations. An in-house 41. Sequences of relevant coronavirus species and similar reference genomes identified from BLAST analyses of sequences from this study were collected from GenBank (NCBI). Estimations of pairwise similarities were performed with p-distance analyses in MEGA v742. Phylogenetic analyses were performed with Bayesian phylogenetics using BEAST v.1.10.443. CIPRES Science Gateway was used to run jModelTest2 and BEAST44. Maximum clade credibility trees were constructed with the general time reversible (GTR) model with gamma distribution and invariant sites. Bayesian MCMC chains were set to 25,000,000 states, sampling every 2500 steps, and convergence confirmed via an effective sample size (ESS) of > 200. Final trees were calculated in Tree Annotator with a 10% burn-in45. Trees were viewed and edited in Figtree v1.4.2.Sequence manipulations and alignments were performed with CLUSTALW in the BioEdit sequence alignment editor (v7.2.5)46. We included overall and annual cyclic time-varying splines to account for temporal variation. These and model intercepts were stratified by sample type. For rectal swabs where samples could be attributed to individual bats, we included hierarchical splines to test whether dynamics varied by the sex or age of the bats47. We included a penalized random effect term to test if reproductive condition category  affected probability of positivity in rectal swabs, as well as a nonlinear term for the effect of forearm mass index  as per Meng et al.48 As sex, age, and reproductive condition were correlated with FMI, it was normalized within these categories to reduce collinearity. All terms were included in a single model as the analytical goal was to test and compare the effects of multiple factors50.A multinomial generalized additive mixed model (GAMM) was used to jointly model the dynamics of all three viruses in both rectal swabs and pooled fecal samples51 in R 4.2.152, then used these estimates to initialize Metropolis–Hastings MCMC sampling of the model posterior. Spline wiggliness was penalized to reduce to linear effects in the absence of support for nonlinear effects. Convergence and efficiency of MCMC chains were checked using the R-hat and Effective Sample Size statistics53. Significance of nonlinear model terms was determined using Nychka criteria54, and reported model estimates and confidence intervals as means and 95% high-density intervals of posterior predictions. Both raw and model-estimated values of prevalence in pooled fecal samples were converted to individual-sample level using the Burrows estimator55, which corrects for upward bias in pooled sampling.The GAMM initially fit with the restricted maximum likelihood estimation method using the mgcv package40. This resulted in a combined 200 positive samples, 94 from rectal samples (raw detection rate 13.2%) and 106 from fecal pools . Model-estimated average (intercept) detection rate was 9.6% (CI 7.3–11.9%) for rectal swabs, and 3.3% (CI 2.5–4.1%) for fecal samples after correcting for pooling (Table S1). The detection rate among rectal swabs was not statistically different from that estimated for the overall uncorrected pooled fecal samples (Table S1). Datasets for samples containing coronavirus RNA are listed in supplementary dataset S1 and S2.A total of 720 fecal pools and 710 rectal swabs were tested for coronavirus nucleic acid with a conventional RT-PCR assayRousettus colony following sequencing of the PCR positive samples  identified in Asian bat species (Bat coronavirus HKU10 from Rousettus leschenaulti (NC_018871) and Hipposideros pomona (JQ989266)), sharing 78–79% nucleotide identities  were 94.9–100% identical at a nucleotide level (within the short-amplified region of the RdRp gene) and detected throughout the two years of surveillance. These sequences shared close similarities to members of the Rousettus bat coronavirus HKU9 species ; and between 94–98% identity to HKU9 strains from Asia59. The second betacoronavirus (RouNobeCoV) lineage identified only shared 74.2–76.7% identity to the detected HKU9-lineage sequences and 79.2–80.1% identity to an unclassified clade of coronaviruses originating from multiple African fruit bat host genera   with similarities of 95.7–96.2%.Two eage n = 45 were 9eage n = 45 were 9eage n = 45 were 9Coronavirus excretion varied considerably over time and between lineages, ranging from 0% of positive samples in monthly sampling events to over 40% of rectal swabs positive in April 2018, with smaller peaks observed in the first half of 2019 , where the roost is ‘re-colonized’62. Gestation lasts approximately four months with pups first seen around the end of October each year while peak parturition is seen in November–December63. The population is at its largest in summer/autumn at the start of the year (February-April), during which young bats are also becoming independent. At the start of winter, many bats in the population leave the maternity roost, with only a small population remaining in the roost over winter (June–August). These changes in colony populations are reflected in the demographic makeup of bats , with distinct timings of peak prevalence at different times of the year  except for rectal samples of the HKU9-related and RouAlphaCoV lineages. The wider range of estimates in rectal samples is attributable to the smaller number of positive detections. In addition, the HKU9-related lineage exhibited an overall interannual decline in positivity in fecal samples over the sampling period . Raw data prevalence estimates per demographic group showed that coronavirus RNA was more frequently detected in subadults than adults  Table . Only re48, and average FMI for adult bats was 13.6 kg/m2 (5.7–20.3) and an average of 11.6 kg/m2 (5.8–16.7) for subadults (Table S2). As FMI was strongly determined by other variables, especially reproductive condition .Forearm mass index (FMI) was used as a measure of body conditionion Fig. , it was Lastly, recapture data was used to compare detectable duration of viral excretion. For estimation of possible viral clearance or persistent infections, bats need to be frequently recaptured and tested for coronavirus RNA, preferably shortly after the initial detection of excretion. A total of 75 bats (10.6%) from the 710 individuals sampled were recaptured. Among the recaptured bats, RNA was detected from 20 individuals. In nine recaptured bats, coronavirus RNA was detected only at their last recapture indicating bats were infected since their first capture. The remaining 11 bats represented individuals from which viral RNA was detected at their first or second capture, though not at subsequent captures  than previous cross-sectional studies in R. aegyptiacus66. The study revealed the circulation of three lineages in the population within the sampling time frame, though no co-infections were identified. Co-circulation of alpha- and betacoronaviruses have previously been recorded for R. aegyptiacus and other fruit bats66 as well as other bat species10. By examining the excretion of the separate lineages, coronavirus excretion from the Rousettus population was determined to exhibit strong but distinct seasonality, with differences in timing between the viruses . These results  are within the range reported by other similar longitudinal studies in Asia (4.2–18.5%)es Figs. . The domFecal samples collected beneath roosting bats (pooled) serve as a simple non-invasive alternative to swabbing bats in the hand for determining coronavirus excretion from a colony, since they are easily collected with minimal disturbance to the colony. They also provide a larger volume of material to enable an additional number of molecular assays to be performed (such as genome characterization) or for viral culture. However, since fecal pools cannot be associated with individuals, inferences of how individual host traits influence excretion or timing among demographic groups are limited and are investigated via individual bat capture and sampling. Moreover, rectal swabs from individuals also enable estimation of viral clearance in recaptured bats.Higher coronavirus detection (from primary/raw data) was found among subadults than adults . However, reproductive condition, rather than age, was the only significant individual-level predictor of prevalence when all effects