Eukaryotic genomes are known to prevalently transcribe diverse classes of RNAs, virtually all of which, including nascent RNAs from protein-coding genes, are now recognized to have regulatory functions in gene expression, suggesting that RNAs are both the products and the regulators of gene expression. Their functions must enlist specific RNA-binding proteins (RBPs) to execute their regulatory activities, and recent evidence suggests that nearly all biochemically defined chromatin regions in the human genome, whether defined for gene activation or silencing, have the involvement of specific RBPs. Interestingly, the boundary between RNA- and DNA-binding proteins is also melting, as many DNA-binding proteins traditionally studied in the context of transcription are able to bind RNAs, some of which may simultaneously bind both DNA and RNA to facilitate network interactions in three-dimensional (3D) genome. In this review, we focus on RBPs that function at chromatin levels, with particular emphasis on their mechanisms of action in regulated gene expression, which is intended to facilitate future functional and mechanistic dissection of chromatin-associated RBPs. The traditional view of transcription is to produce either structural RNAs or protein-coding mRNAs. Specific structural RNAs are assembled into various RNA machines to catalyze specific biochemical reactions, and protein-coding RNAs are processed in the nucleus  and then exported to the cytoplasm to translate into proteins. The advent of deepsequencing technologies has now revealed that mammalian genomes are far more active in transcription , generatRNA molecules contain a series of single- and double-stranded regions that enable them to interact with DNA, RNA, and protein, thus providing versatile structural modules that are distinct from those in proteins to mediate network interactions. Through functional dissection of specific RNA metabolism pathways, a large number of RNA-binding proteins (RBPs) have been characterized, which often process unique structural motifs for direct contact with RNA sequences, base compositions and modifications, polynucleotide backbone, double-stranded regions, or RNA tertiary structures. However, recent global surveys of RBPs reveal ~ 1500 RBPs encoded by mammalian genomes, many of which do not carry canonical RNA-binding domains . It is pGiven prevalent transcription activities in mammalian genomes, our recent large-scale chromatin immunoprecipitation sequencing (ChIP-seq) analysis of RBPs reveals that nearly all biochemically defined chromatin regions  in the human genome involve specific RBPs, and a significant fraction of these nuclear RBPs appear to directly participate in transcriptional control . In thisChromatin-associated RBPs can be detected either on an individual basis or at the genome-wide scale. If the experimental goal is to explore a suspected function of a specific RBP on chromatin , the fircis-acting RNAs, but it is quite challenging to link RBP–chromatin interactions mediated by trans-acting RNAs. However, this is readily approachable if a study begins with a specific chromatin-associated noncoding RNA and the goal is to understand the function and mechanism of such potential regulatory RNA on chromatin by identifying its interaction with DNA and then searching for potential RBPs involved   or formainvolved . For thiinvolved , CHART , which measures nascent RNA production .To help differentiate between direct versus indirect effects, one may determine whether RBP–chromatin interactions are linked to target genes the RBP binds, assuming that bound target genes are more affected than unbound genes. However, if the RBP under investigation preferentially binds intergenic regions, such as enhancers, it would be important to link individual binding events to likely target genes . Most stIf an RBP is suspected to play a direct role in transcription, it is often informative to survey its impact on the behavior of specific RNA polymerases. Using Pol II as an example, the carboxy-terminal domain (CTD) of the largest subunit is posttranslationally modified, and specific modification events have been linked to different steps in transcription according to the so-called CTD code (Hsin and Manley 2012). Therefore, quantifying those modification events and mapping them to chromatin by ChIP-seq in response to