were considered jointly, with non-reproductive individuals (at the time of sampling) having higher prevalence than reproductively active individuals . This is likely because the interaction between age, sex, reproductive and body condition masks individual causal effects—subadults and non-reproductive adults are more alike in prevalence than reproductive adults. Targeted, more intense and longer sampling designed to disentangle these effects may in the future allow us to determine the role of reproductive stresses in driving the dynamics of these coronaviruses.48, and in both adult males and females, a higher FMI among reproductively active individuals is generally linked to increased resource allocation for reproduction (such as increased weight of the foetus/milk during gestation). Physiological stresses of reproduction have previously been suggested as risk factors increasing susceptibility of bats to possible infection of diverse viruses68. No significant evidence of FMI and prevalence associations were found among these three viral lineages from Rousettus at this time, and further data may enhance our ability to measure these effects.FMI may be considered a good measure of body mass quality among bats20, and multivariate analyses using generalized linear (mixed) models (GLMs and GLMMs)24 or general additive mixed models (GAMMs)23. In this study, a multinomial GAMM46 was used to jointly model the dynamics of multiple viral lineages in different sample types while avoiding multiple-testing challenges, and used this to examine the seasonal, interannual, and individual variation in viral excretion. Like other longitudinal studies, differences in coronavirus prevalence between age-classes was found in raw data, but under the scrutiny of multivariate analyses these effects could not be disentangled from other co-varying factors.Very limited longitudinal surveillance studies among specific bat populations have investigated the maintenance of bat coronaviruses. Moreover, there is little consistency in statistical frameworks for these studies, contributing to challenges in comparing results. Factors influencing coronavirus excretion have been reported based on simple significance testing on individual variables  between two or more groups)21. Such infection dynamics within maternity colonies has been suggested for various bat hosts and coronavirus species23, as well as other viruses such as Marburg virus and novel paramyxoviruses69. Serological information on the duration of maternal-antibody protection, general immunity as well as possible cross-protection of different bat coronaviruses is a key research area with very limited data. Alternative circulation strategies, such as those described for viruses like Nipah virus, include non-seasonal cycles in bat colonies, driven by immune dynamics and viral re-introduction/recrudescence rather than seasonal cycles70. Under multivariate analyses, the findings from this longitudinal study (over approximately 2 years) determined that individual-level effects, particularly of age, could not be separated from other co-varying factors. Reproductive condition was the only individual-level predictor of viral prevalence (and only for the HKU9-related lineage). It is possible that additional age-prevalence data from multiple seasons will allow for more precise investigation of the causal hypotheses.It has been suggested in literature that an increase in coronavirus circulation among young bats  occurs towards the end of the weaning period, presumably due to a gradual loss of maternally-derived antibody protection19 suggests a combination of both mechanisms may be contributing to population-level maintenance of bat coronaviruses. Intermittent shedding from persistent infections may cause pulses of viral amplification in the colony19, which can be monitored and allow continual re-seeding of the virus into the population. Moreover, the duration of bat coronavirus immunity for transient infections with clearance is currently unknown . Viral lineages would rely on reinfection events to prevent viral extinction. Due to limited evidence, most research focusing on the disease ecology of bat coronaviruses rely on certain general assumptions, including that the majority of bat coronaviruses likely follow the same general trend of host infection. Using recapture data representing 10% of the bats sampled, a preliminary timeframe for potential viral clearance could be estimated to occur within at least four weeks. This is comparable both in the percentage of recaptures and clearance estimations of two weeks to four months from other studies71. Though the results may appear to indicate transient infections, persistent infection with intermittent shedding cannot be ruled out based on the small number of recaptured bats in this study . As the results are based on the detection of viral nucleic acids, conclusions on whether identified nucleic acids represent excreted infectious viruses should be made with caution .Generally, the mode of coronavirus infection in bats remains largely unclear. Specifically, if persistent infections with either chronic or intermittent shedding occur, or whether acute, transient infections lead to viral clearance. Research by Jeong et al.72, and future studies will incorporate more approaches to estimating bat health status. Serological surveillance of coronaviruses in bats is severely lacking, largely due to limited biologics available for reliable assay development. This should be a priority research area to enable better understanding of coronavirus-associated immune protection or susceptibility. Bat models for experimental host-virus studies are also lacking, and may provide valuable data about infectious period, correlation between viral shedding and RNA detection, and viral transmission among individuals, that can be used to parameterize models of infection (and shedding) dynamics in wild populations.This research has shown that factors associated with coronavirus maintenance among bat populations may not be universal among coronavirus species within bat populations. Two years of monthly surveillance has provided an indication of excretion dynamics from the colony, though only continued sampling at this location may determine if the high excretion times are consistent and whether specific lineages may be outcompeted. In addition, full genome characterization of these viral lineages (currently underway and not the focus of this research paper) will also enable investigations of possible recombination between lineages. A limitation of the study includes sufficient sample sizes per demographic cohort  per month, due to the natural variation among the host populations at certain times of year. Improved stratified sampling designs to ensure sufficient sizes per available demographic group can shed more light on the drivers of viral circulation. Use of FMI as a metric of estimating body condition and health has advantages and disadvantages62, terrestrial wildlife, domestic animals, and people (in the cave and surrounding areas). This study served as the groundwork for coronavirus surveillance at this site, identifying the diversity within this species and leading to a basic understanding of viral excretion patterns during the year. Future research will continue monitoring the viral dynamics at the colony over time, examine co-infection between different viral families, cross-species transmissions to other bats in the cave, assess land-use changes potentially impacting risk of spillover to livestock and people, and expand surveillance to include other animals and human populations living in close association with this population of bats. Studies that assess viral dynamics in free-ranging host populations living in close association with people and domestic animals can provide useful insights into the risk of spillover and may help develop interventions to protect both human and animal health.Longitudinal surveillance studies, such as this study, provide valuable information regarding the circulation of viruses and active assessment of factors  that affect viral maintenance. The Matlapitisi colony is an ideal interface to investigate viral excretion dynamics within a maternity roost, as the bats in the colony interact with other bat speciesSupplementary Information.


The aim of this study was to investigate the link between self-reported health (SRH) and mortality in older adults. In total, 505 studies were found in PubMed and Scopus, of which 26 were included in this review. In total, 6 of the 26 studies included did not find any evidence of an association between SRH and mortality. Of the 21 studies that included community dwellers, 16 found a significant relationship between SRH and mortality. In total, 17 studies involved patients with no specific medical conditions; among these, 12 found a significant link between SRH and mortality. Among the studies in adults with specific medical conditions, eight showed a significant association between SRH and mortality. Among the 20 studies that definitely included people younger than 80 years, 14 found a significant association between SRH and mortality. Of the twenty-six studies, four examined short-term mortality; seven, medium-term mortality; and eighteen, long-term mortality. Among these, a significant association between SRH and mortality was found in 3, 7, and 12 studies, respectively. This study supports the existence of a significant relation between SRH and mortality. A better understanding of the components of SRH might help guide preventive health policies aimed at delaying mortality in the long term. Numerous studies have investigated the predictive value of self-reported health (SRH) on mortality or adverse health outcomes in both young and old adults ,2,3. OveIn this systematic review, we aimed to determine whether there is a significant link between SRH and mortality in older adults.Before launching the literature search, we ensured that no systematic review had previously been conducted on this specific topic and in this particular population, by means of verification in PubMed, Scopus, Prospero, and the Cochrane library.This was only a systematic review. A comprehensive literature search was performed in PubMed and Scopus. The search covered all publications up to and including 23 March 2022, with no specific start date specified. Search terms were defined by two senior researchers (L.G. and M.D.) and included the following keywords in the title and/or the abstract: (“obesity paradox” OR “reverse epidemiology” OR “body mass index”) AND   AND  AND (Age OR old OR elder*). Filters were applied to select studies in the English or French language and studies only including human subjects and to exclude the following publication types: reviews, case reports and case series, editorials, and correspondence. Reference lists were manually checked for additional studies. Study selection was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This study was registered with PROSPERO , under the number CRD42022329082.Study eligibility criteria were defined prior to performing the literature search by two senior researchers (L.G. and M.D.) according to the PICOS framework. Studies were eligible for inclusion if they reported data on self-rated health. The population of the studies included people aged 65 years or older, of any sex, ethnicity, or living place. The groups to be compared were defined according to their levels of self-rated health (SRH). The outcome was death, whatever the timepoint. Basic science articles, reviews, case reports and case series, editorials, and correspondence were excluded.www.covidence.org (accessed on 23 March 2022). After eliminating duplicates, two senior researchers (L.G. and M.D.) independently reviewed the titles and abstracts of all articles. In case of disagreement about whether or not to include an article, the case was discussed until consensus was reached. Overlap between studies in the results reported was checked. We independently extracted the data, using the same data extraction form. For descriptive analyses, the following data were extracted: publication year, country where the study was conducted, study design, study setting, medical condition (if any), sample size, and age . To analyse the relation between SRH and mortality, the following information was collected: outcome , type of analysis (whether adjusted or not), SRH levels, statistical estimates  and their respective 95% confidence intervals (95% CIs), and the level of significance .Data analysis was performed using Covidence systematic review software© , available at The quality of the included studies was assessed independently by two researchers (L.G. and M.D.) using the Newcastle–Ottawa Scale (NOS) . The NOSIn total, 505 studies were identified during the literature search . Among tThe main results of the included studies are summarized in The quality of the included studies, as assessed using the NOS, is summarized in In this systematic review of the predictive relationship between self-rated health (SRH) and mortality in people aged 65 years or over, we included 26 studies ,33,34,35For the two studies that included people aged 80 years or over, the authors showed a significant relationship between SRH and all-cause mortality at each timepoint . However, it seems difficult to extrapolate these results, as they all concern the same population, hospitalised via the emergency department for an acute condition.In the community-dwelling population, with a mean age of <80 years at inclusion, not selected for the presence of any specific pathology, our systematic review supports a predictive relationship between SRH and all-cause mortality at each time point. Of 17 articles ,30,32,33SRH is a composite concept that encompasses medical, social, cultural, religious, ethnical, and individual dimensions. Several authors have attempted to characterise the different dimensions of health under the term “SRH” ,38,39,40SRH is also associated with other factors known to predict outcome in the elderly population, such as interkeukin-6 (IL-6) . ArnbergThroughout life, including in the older population, the SRH seems to be a fairly accurate assessment of an individual’s functional capacities and even functional reserves for coping with the hazards of life, as evidenced by the predictive link with all-cause mortality demonstrated at all ages of adult life and at all timepoints. SRH is easily collected , even inThe methods used to collect SRH are variable. In our systematic review, some authors chose to assess the SRH on a value scale from excellent to very poor . Others chose to class SRH on a binary scale  versus ). Of the 11 authors who evaluated SRH on a binary scale, 9  found a predictive link between SRH and mortality in the short, medium, or long term ,28,30,31The predictive link between SRH and specific mortality in specific medical conditions seems to be more difficult to establish, because it is less well documented. In this systematic review, three articles investigated mortality linked to cancer ,15,16, aSRH seems to be a good criterion for assessing the risk of mortality in the short, medium, or long term in a population of elderly subjects living at home according to the articles studied in this systematic review. SRH assessment is complementary to so-called objective medical measures. SRH is simple to collect, which makes it easy to use for health professionals and acceptable to the population. Its composite nature makes it possible to take into account an individual’s health in a global manner.A better understanding of the components of SRH and their respective weight at each age might help to guide preventive health policies aimed at delaying mortality in the long term. However, there are currently no studies that have established that improving the criteria comprising SRH would make it possible to reduce mortality.Moreover, as the weight of each criterion seems to vary according to the individual and the age considered, targeted interventions may not be very effective. The composite nature of the SRH concept should encourage us to implement comprehensive prevention strategies from the outset, individualised and variable over time for greater effectiveness.Prevention strategies should be implemented early in the life of the individual and continue throughout life. The identification of poor SRH in a patient should prompt healthcare providers to promptly look for associated modifiable factors in an attempt to improve them.