RBP knockdown are often informative to pinpoint a specific transcription step(s) being regulated . MoreoveChromatin-bound RBPs are not necessarily involved in transcription, but rather in coupling transcription with downstream RNA processing events, which can affect RNA fates by multiple mechanisms. A battery of high-throughput technologies may be used to pinpoint changes in RNA fate. RNA-seq is again a powerful strategy to obtain the first approximation on differential gene expression. Resultant high-density reads can be aligned to the reference genome to deduce alternative splicing events by using rMATS . VariousBelow, we discuss specific RBPs that have been characterized to some mechanistic details to illustrate how RBPs may be coupled with regulatory RNAs to control gene expression via their actions on chromatin.Multiple RBPs are known to be either part of the Pol II holoenzyme or PIC that contains Pol II. In fact, the active center cleft of Pol II has been found to be able to bind B2 noncoding RNA transcribed from a repeat (SINE) element , which cInitiation of transcription requires the accessibility of gene promoters to DNA-binding TFs, and a recent large-scale survey of RBPs reveals that gene promoters are also the most predominant hotspots for RBPs . AlthougMost gene promoters in mammalian genomes are now known to be regulated by transcription pausing and pause release in promoter-proximal regions . A largeThe release of paused Pol II is a major step in transcriptional control, which is regulated by the P-TEFb complex, consisting of cyclin T and CDK9 kinase, to phosphorylate NELF, DSIF, and Pol II Ser2 . InteresGene silencing results from the formation of heterochromatin, but, counterintuitively, both the formation and maintenance of heterochromatin appear to depend on ongoing transcription. Heterochromatin can be further classified into facultative or constitutive heterochromatin, which are respectively decorated with H3K27me3 and H3K9me2/3. Interestingly, both lncRNAs and small RNAs have been shown to play critical roles in establishing heterochromatin . ExemplaDrosophila germline cells, piRNAs are generated and amplified by the “ping-pong” mechanism. These endo-siRNAs or piRNAs are loaded on RNA-induced transcriptional silencing complex to target nascent homologous transcripts on chromatin, which help recruit H3K9me2/3 methyltransferases (SUV39) to deposit the histone marks to establish constitutive heterochromatin. Importantly, this process involves numerous RNA-mediated interactions, including direct interactions of the constitutive heterochromatin factors themselves with RNA to facilitate both the formation and spreading of heterochromatin (Repeat-derived small RNAs are well known to mediate the formation of constitutive heterochromatin . Brieflyhromatin . It is thromatin . Therefohromatin .The primary purpose for RBPs to associate with chromatin has been thought to facilitate cotranscriptional processing events from RNA modification to intron removal to polyadenylation to RNA export . AlthougInterestingly, promoters have been reported to dictate downstream events from splicing  to RNA sAnother popular idea is for RBPs to interact with various modified histones, thereby coupling specific epigenetic features to the regulation of RNA processing. Indeed, many RBPs have been identified to associate with histone modification events , and somChromatin-associated RBPs may play larger roles in 3D genome beyond their functions in modulating local transcriptional and cotranscriptional activities , as reguMore recently, another zinc-finger-containing TF YY1 is recognized to also play a broad role in mediating long-distance genomic interactions between promoters and enhancers . InteresWe have particularly focused in this review on diverse functions and mechanisms of chromatin-associated RBPs in regulated gene expression. An important message is that RNAs are no longer just products, but are also regulators of gene expression. Their regulatory functions are executed by specific RBPs, together contributing to network interactions in 3D genome. Such RNA and RBP-mediated interactions are critical for both gene activation and silencing, which calls for future research in this direction to understand how functional genome is organized and dynamically regulated by RNAs and RBPs in development and disease.