Unprecedented discoveries during the past decade have unearthed the ubiquitous presence of biomolecular condensates (BCs) in diverse organisms and their involvement in a plethora of biological functions. A predominant number of BCs involve coacervation of RNA and proteins that demix from homogenous solutions by a process of phase separation well described by liquid-liquid phase separation (LLPS), which results in a phase with higher concentration and density from the bulk solution. BCs provide a simple and effective means to achieve reversible spatiotemporal control of cellular processes and adaptation to environmental stimuli in an energy-independent manner. The journey into the past of this phenomenon provides clues to the evolutionary origins of life itself. Here I assemble some current and historic discoveries on LLPS to contemplate whether BCs are extant biological hubs or evolving microcompartments. I conclude that BCs in biology could be extant as a phenomenon but are co-evolving as functionally and compositionally complex microcompartments in cells alongside the membrane-bound organelles. This Perspective discusses how biomolecular condensates are co-evolving as complex microcompartments alongside membrane-bound organelles in cells. The idea of the demixing of liquids occurring in biology was first postulated by Bugenberg de John and Kurt in 19294 and shortly after by Oparin in his book called The Origin of Life in 19385. However, it was not until eight decades later in 2009 that unequivocal evidence in support of LLPS and the formation of membraneless organelles (MLOs) in living cells was brought to light by Brangwynne and Hyman6. A cornucopia of equally significant discoveries following Brangwynne and Hyman’s in the last decade has now unearthed the near-ubiquitous involvement of MLOs in cellular processes across many organisms. Together, these discoveries have not only opened an unprecedented scientific quest to understand the mechanisms of spatiotemporal control in biological processes but also help glean the origins of life itself. MLOs are spatially separated mesoscale compartments devoid of a membrane barrier observed in membrane-bound organelles such as mitochondria or lysosomes. A variety of different cellular processes involve MLOs, which are also referred to as biomolecular condensates (BCs)7. In BCs, biomolecules such as proteins and nucleic acids coacervate to demix from the bulk solution by the process that is best described by LLPS (detailed below in the next section). One of the earliest discovered membraneless organelles in eukaryotes is the nucleolus within the nucleus, which is the location of ribosomal synthesis8. Since then, more than 20 MLOs have come to the limelight in mammalian cells, and the numbers are rising at a staggering rate10. The near ubiquity of the phenomenon in a wide range of cellular processes begs the question of whether the coacervation of proteins and RNA toward the formation of BCs and MLOs have come into existence by Darwinian evolution of natural selection for acquiring spatiotemporal control on biochemical processes or are they relics of cellular origins on earth. Given the prevalence and functional integration of this phenomenon in all kingdoms of life, it is important to dwell in some historical, as well as current research perspectives on biomolecular condensates and take a philosophical dive into the possible origins and evolution of BCs. which is likely to kindle intriguing thoughts about this phenomenon in the biological world.The phenomenon of liquid-liquid phase separation (LLPS) in aqueous solutions has been known for quite some time, especially in the fields of polymer science and process engineering13. For complex multi-component solutions containing two or more biopolymers, the ability of biopolymers containing opposite charges to engage in complex coacervation involving weak, transient, multivalent, and nonstoichiometric interactions comprising of, but not limited to, electrostatic, π-π or cation-π forces, will determine whether they undergo liquid-liquid demixing into two distinct phase regimes16. LLPS is a density transition with a concentration threshold called saturation concentration (Csat) above which the system undergoes LLPS. Csat defines the phase boundary of a particular system18. Although widely referred to as LLPS, many of the coacervating biomolecules both in vitro and in vivo show viscoelastic properties meaning that the condensates show liquid-like behavior as a function of time and length scales. However, numerous models developed for LLPS adequately describe the macroscopic behavior of the coacervating biomolecules and BCs, and therefore, one may note that within the biological timescales, BCs show liquid-like behavior. Nevertheless, in demixed state, the dense phase is enriched, and the dilute phase is depleted in biopolymers creating a concentration gradient across the phase boundary19. The formation of condensates depends on the mixture’s composition and other parameters such as temperature and ionic strength. In non-equilibrium states like those in the cells, the phase boundary can be shifted depending on the diffusion and fluxes of molecules and regulators. The demixed state can therefore be reversibly dissolved or formed based on the environmental cues. Demixing of liquids into inhomogeneous two or more component systems by LLPS-like process presents three fundamental advantages in cellular functions: a) It offers spatial control via compartmentalization and confinement of biomolecules without having to actively or passively transport them into specialized, membrane-bound organelles20, b) it enables to achieve an increase in the effective concentrations of coacervating molecules within the dense phase and thereby providing a temporal control over the rates of the reactions23, and c) MLOs thus formed as distinct heterogeneous phases can be diffused back to a single homogenous phase by a variety of different mechanisms depending on the environment and stimuli in an ATP-independent manner. These advantages predispose the cellular machinery to adopt phase separation as a cost-effective and need-based mechanism to optimize their functions.A homogeneous liquid containing a binary mixture of solute and solvent can exist in a fully miscible single phase or undergo LLPS into a demixed, inhomogeneous state containing two or more phases. If the energy of interactions between solute and solvent is greater than that of solute and solute, a single phase will exist but on the other hand, if the energy of solute-solute interactions is greater than that of the solute-solvent interactions, the system will demix and co-exist in two distinct liquid phases26. This remarkable discovery of the origins of life on Earth was made by systematically uncovering the complex cellular forms layer by layer to reveal the conserved molecules and machinery across the phylogenic tree. Discoveries such as self-splicing introns in the form of ribozymes27, and auto-regulatory riboswitches28 to name a few, showcase the versatility of RNA molecules. One of the important properties of RNA molecules is the ability self-replicate and a significant body of evidence points toward the evolution of RNA replicase, which is an RNA molecule that is capable of acting as a template for its own replication and the storage of genetic information, and as an RNA polymerase that is able to catalyze its own production29 Although RNAs are versatile molecules that are capable to replicate, catalyze and multiply, these molecules by themselves cannot accomplish more complex functions without achieving some level of selectivity and spatial compartmentalization.It is now well established that RNA molecules are the earliest- biomolecules formed on the planet and were able to sustain primordial life their ability to that catalyze their own self-replication30. If so, what were the methods by which such confinement of molecules especially with RNA was adopted? Evidence indicates that many such membraneless microcompartments could have existed in the prebiotic world in the form of liquid droplets made of organic molecules and oils31, silica-based inorganic microcells33, aqueous two-phase systems34, peptide coacervates35, polyester microdroplets from α-hydroxy acids30, and anhydride compartmentalization mechanisms34. Although it will be difficult to track the timeframes of origins for these membraneless microcompartments, parsimoniously one can conclude that they could have coevolved with membrane-bound protocells and compartments. Yet, one of the compelling arguments for the membraneless compartments to have evolved earlier than the membrane-bound ones is the simplicity by which the ions and other molecules can reversibly diffuse in and out of the droplets without spending valuable energy or having to engage active transporters to accomplish sequestration and confinement. In addition, the ease and reversibility of formation and dissolution provide a great deal of temporal and spatial control for the reactions within membraneless microcompartments. One of the pivotal transformations during the compartmentalization period of the prebiotic evolution in gaining spatiotemporal control of biological reactions was the evolution of RNA to protein or the coevolution of RNA-protein complexes, which likely followed the ‘RNA-only’ world39  that are membraneless nuclear hubs for mi-RNA biogenesis in plants55, in addition to those found in mammalian ones such as Cajal bodies, p-bodies, stress granules, etc.50 . The widespread prevalence of BCs across many organisms is probably the consequence of the physiochemical simplicity of the LLPS process which provides a distinctive advantage in sensing and adapting to environmental changes within the cellular milieu without having to expend precious energy. Based on this hypothesis, one could infer that BCs are extant. However, on the other hand, the prevalence of BCs could also suggest their co-evolution along with higher-order organisms containing membrane-bound organelles, as they continue to utilize LLPS and BCs to carry out a multitude of complex functions.Similarly, advanced single-molecule techniques and super high-resolution microscopy have made it possible to detect MLOs in microorganisms such as bacteria and fungi also. In bacteria, several MLOs, in the form of dense foci, are known to form namely BR-bodies, PopZ microdomains, RNA polymerase clusters, polyphosphate granules, etc. In 65. Based on sequence analysis, it is predicted that disordered proteins are significantly more prevalent in eukaryotes (33%) as compared to prokaryotes (4%)66, and thus many BCs and MLOs have been identified in mammalian cells.As detailed above, MLOs are present in many organisms across prokaryotes and eukaryotes and are involved in diverse functions. I will refocus our attention back on mammalian cells to bring out not only the ubiquity involved in the utility of phase separation in cellular functions but also the complexities of the condensates in terms of their composition, size, and biological functions. Although not a prerequisite, intrinsic disorder in proteins facilitates LLPS67. In addition to these ubiquitous MLOs, stimuli and condition-dependent ones such as stress granules, U-bodies, metabolic granules, and proteasome storage granules are also formed in various cells69  is where the rRNA synthesis begins and as the nascent pre-rRNA emerges, intrinsically disordered Gly-Arg-rich proteins coacervate with them to initiate the formation of the second layer of the nucleolus called the dense fibrillar component (DFC)71.Arguably nucleolus is the first MLO to be observed visually. Being the site of ribosome biogenesis, it is now clear that nucleolus is also a complex, multi-layer condensate containing three compartments of immiscible liquids with specific events of ribosome biogenesis taking place sequentially from the innermost layer to the outermost layer74. They are known to contain more than 25 different proteins, including ribonucleoproteins, those in translationally arrested pre-initiation complex with 40 S ribosomal unit, small RNAs, SG-associated initiation complexes, stress granule nucleators, etc., in addition to mRNA transcripts75. Proteomic and genetic screens have identified hundreds of proteins associated with stress granules, which require a well-orchestrated play involving physical and chemical forces that synchronize phase separation and one that can adapt to the environmental cues to provide cellular protection under stress. This multi-component, complex biomolecular condensate formation is a key integral part of stress-coping mechanisms in eukaryotic cells.These cytoplasmic MLOs are RNA-rich foci that are reversibly formed during cellular stress conditions. The critical role of the stress granules includes the control of translational regulation of mRNA during stress by sequestering the transcripts into the foci78. Recently, the transcriptional control of transcriptional factors and activating domains embryonic stem cell transcription factor OCT4, estrogen receptor, and yeast transcription factor, GCN4 form phase-separated condensate with other co-activating molecules for gene activation79. LLPS-like process has also been observed in key RNA polymerase II transcription80 and in chromatin regulation81. An increasing number of RNA-dependent transcriptional and translational condensate hubs have been discovered84, making the foci containing biomolecular condensates a key component for organizing and activating transcriptional machinery. Related to transcriptional control, the processing bodies or p-bodies are cytoplasmic hubs enriched in mRNA and also play key roles86.It has been well-established that transcriptional machinery involved many disordered proteins and disordered regions that provide the plasticity needed for long-range and multivalent interactions between the DNA, RNA, and activation factors88. Partitioning of enzymes in biomolecular condensates not only facilitates enzyme efficiency by providing increased effective concentrations but also by other mechanisms, such as changing conformations and specific activity23. An increasing number of reports in addition to these examples showcase the ability of enzymes to be efficient and specific by adopting LLPS-like mechanisms and thus help us glean the versatility of the phenomenon in many cellular aspects. In addition to these, BCs and MLOs are known to play a role in other cellular processes, including autophagy89, organizing synaptic density90, and immune response91.A high degree of specificity and selectivity are the key features of enzyme-catalyzed reactions, and therefore, one could assume that enzymes evolved late to carry out such specific reactions. However, since compartmentalization by phase-separated condensates provides higher effective concentrations, enzymes could take advantage of biomolecular condensate as a way of increasing their efficiency. This seems to be the case as many enzymes are known to exist as assemblies and in condensate states93. Proteins such as FUS94, TDP4395, and tau96 have been shown to form amyloid aggregates nucleating from the BCs and are implicated in pathologies such as amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Parkinson’s disease (PD)72. BCs have also been implicated in cancer97, and in SARS-Cov-2 viral infection98.One of the obvious consequences of spatial compartmentalization in BCs is the increase in protein concentration in the dense phase. Although this is critical for many physiological processes, BCs become a crucible for many proteins that are prone to form toxic amyloid aggregates103. In addition, the evolution of protein clusters and structural disorder and sequence low complexity also played a role in the evolution of BCs. However, it is also clear that during the early evolution period, LLPS-based compartmentalization also presented limitations on selectivity and specificity, which the membrane-bound organelles could offer. So there seems to be a trade-off between the energy-efficient, simpler compartmentalization by phase separation and the membrane-bound compartmentalization that offered greater selectivity and specificity but with energy-demanding processes, such as active transport, endo- or exocytosis. Therefore, it seems as if MLOs and membrane-bound organelles have co-evolved compartments instead of out-competing each other. The cells have adapted to utilize both methods of compartmentalization to optimize biological functions depending on the need. Although it would be difficult to assert whether or not BCs are an extant biological relic, or they are evolving alongside the increasing complexities of life forms, it would certainly not be inaccurate to conclude that BCs and MLOs are unlikely to get out-evolved by membrane-bound compartments. It seems as if a synergy has been achieved to utilize both mechanisms to cope and adapt to the increasing complexities of the biological world.The afore-described biophysical, spatial, and functional characteristics along with the prevalence of BCs and MLOs in organisms across eukaryotes, prokaryotes, and archaea seem to suggest that the phenomenon of phase separation resembling LLPS is an evolutionarily conserved mechanism for spatial compartmentalization since the primordial life on earth. The most important attribute of phase separation is its simplicity for compartmentalization and spatiotemporal control of coacervating components in a given system as opposed to membrane-bound organelles. LLPS-like process involving simpler two- or three-component systems could have prevailed during pre-biotic and early life processes, but the formation of BCs and MLOs in cellular life forms would have been challenging given the organismal complexity and multi-component systems in a crowded milieu. Yet as described above, widespread BCs are observed in eukaryotes often involved in complex cellular functions and comprising hundreds of components, in some cases containing multiple phases, in a dynamical non-equilibrium system. Ubiquity observed in such complex condensates suggests that cells may have adapted to embrace the tenets of soft matter physics to continue to utilize phenomenologically and energetically simplistic LLPS-like mechanisms rather than the requirement for membrane-bound specialized compartments Fig. . If one Peer Review File