Extracellular vesicles (EV) have attracted much attention as potential biomarkers due to their protein, RNA and other nucleic acid content. The most common method used for EV isolation is differential ultracentrifugation (DU), however given the DU technical difficulties, other more practical methods have surged, such as membrane-affinity column commercial kits. Here, we assessed one commercial kit in terms of EV recovery and EV-derived RNA yield and compared it with a DU protocol. Our data shows that the commercial kit preparation results in a lower count of EV-like structures and a reduced expression of EV markers when compared to DU samples. Thus, apparently suggesting that the commercial kit had a lower EV yield. However, these findings did not reflect on RNA yield, which was greater with the commercial kit, even after an enzymatic treatment with proteinase K and RNAse A. We conclude that the kit has a higher EV-derived RNA yield in comparison to our DU protocol, suggesting that it may be the method of choice for RNA sequencing purposes. Extracellular vesicles (EV) is a generic term used to define particles delimited by a lipid bilayer that originate from the cell ,2. ClassEV have almost ubiquitous presence throughout distinct biofluids –9. ThereEV show several characteristics that make them strong candidates for biomarker research. Firstly, they are present in biofluids that are easier and safer to obtain than tissue biopsies, particularly in sites of difficult access like the central nervous system. Moreover, they contain RNA and DNA that can aid in the diagnosis, disease monitoring and early detection or relapse of diseases ,22. SeveTo further study EV-derived RNA as a biomarker, a series of recommendations have to be considered: the chosen isolation method has to result preferentially in a high yield of EV and EV-derived RNA to use in downstream experiments such as sequencing ; if the The most widely used EV isolation method is Differential Ultracentrifugation (DU), a technique that separates particles based on their buoyant density . This meThere are only two studies that have evaluated the membrane-affinity column kit performance. A primary evaluation of the exoRNeasy serum/plasma Maxi Kit (QIAGEN) showed its ability to isolate equal or higher amounts of EV and EV-derived RNA in comparison to an optimized DU protocol . In contTo clarify this, we assessed ExoEasy kit ability to isolate EV in comparison to a DU protocol in culture supernatant and plasma samples. Also, we analysed EV-derived RNA yield after elimination of non-vesicle RNA in DU and ExoEasy kit samples. Here we present evidence about the utility of ExoEasy kit in EV-derived RNA obtainment.2. For EV harvesting, cells from passages 3–10 were employed. For EV isolation, cultures were supplemented with non-heat inactivated exosome depleted FBS at 10% (v/v) from Thermo Fisher (A2720803).The diffuse large B cell lymphoma cell line SU-DHL-6  was used for EV isolation. Cells were maintained in RPMI 1640 medium (No. Cat 30–2001) supplemented with penicillin streptomycin solution 10,000 U/ml at 5% (v/v) from SIGMA (P4333-100ML) and non-heat inactivated FBS at 10% (v/v) from ATCC (No. Cat 30–2020). The cells were incubated at 37°C with 5% COTwenty millilitres of peripheral blood were collected from five healthy donors  between 20–40 years old. None of the donors were taking medication at the time of blood collection. All donors had a 10 h fasting. Blood collection was done according to the International Society for Extracellular Vesicles (ISEV) guidelines . In. In40]. Then we studied the size and concentration of nanoparticles in DU-Plasma and Exo-Plasma samples by NTA. To do this, diluted DU-Plasma and Exo-Plasma samples were analysed by the NanoSight instrument. A representative histogram of DU-Plasma  and Exo-The expression of EV markers was analysed in DU-Plasma and Exo-Plasma samples. Although both samples expressed tetraspanins CD9, CD63 and membrane-associated protein Flotillin, higher intensity bands were shown in DU-Plasma samples . CellulaTherefore, Exo-Plasma samples had a larger number of particles but a lower amount of EV-like structures and EV markers when compared to DU-Plasma samples. Our data shows that the extra particles detected by NTA in Exo-Plasma samples do not come from EV resuspension buffer  nor fromEV have been reported to co-isolate with albumin, protein complexes and lipoproteins . TherefoIn order to have an overview of the protein repertoire in samples and compare it with that of secreting cells, SDS-PAGE gels were stained with silver nitrate that reacts with NH3 groups from proteins resulting in silver precipitation and thus visualization of proteins. In the first gel, a greater repertoire of proteins from various sizes was observed in SUDHL6 lysates in comparison to DU-CS and Exo-CS samples . InteresIn order to eliminate non-EV RNA from DU-CS and Exo-CS samples, these were treated with proteinase K and subsequently with RNAse A. Then, RNA was extracted and examined by Bioanalyzer equipment using the RNA Pico kit. As negative control, RNA was extracted from DU-derived EVs isolated from medium , that had not been exposed to