Shot boundary detection is the process of identifying and locating the boundaries between individual shots in a video sequence. A shot is a continuous sequence of frames that are captured by a single camera, without any cuts or edits. Recent investigations have shown the effectiveness of the use of 3D convolutional networks to solve this task due to its high capacity to extract spatiotemporal features of the video and determine in which frame a transition or shot change occurs. When this task is used as part of a scene segmentation use case with the aim of improving the experience of viewing content from streaming platforms, the speed of segmentation is very important for live and near-live use cases such as start-over. The problem with models based on 3D convolutions is the large number of parameters that they entail. Standard 3D convolutions impose much higher CPU and memory requirements than do the same 2D operations. In this paper, we rely on depthwise separable convolutions to address the problem but with a scheme that significantly reduces the number of parameters. To compensate for the slight loss of performance, we analyze and propose the use of visual self-attention as a mechanism of improvement. Shot boundary detection is an important task in video processing and computer vision, where the goal is to automatically identify the boundaries between consecutive shots in a video sequence. One of the techniques used for shot boundary detection is 3D convolution. Three-dimensional convolution applies a filter kernel to a three-dimensional input volume, such as a video sequence. The kernel slides over the input volume in three dimensions, computing a dot product at each position. The result of this operation is a new three-dimensional volume, which can be used to detect shot boundaries. The input volume is typically composed of a stack of video frames, where each frame is represented as a two-dimensional image. In a simple way, a 3D convolution allows for extracting spatiotemporal features that are relevant for shot boundary detection, such as changes in color, texture, or motion. One common approach is to use a thresholding technique to separate regions of the volume that correspond to different shots. Other techniques, such as clustering, can also be used to segment the output volume and detect shot boundaries. The effectiveness of 3D convolution for shot boundary detection depends on how the convolutional layers are stacked, the type of convolution chosen, the number of filters utilized at each stage of the stacks, and how additional techniques help generalization such as batch normalization, pooling, and dropout are utilized.Depthwise convolution is a type of convolution operation used in convolutional neural networks (CNNs). In contrast to traditional convolution, which convolves an input with a set of learnable filters across all input channels, depthwise convolution applies a separate filter to each input channel.The operation involves sliding a 3D kernel over each channel of the input, performing an element-wise multiplication between the kernel and the corresponding channel, and summing the resulting values to produce a single output value for each position in the feature map. This process is repeated for each channel in the input, resulting in a set of intermediate feature maps. Depthwise convolution can be followed by a pointwise convolution, which applies a 1 × 1 × 1 convolution to combine the intermediate feature maps into a new set of features. This combination is also known as a depthwise separable convolution, and it is commonly used in architectures that require efficient computation, such as mobile or embedded devices. The advantages of depthwise convolution include reduced computation and memory requirements, as well as increased model efficiency. By reducing the number of parameters in the network, depthwise convolution can improve training speed and reduce overfitting. Depthwise convolution has been used in various architectures, such as MobileNet , to achiResidual visual attention refers to a mechanism that enhances the ability of a convolutional neural network (CNN) to focus on salient features in an image, while also maintaining the representation of the original input. This mechanism combines the concept of residual connections, which enable the network to learn the difference between the input and the output, with visual attention, which allows the network to selectively focus on relevant features. In a residual visual attention block, the input is passed through a series of convolutional layers, followed by a residual connection that adds the input back to the output of the convolutional layers. A visual attention mechanism is then applied to the output, which weights the importance of different spatial and temporal regions of the feature map based on their relevance to the task at hand. The visual attention mechanism can be implemented in different ways, such as using a spatial transformer network, which applies an affine transformation to the feature map to focus on a specific region of interest, or using a self-attention module, which computes attention weights based on the similarity between different feature map locations. By combining residual connections with visual attention, residual visual attention blocks allow the network to selectively focus on salient features, while still preserving the representation of the original input. This can lead to improved performance on tasks such as image classification, object detection, and segmentation. Residual visual attention has been used in various deep learning architectures, such as ResNet-SE , SENet , and SKN1.Modifying some state-of-the-art architectures such as Transnet  and Tran2.The usage of a residual self-attention block with attention map computation and the verification of the performance of the models.3.We provide comparative results, testing the performance of the models in three different sets of experiments. A first set with no depthwise convolutions, a second set with only depthwise convolutions, and a third set where we combine depthwise convolutions and residual attention blocks.Some novel aspects of our work include the following:The remaining of paper is organized as follows: Depthwise separable convolutions. Since the development of convolutional networks, many studies have been carried out aimed at reducing the number of parameters to optimize the computational load and improve the inference speed.Flattened convolutional neural networks are presented in . Those aFactorized Networks  introducSome Inception versions already explored ways to scale up networks with the aim of utilizing the added computation as efficiently as possible by suitable factorized convolutions and aggressive regularization. The inceptionV3 network  replacesThe Xception network  demonstrThe work in  focuses In , authorsRef.  proposesThe difference between separable and pseudo-3D CNNs is that in the latter, up to three types are defined depending on how the spatial and temporal convolutions are combined. The case of the pseudo block type A in  verticalSimilar to ,20, in , the autBased in , MobileNShuffleNet in  is anothMobileNetV2 in  is an imSqueezeNet in  is an arIn , shot deA second version of the network provided in  is preseSelf-attention modules. Attention modules can model long-range dependencies and have been widely applied in many tasks. In particular, the work [the work  is the fthe work  introducthe work  integratthe work , a residthe work , a very the work  is propoDifferent from previous works, we extend the self-attention mechanism in the task of shot boundary detection and carefully design several types of attention modules to capture rich contextual relationships for better feature representations with intra-class compactness. Comprehensive empirical results in The most straightforward way of improving the performance of deep neural networks is by increasing their size; this includes both increasing the depth (the number of levels) and its width (the number of units at each level). This is an easy and safe way of training higher quality models, especially given the ability of a large amount of labeled training data. However, this solution comes with two major drawbacks. A bigger size typically means a larger number of parameters, which makes the enlarged network more prone to overfitting, especially if the number of labeled examples in the training set is limited. Second, uniformly increased network size is the dramatically increased use of computational resources. The fundamental way of solving both issues would be by ultimately moving from fully connected to sparsely connected architectures, even inside the convolutions.Sparsely connected refers to a type of neural network architecture where not all neurons in a layer are connected to every neuron in the previous layer. In other words, some connections between neurons are omitted or removed, resulting in a sparse connectivity pattern. In contrast, a fully connected neural network architecture has every neuron in one layer connected to every neuron in the next layer. This results in a dense connectivity pattern where each neuron receives input from every neuron in the previous layer. Sparsely connected architectures can be beneficial because they can reduce the number of parameters in the model and improve computational efficiency, which can make training and inference faster and more resource-efficient. However, they can also be more difficult to train than fully connected architectures because of the reduced number of connections, and they may require specialized techniques to be implemented effectively.In the context of convolutional neural networks, sparsely connected architectures can be achieved by using techniques such as dilated convolutions, depthwise separable convolutions, and group convolutions. These techniques allow for more efficient and sparse connectivity patterns within the convolutions, which can lead to improved performance and efficiency. In previous works, Transnet  and TranWe propose the use of separable depthwise convolutions, such as those used in Mobilnet , as wellStandard 3D convolution. Similar to 2D convolution, 3D convolution applies a filter/kernel over the input data and calculates the dot product of the filter with each overlapping section of the input. The output of this operation is a new volume, where each element represents the dot product of the filter with the corresponding section of the input. In contrast to 2D convolution, where the input has two dimensions (width and height), 3D convolution operates on a 3D tensor with the dimensions depth, width, and height. The filter/kernel used in 3D convolution has a 3D shape and slides across the depth, width, and height dimensions of the input tensor. The output size of 3D convolution depends on the size of the input tensor, the size of the filter/kernel, and the stride used during the convolution operation.l, w, h, c), where l, w, h represents length, width, height and c denotes channels, the natural way of doing convolution operation on it would be using a filter with size k × k × k, where k is the side length of the filter, to go over the 3D matrix.Given a 3D feature matrix with shape  is:The computation cost would be:3D Depthwise Convolution. In 3D depthwise convolution, we decompose one 3D convolution operation into two steps: first, apply separate filters for each individual channel; second, stack the c feature maps obtained by the first step.x, y, z and i, j, k denote the spatial position of a voxel. K′ is a depthwise convolution kernel of size k × k × k × c (consisting of c filters). The m-th filter in K′ would be applied to the m-th channel in F. The output of the m-th filter becomes the m-th layer in G′.The output feature matrix for a 3D depthwise convolution is computed as:The number of parameters of a standard 3D convolution (excluding bias) is:The computation cost would be:3D Pointwise Convolution 3D pointwise convolution, also known as 1 × 1 × 1 convolution, is a type of convolutional operation that involves a filter/kernel with a size of 1 × 1 × 1 applied to the input tensor. In other words, 3D pointwise convolution operates on each individual voxel of the input tensor. Unlike regular 3D convolution, which uses a larger filter/kernel size to capture spatial information, pointwise 3D convolution is used to perform channel-wise operations on the input tensor. It is commonly used in deep learning models to increase or decrease the number of channels of a given tensor. It carries out a linear combination of layers of all depths. It is essentially fusing the split channels back together and activating the exchange of information adequately across channels. The number of parameters of a pointwise convolution is:The computation cost would be:If we combine a depthwise and a pointwise convolution, the computational cost is:We then divide with the computational cost of a standard 3D convolution to obtain the reduction factor in computation terms:k = 3, the reduction is about 0.44. As soon as we increase the value of Assuming channel size The effect of using a pointwise convolution after a depthwise convolution is that it helps to increase the expressiveness of the network while maintaining computational efficiency. The depthwise convolution captures spatial information from the input data, while the pointwise convolution combines this information and generates new features that can be used by the subsequent layers of the network. Using a pointwise convolution after a depthwise convolution helps to reduce overfitting in the network by reducing the number of parameters. This technique has been used in various successful neural network architectures, such as MobileNet  and ShufSelf-attention is a mechanism in deep learning and natural language processing that helps the model to focus on different parts of the input when making predictions. The key, query, and value concepts are fundamental components of self-attention. In self-attention, the input is transformed into three vectors: key, query, and value. The key vector represents the importance of each sample in the input, the query vector is used to retrieve information from the key vector, and the value contains the actual information. During self-attention, the query tensor is used to compute the similarity scores between itself and each of the key tensors. The similarity scores are then used as weights to compute a weighted sum of the value vectors. The weighted sum is the final output of the self-attention mechanism. In essence, the key, query, value concept allows the self-attention mechanism to identify the most relevant parts of the input (represented by the key vector), retrieve information from those parts (using the query vector), and use that information to produce the final output .k(x), q(x), and v(x) as follows:N is the batch dimension, C is the number of channels, HxW represents the spatial dimensions, and T the temporal dimension. We project x through the learnable query i location and all other j locations. The output of the attention layer is The key, query, and value are denoted by x is the feature map input to the residual self-attention block and We use Transnet  and TranWe experiment with two major architectural modifications: (a) incorporating the use of depthwise separable convolutions and (b) adding a residual self-attention block, in which the feature map can be temporal, spatial, channel, or multiple. Within the depthwise option, we experiment with two versions: using a shared filter for all channels (v1) and using a different filter per channel (v2). In the residual self-attention option, we analyze two implementations, using 3D convolutions (v1) and using 1D convolutions (v2).As in We investigate the effect of the attention mechanism depending on the axis used to generate the attention map see a, distinIn the case of the Transnetv2 option, we tested with two options when applying residual connections: (a) residual connection on a pointwise after a Depthwise Dilated layer, We employ two residual attention block architecture options, depending on whether we use 3D a or 1D b convoluDepthwisev{X}TransnetAttn{Type}v{Z}, for our DepthwiseTransnet with attention  is improved by the Depthwise version with channel attention (0.911) and TransnetV2 (0.905) with spatial attention (0.915). That is, the original results are improved with a fraction ×4 of the total parameters.In addition, and in order to test variants 1 and 2 of the attention blocks, in In this work, we present two new models for video shot boundary detection. Those are based on modified versions of Transnet  and TranWe have investigated two variants of the depthwise separable convolution. Version 1 employs only one filter for all input channels, while version two employs a different filter per channel. In general, version 2 is better than version 1 because it is not forced to extract features from a single shared filter for all channels. In the case of TransnetV2, one of the modifications we made is to incorporate a block called Res3DDwDCNN, which we investigated in two versions, A and B, differentiated in how a residual connection is constituted. We observed that option B is better than A because the residual connection is made directly on the 3DDwDCNN layer, using a pointwise convolution for dimension adjustment.In the case of the attention block, we investigate two versions, v1, and v2. Version v1 uses 3D convolutions, while v2 uses 1D convolutions. This leads to having to reform the tensors at different points in the block. We observe that version v2 works slightly better than v1 because with the use of pointwise convolutions, no reduction of the input tensor is performed, and therefore, the attention map is generated without losing representative capacity, already very reduced due to the use of depthwise convolutions in the previous stage.All experiments have been performed using the IACC3  dataset When we tested with the IACC3 split, the use of the Depthwise version 2 modification without attention block improved the F1 metric obtained with the original model. The attention block even improved the metric obtained using Depthwise version 1 to be similar to the one obtained using Depthwise version 2 with attention. We also verified this when testing with BBC for the case of Depthwisev2Transnetv2B.In the case of testing with RAI and ClipShots, the metrics obtained with the Depthwise versions are similar to the original ones. It should be noted that the Depthwise versions are therefore matching and even improving the F1 metrics of the original models to a fraction between ×4 and ×5 the number of parameters of the original models.With respect to the impact on the use of the attention block, in all cases, the F1 metric of the original model is improved, except with Depthwisev2Transnet. The DepthwiseTransnetv2 score was so high that none of the other types of care were able to improve it. Besides this, we noticed that when there is an improvement, it is not always using the same type of attention. These are relevant facts that we leave for further research and we think are correlated. Possible improvements to address this issue in the attention block would be to use a parametrizable dimension attention map, Also relevant is the disparity of F1 results obtained depending on the test dataset used. This fact is also evident in the original papers. We believe that for this task to be tackled with better guarantees, it would be convenient to develop a single standardized benchmark dataset for both training and testing that would serve as a reference in a similar way as other datasets, such as Imagenet, in the image classification task. For all tests, we use three NVIDIA RTX 3090 graphic cards as GPU support for training and inference. The computer has an i7 processor, with 96GB RAM. Models are reduced by a factor between ×4 and ×5 the number of parameters, with a total training time of 6 h, using all IACC3 datasets and 30% split for test. Inference time is 0.8 s for an input of 100 frames.