cells. As expected, no bovine EV-derived RNA (~200 nt) was present in the samples when measured by bioanalyzer; however, it might be present when analysed by more sensitive techniques . A repreBoth DU-Plasma and Exo-Plasma samples were also treated with proteinase K and RNase A before RNA extraction. As a positive control for enzyme conditions, small RNA was isolated from peripheral blood by a combined phenol/column-based isolation method  and then, treated with proteinase K and RNAseA. As expected, both enzymes resulted in the degradation of all the small RNA present in the sample . The hisTotal RNA extracted from DU-CS samples (mean: 86.8 ng) was lower than Exo-CS samples (mean: 149.8 ng). After enzymatic treatment, a similar tendency was observed. Exo-CS samples had a greater RNA recovery (mean: 49 ng) in comparison to DU-CS samples (mean: 3.3 ng) .When comparing RNA yield in DU-Plasma and Exo-Plasma, there was a higher yield of total RNA in the latter case. After enzymatic treatment, Exo-Plasma still had the highest RNA yield (mean: 3.9 ng) in comparison to DU-Plasma (0.2 ng) . This waIt is not clear how efficient is the performance of membrane affinity column kits in terms of EV recovery and EV-derived RNA yield. Thus, we compared the characteristics of ExoEasy kit derived EV as well as their RNA content to those obtained by DU when processing culture supernatant and plasma samples.7 cells), did not reflect the apparently greater EV recovery by DU protocols.All samples used the same starting CS or plasma sample as well as the same RNA extraction protocol in order to make possible the comparison between ExoEasy Maxi kit and DU. Our data showed that Exo-CS samples exhibited a scarce number of EV-like structures by TEM in comparison to DU-CS samples. In addition, a lower expression of EV markers CD63, CD9 and Flotillin was detected by immunoblotting in Exo samples (Exo-CS and Exo-Plasma) when compared to DU samples (DU-CS and DU-Plasma). These data suggested a higher EV recovery in DU samples when compared to Exo samples. However, a greater particle concentration detected by NTA in Exo samples as well as a higher RNA yield  required in order to obtain EV, DU is associated with increased damage to the integrity of EV structure when compared with other isolation methods ,46. ThisIt is possible that DU is a more aggressive procedure than affinity column thus resulting in the breaking of some EV. This might lead to the leakage of EV-derived RNA which will be susceptible to the action of proteinase K and RNAse A, thus resulting in a lower RNA yield. In this same line of reasoning, the fragments of damaged EV still retained CD63, CD9 and Flotillin expression that could be detected by immunoblotting, thus giving a higher signal in DU samples. However, the fragments of broken EV, which will probably have a size shorter than 60 nm (detection limit of NS300) –50, willIn terms of co-isolated contaminants, we showed by immunoblotting and fluorescent gel staining that albumin is present in both Exo samples and DU samples. Apo A-I which is a major component of high-density lipoprotein (HDL), was found more concentrated in DU samples than Exo samples, suggesting a higher amount of HDL in DU preparations. This is important as HDL has been found to transport microRNAs . ApoB 48Based on the results reported in this study, ExoEasy kit might be a more delicate technique which leads to a higher RNA yield in comparison to DU. We proposed the incubation of the ExoEasy kit eluate with proteinase K and RNAse A before RNA extraction as we showed the presence of non-EV RNA in the ExoEasy kit product. The yield of protected RNA obtained by ExoEasy kit in both CS and plasma samples has an acceptable concentration for library preparation as well as having low contamination with lipoproteins. Therefore, ExoEasy kit is a good option for EV-derived RNA workflows.S1 Fig(A) Particle concentration measured by NTA in filtered PBS, diluted DU-Plasma sample (1:150) and undiluted DU-Plasma sample. (B) Particle concentration measured by NTA in buffer XE, diluted Exo-Plasma sample (1:600) and undiluted Exo-CS. (C) Median size value and concentration of particles found in PBS and buffer XE.(TIF)Click here for additional data file.S2 FigElectropherograms corresponding to DU-CS (A-C) and Exo-CS samples (B-D) before and after proteinase K and RNAse A treatment. All samples were analysed by Agilent small RNA Kit in an Agilent 2100 Bioanalyzer (4–150 nt). The electropherograms show the size distribution in nucleotides (nt) and fluorescence intensity (FU) of small RNA. The peak at 4 nt is an internal standard. The region delimited from 10–40 nt correspond to miRNA-like species. A representative electropherogram of two independent experiments is shown.(TIF)Click here for additional data file.S3 FigQuantitation of small RNA by MicroQubit assay in Exo-Plasma samples before and after Proteinase K and RNAse A treatment. Each bar of Exo-Plasma and Exo-Plasma + RA + PK represent the mean values ± SD of total RNA (ng) obtained in three independent experiments.(TIF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S1 Raw images(TIF)Click here for additional data file.


Porites astreoides coral colonies on a shallow reef in St. John, U.S. Virgin Islands: near-coral , reef-depth (sampled 2 m above each colony) and surface seawater (sampled 1 m from the seawater surface). At all time points and locations, we quantified abundances of microbial cells, sequenced small subunit rRNA genes of bacterial and archaeal communities, and measured inorganic nutrient concentrations. Prochlorococcus and Synechococcus cells were consistently elevated at night compared to day and these abundances changed over time, corresponding with temperature, nitrite, and silicate concentrations. During the day, bacterial and archaeal alpha diversity was significantly higher in reef-depth and near-coral seawater compared to the surface seawater, signifying that the reef influences the diversity of the seawater microorganisms. At night, alpha diversity decreased across all samples, suggesting that photosynthesis may favor a more taxonomically diverse community. While Prochlorococcus exhibited consistent temporal rhythmicity, additional taxa were enriched in reef seawater at night compared to day or in reef-depth compared to surface seawater based on their normalized sequence counts. There were some significant differences in nutrient concentrations and cell abundances between reef-depth and near-coral seawater but no clear trends. This study demonstrates that temporal variation supersedes small-scale spatial variation in proximity to corals in reef seawater microbial communities. As coral reefs continue to change in benthic composition worldwide, monitoring microbial composition in response to temporal changes and environmental fluctuations will help discern normal variability from longer lasting changes attributed to anthropogenic stressors and global climate change.Reef organisms influence microorganisms within the surrounding seawater, yet the spatial and temporal dynamics of seawater microbial communities located in proximity to corals are rarely investigated. To better understand reef seawater microbial community dynamics over time and space, we collected small-volume seawater samples during the day and night over a 72 hour period from three locations that differed in spatial distance from 5  Microorganisms play fundamental roles in coral reef ecosystem nutrient cycling , 2. MicrMost of our knowledge of reef seawater microbial community composition and function is obtained from opportunistic field sampling of reef seawater collected across reefs at a single point in time. This sampling approach has contributed knowledge about coral reef seawater microbial dynamics along various environmental and anthropogenic gradients –16, but On coral reefs, there are significant temporal changes due to the availability of light (influencing primary production), influences of tides, and diel vertical migration and grazing by zooplankton . The avaAcropora formosa changed between day and night , but some of the trends we observed in this study have been observed in other marine microbial communities. Gammaproteobacteria exhibited higher activity at night [Differential enrichment tests of reef seawater bacterial and archaeal communities  between day and night revealed ASVs exhibiting diel enrichment. Excluding the consistent diel changes in at night  in the Mat night  and up-rat night . These tat night , 73.Prochlorococcus, SAR11 (clades 1a and 1b), the NS4 marine group, and “Candidatus Actinomarina,” taxa that are associated with oligotrophic environments and that are commonly detected in reef seawater.The observed richness of bacterial and archaeal ASVs was higher in seawater collected from reef-depth and coral ecosphere seawater relative to surface seawater during both day and night, demonstrating spatial heterogeneity in the microbial community over the water column and elevated microbial diversity at depth. Enhanced diversity closer to the reef may reflect a wider variety of microbial niches, increased nutrient availability, as well as less photoinhibition compared to surface seawater. Taxa enriched in reef-depth and coral ecosphere seawater compared to surface seawater bacterial and archaeal communities included Prochlorococcus ASVs were identified as Prochlorococcus marinus (MIT9131), a low-light adapted Prochlorococcus ecotype. We suspect that there are more Prochlorococcus ecotypes in reef seawater, but did not have the resolution to capture this diversity by comparing differences in the V4 region of the 16S rRNA gene because ecotypes can differ by <1% variation [reviewed by Prochlorococcus ecotypes, MIT9313 is usually found at the base of the euphotic zone and has the genetic capability of using and reducing nitrite as a source of nitrogen [Prochlorococcus could be advecting onto the reef from offshore currents, but if this were the case, we would expect Prochlorococcus to be evenly distributed across the shallow water column. We consistently detected this ecotype at 7 m depth as opposed to 100 m in the open ocean [reviewed within Prochlorococcus because of photoinhibition [nitrogen , 76. Prohibition .Prochlorococcus, heterotrophic bacteria within the SAR11 clade are abundant in oligotrophic marine environments and coral reef seawater [P. astreoides stimulate SAR11 growth rates and there is evidence that P. astreoides also grazes on these cells in mesocosm experiments [Like seawater , 79. In eriments . EnrichmCa. Actinomarina” are very small