RNA modifications have been proven to be an important complement to epigenetics. Currently, at least 170 types of RNA modifications have been identified among all three life domains. In the past decade, several molecular functions of RNA modifications have been unveiled, including RNA structure switches, RNA stability, RNA export, and translation. RNA modification-associated biological processes such as neuro and embryo development, cell cycle, and stress response were also investigated. Additionally, aberrant RNA modifications have been observed in multiple diseases and are considered as potential therapeutic targets. However, compared with well-studied DNA modifications and posttranslational modifications, the biological meanings of RNA modifications have not been yet deciphered in detail. To facilitate the understanding of RNA modifications and their biological roles, we collected 14 articles on this Research Topic, including 13 research articles and a review.Pecori et al. provide unique work on this Research Topic, focusing on the RNA editing mechanism. Based on high-throughput sequencing methods, the authors found putative U-to-C editing sites. A more in-depth analysis revealed that such sites may be misinterpreted as novel modification events, resulting instead from A-to-I editing on overlapping antisense RNAs that are transcribed from the same loci. Their findings were experimentally validated by RT‒qPCR and editing quantification.Wang H. et al. collected twenty patients with and without SAE. In this work, authors integrated multiple experimental methods, such as ELISA, RT‒qPCR, 16S rDNA sequencing, and LC-MS/MS. The ELISA and RT‒qPCR results showed positive correlations between IL-6, ICAM-5, and the m6A methyltransferase METTL3, while the m6A demethylase FTO was decreased in SAE patients. Interestingly, a positive correlation between the abundance of Acinetobacter and the expression of METTL3 was also observed, which affected the diversity of the gut microbiome. In general, m6A regulators could be used for SAE screening.Noncentral nervous system sepsis can cause sepsis-related encephalopathy (SAE), a brain dysfunction disease. To identify the potential biomarkers and association among the gut microbiome, serum metabolomic profile, and RNA m6A methylation in SAE patients, She et al. provided an epitranscriptome profile in villous tissues from spontaneous abortion (SA). They applied MeRIP-seq to detect methylation regions and integrated bioinformatics analysis. Based on the sequencing results, the authors suggest that the methylation distribution and motifs differ in SA and normal conditions. In the conjoint analysis of meRIP-seq and RNA-seq, the enriched gene ontology and KEGG pathways also differed between SA and normal conditions. Additionally, their results suggested that m6A modification plays an important role in SA by regulating lysine degradation and the Hippo signaling pathway. In summary, the authors believe their findings provide an alternative therapeutic target for spontaneous abortion.Three studies used high-throughput sequencing to show the landscape of posttranscriptional regulation. Wang S. et al. presented the epitranscriptome of the mammary gland tissues of dairy goats at different lactation stages. They applied MeRIP-seq to show 2,476 and 1,451 m6A methylation peaks during the early and peak stages of lactation, respectively. The distribution of m6A peaks among transcriptomes differs at the early and peak stages, whereas the motif is similar in different stages. The differentially methylated genes were further analyzed by gene ontology and KEGG pathway analyses, and the results suggest that hypo- or hypermethylated genes participate in biological processes, such as cell apoptosis, cell growth processes, cellular components, or biogenesis. Finally, the hub genes show that HRAS, JUN, and EGFR may play the most important roles in the lactation stages.Fei et al. used high-throughput miRNA-seq to analyze differentially expressed miRNAs in the liver tissue between Hu (short/fat-tailed) sheep and Tibetan (short/thin-tailed) sheep. Compared with Hu sheep, six upregulated and five downregulated miRNAs were observed in Tibetan sheep. Miranda and RNAhybrid were used to predict the target of miRNA. The differentially expressed miRNAs and their target genes were integrated into gene ontology and KEGG pathway analysis. In addition to bioinformatics analysis, oar-miR-432-regulated SIRT1 was validated by Western blotting. In general, authors believe their work could provide a theory to study the fat metabolism of sheep.miRNA is another type of posttranscriptional regulation. He et al. provided a bioinformatics analysis to show the potential association between RNA methylation and lncRNAs in colon cancer, which could be biomarkers for hot and cold tumors and prognosis. They used RNA-seq data from the colon cancer cohort from TCGA and identified m1A/m5C/m6A/m7G-related lncRNAs based on Pearson correlation. In a further step, univariate Cox regression analysis was applied to identify 23 RNA modification-related lncRNAs with prognostic value. Additionally, the patients classified into different groups based on RNA modification-related lncRNAs had different clinical characteristics in immune microenvironmental infiltration and immunotherapy response. The authors believe their work will contribute to personalized treatment regimens. Other works presented by Li et al. analyzed the necroptosis-related genes in the colon cancer cohort and the potential association between necroptosis-related genes and RNA modifications.Two works focused on colon cancer. Gao et al. and Lu et al. showed that m6A and m5C participate in the development of liver disease, respectively. Gao et al. used wet lab methodologies to identify that the m6A methyltransferase METTL16 contributes to liver fibrosis in chronic hepatitis B infection. Lu et al. used bioinformatics analysis to identify m5C-related lncRNAs in hepatocellular carcinoma.Qiao et al. analyzed high-throughput sequencing data to show that m6A regulators are biomarkers in osteoporosis, which was validated by experiments. Huang et al. used TCGA-BRCA RNA-seq data to identify m7G-related lncRNAs and validated them by RT‒qPCR. Cai et al. analyzed previously published DNA and RNA methylation profiles to study uterine fibroids and validated them by experiments to identify PLP1 as a biomarker. Another work presented by Wang Z. et al. used different datasets to build a prediction model for the prognosis of idiopathic pulmonary fibrosis and validation.Bioinformatics analysis with experimental verification was integrated into osteoporosis, breast cancer, and uterine fibroids studies. Sun et al. provided a review article to summarize the roles of m6A methylation in aging and aging-associated diseases, including tumors, neurodegenerative diseases, diabetes, and cardiovascular diseases. In addition, the authors also discussed the association between m6A methylation and autophagy, inflammation, oxidative stress, and DNA damage. Considering the importance of m6A in aging and disease development, the authors suggest that m6A-related drugs should be developed to address the challenges of aging.Generally, in this Research Topic, 13 articles focused on posttranscriptional regulation-related biological processes or disease development covering m1A/m5C/m6A/m7G, and one article studied the mechanism of RNA editing. We hope our Research Topic enhances our understanding of RNA modifications.