and their distribution tracks with abundances of Synechococcus [Ca. Actinomarina” have also been identified in coral reef seawater and coral mucus previously [NS4 marine group bacteria within the phylum Bacteroidetes are commonly detected in marine microbial communities –83 and ehococcus . “Ca. Aceviously . We haveP. astreoides does not influence coral ecosphere bacterial and archaeal communities to the same degree that other coral species do. For example, P. astreoides’ ecosphere bacterial and archaeal communities sampled in Cuba were more similar to reef-depth seawater microbial communities compared to ecosphere communities sampled surrounding other Caribbean coral species including P. astreoides, Orbicella faveolata, Montastrea cavernosa, Pseudodiploria strigosa, and Acropora cervicornis [P. astreoides could be site-specific and depend on local environmental conditions and/or on the health state of the coral colony, similar to observations that have been made about common coral-associated microorganisms [P. astreoides were mostly dominated by Endozoicomonas bacteria whereas Endozoicomonas were present but cryptic (average relative abundance of 0.11%) in coral ecosphere and reef-depth seawater. These findings demonstrate that interactions between corals and planktonic microorganisms may be nuanced and depend on factors that have been previously unexplored, necessitating more research in this area.Coral ecosphere and reef-depth seawater bacterial and archaeal communities had generally similar compositions and metrics of alpha diversity (richness and Shannon’s diversity index) even though distance  significantly influenced community similarity as revealed by the PERMANOVA test. Furthermore, no ASVs were significantly enriched in coral ecosphere compared to reef-depth seawater. These conflicting results potentially suggest that minor compositional differences do exist between ecosphere and reef-depth seawater, but that most of the compositional differences are driven by temporal changes. To elaborate, we expected to find taxonomic differences between these two seawater environments as well as enrichment of Gammaproteobacteria within the coral ecosphere seawater based on previous observations , 37, butrganisms . For insProchlorococcus and Synechococcus cell abundances at night and changes over time that corresponded with changes in temperature and increases in nitrite and silicate. We also identified diel patterns in bacterial and archaeal diversity, enrichment of different taxa by day and night, and specific taxa exhibiting rhythmic population fluctuations in reef-depth and coral ecosphere seawater. Bacterial and archaeal alpha diversity was higher in reef-depth seawater compared to surface seawater, suggesting that there may be enhanced microbial niches close to the reef, a hypothesis held widely, but rarely observed. Comparatively, temporal changes superseded spatial differences in terms of influence on the seawater microbial community, emphasizing the need for a more comprehensive understanding of how these communities change over short term  and longer term  time-scales. Coral ecosphere and reef-depth seawater bacterial and archaeal communities had generally similar compositions in this study, suggesting that colony or site-specific conditions may influence the outcome of coral-microbial interactions within the coral ecosphere. Overall, these findings demonstrate the small-scale population dynamics that take place over a diel cycle and the relative influence of temporal compared to spatial changes on microbial communities sampled across one reef.High-resolution sampling of surface, reef-depth, and coral ecosphere seawater microbial communities revealed several novel observations of seawater microbial dynamics on tropical coral reefs. Even though reef seawater bacterial and archaeal communities were highly similar over three days, there were consistent increases in At the scale of the reef, understanding variability in microbial composition as well as dominant forcings on these communities is essential for describing baseline temporal and spatial dynamics in productive, diverse, and sensitive coral reef ecosystems. As coral reefs continue to decline, these changes may not only impact coral reef health and the composition of microbial communities on reefs, but also the variability of microbial population fluctuations.S1 FigEach point represents a sample. Point shape corresponds to sampling distance from the coral and point color reflects the colony adjacent to where sampling was conducted. Gray shading indicates samples collected at night. Lower and upper edges of the boxplot correspond to the first and third quartiles, the whiskers extend to the largest or smallest value at 1.5 times the interquartile, and the black bar across the box represents the median.(DOCX)Click here for additional data file.S2 FigEach point represents a sample. Point shape corresponds to sampling distance from the coral and point color reflects the colony adjacent to where sampling was conducted. Gray shading indicates samples collected at night. Lower and upper edges of the boxplot correspond to the first and third quartiles, the whiskers extend to the largest or smallest value at 1.5 times the interquartile, and the black bar across the box represents the median.(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Appendix(XLSX)Click here for additional data file.


In this article, I develop a formal model of free will for complex systems based on emergent properties and adaptive selection. The model is based on a process ontology in which a free choice is a singular process that takes a system from one macrostate to another. I quantify the model by introducing a formal measure of the ‘freedom’ of a singular choice. The ‘free will’ of a system, then, is emergent from the aggregate freedom of the choice processes carried out by the system. The focus in this model is on the actual choices themselves viewed in the context of processes. That is, the nature of the system making the choices is not considered. Nevertheless, my model does not necessarily conflict with models that are based on internal properties of the system. Rather it takes a behavioral approach by focusing on the externalities of the choice process. In other words, a more formal and rigorous definition of freedom of choice ought to be a prerequisite to any deeper understanding of free will.What is it that we mean when we ask if a system possesses free will? In most discussions of free will, the nature of what freedom entails is often taken for granted; if we ask if a given choice is free, we are assuming that it is a well-formed question to begin with. Rather than asking if a given choice is free, we might instead ask what, in general, a free choice This is not merely academic. In EPR tests in quantum mechanics, it is often assumed that the experimenters have free will  determinism has long been central to the nature of free will, at least in the Western perspective . Howeverividual”  p. 183 What do we generally think of when we think of free will? O’Connor claims that systems that possess free will should generally be possessed of three capacities: (1) an awareness and sensitivity to reasons for actions; (2) an ability to weigh and critically probe desires and intentions, and possibly to reevaluate goals; and (3) the ability to choose, based on reasons, which action to take on a given occasion . While Oadaptive free choice, then, includes the capacity to weigh and probe desires, the formation of an intention to act based on the action of weighing and probing desires, and finally the carrying out of the action whose intention was formed. That is, I assume that it is not merely sufficient to possess the capacity of choice. Some action representing the choice being made must also take place. This is not a trivial point.Suppose that I open my refrigerator with the intent of having something to eat and am presented with a variety of options. It is immediately obvious that, if my refrigerator only contained one thing, then I wouldn’t have much of a choice. It seems clear, then, that, aside from any internal capacities that I might possess, one very clear external factor in assessing the freedom of a choice is the number of options that are available to choose from. By its very nature, the word “choice” implies that there must be a minimum of two options to choose from. However, it is also necessary that I be able to read those options (or a certain subset of them) into my memory and then process them, all in a finite amount of time . As such, there is a computational aspect to free choice. Indeed, several computational models of free will exist ,13. Howeenacting a choice. That is, we assume  that, to a generally high degree of probability, enacting the choice results in the desired outcome. To put it another way, in choosing carrots, I am relying on the fact that there is a very high probability that, at some point between choosing carrots and actually removing them from the refrigerator and ingesting them, they remain carrots and do not spontaneously turn into peppers. That is, we rely on the fact that the universe is a relatively stable place. Consider what would happen if we could not be confident that our choices lead to their expected outcomes the vast majority of the time. If this were the case, we would likely give up on choosing since the outcomes would be closer to random and thus the act of making a choice would be pointless. As Nozick pointed out, a random act is not a free act [is a different action. Thus, it is that we are choosing between different processes rather than labeling each choice as a different outcome of the same process [Returning to the refrigerator, for the sake of simplicity, let’s suppose that I only have two options—carrots and peppers. With only two choices, there is no real concern about the reading and processing time being too lengthy and we can assume, for the moment, that neither is it too short, i.e., I am free to stand in front of the refrigerator and ponder these two possibilities, assigning meaning to them in the form of weights. Let us suppose that I then make a choice—carrots, for the sake of argument. There is an often overlooked assumption in discussions of free will having to do with the nature of actually free act . Thus, t process . In otheadaptive . This dive .A state The states of systems are thus functions of variables Definition2 (Process).A process π is a means by which a system may transition between some fixed state That is, though a process always ends in a single definite state, it may probabilistically end up in one of a possible collection of states. We assume that the outcome state is definite . A graphical representation of a process is shown in multiplicityThe and that all outcomes have an equal likelihood of occurring, i.e., the probability distribution for the process is flat. Such a process would naturally be fully random since no single outcome is favored over any other.These definitions now allow us to describe a spectrum of processes. It might be true, for instance, that, for a given process It is important to distinguish here the difference between determinism and causality. In this model, all processes are causal in that they follow from other processes. This includes “spontaneous” processes which are understood to follow from conditions produced by other processes. For example, spontaneous particle creation can only occur because of the presence of the underlying fields. This model would assume that the existence of those fields came about from some process, even if the nature of that process is not known. Thus, though this model is causal, it is not fully deterministic. The distinction between causality and determinism has been rigorously delineated by D’Ariano, Manessi, and Perinotti, and we refer the reader to  for detaI note that all these definitions can be broken into two possible categories—micro or macro—depending on the level of the system under consideration. This has the usual meaning from statistics and statistical mechanics. Thus, for instance, we can speak of a pair of (fair) dice as having 36 different microstates and 11 different macrostates. In this case, the microstates are the various configurations that produce a given macrostate. A pair of (fair) dice is a fairly simple system in which we assume the fundamental constituents (the dice) do not interact. In more complex systems, interactions may occur between the various parts of the system. It is not necessary for interactions to occur, however, in order to obtain a deterministic macrostate from a collection of equally likely microstates.Consider a simple two-state system The multiplicity for this process is n possible microstates N possible macrostates Now, consider a more complex system that is composed of smaller sub-systems. The more complex system will be referred to as a macrosystem and its states and processes will be referred to as macrostates and macroprocesses. I will distinguish these from the component microsystems, microstates, and microprocesses by using capital letters. Thus, for example, if non-interacting two-state microsystems As an example, consider a macrosystem The probability of any given macrostate is given by the multiplicity of that macrostate divided by the total multiplicity. In this situation, there is only one microstate for each macrostate and soN non-interacting two-state microsystems and let us ask how many of the subsystems will be in the state Now, consider an even more complex macrosystem composed of N is very large, we can use Stirling’s approximation, N in If J becomes deterministic. From this we can see that sets of random microprocesses can lead to deterministic macroprocesses.We can see from this that, as the size of the system increases, despite the underlying processes being entirely random, the system tends to cluster in a small number of macrostates. As The processes considered in the above example were non-interacting. The real world, of course, includes interactions. An interaction between two systems is also a process that in some way changes the variables of each system. As such, each subsystem is undergoing a process that is influenced by the other subsystems’ processes. The details of the process ontology for such situations is beyond the scope of the current work, which is intended as an introduction to the model. However, subsequent works will elaborate on the underlying process ontology and discuss how it affects the distributions. For the purposes of explicating the basic model of free will, the nature of the actual distributions is not important. The distributions become important when assessing the actual choices that are fed into the model.define choice in the following manner.I will refer to any system that possesses the capacity of choice, as introduced in Definition 3 (Choice).A choice is a (possible) macroprocess n is deliberate and the reason will become evident in a moment.Note that the lower-case For a particular choice to be free, the total number of choices presented to the agent at a given time, which I will call the ‘choice ensemble’ Axiom 1.The most fundamental systems are irreducible to other systems, i.e., they contain no interactions and cannot be partitioned.Axiom 2. All possible configurations, i.e., microstates, of fundamental systems are equally likely in the long run.Axiom 3. A system’s macrostates are formed via interacting (micro)processes.Axiom 4. The probability that a choice will lead to its macrostate is arbitrarily high if the choice is free, i.e., a free choice inevitably leads to a nearly deterministic outcome.Axiom 5. The number N of possible choices that a system has must be small enough to be read into the system’s memory in a finite time.Axiom 6. The choices do not change at any time during the agent’s processing of the choices nor during the agent’s enactment of the choice.Axiom 7. The choices must be distinct.Given the above definition, I add the following axioms:formal axioms of the model. There are certain assumptions and definitions that fully describe the model that will be introduced in the next section. However, from the formal standpoint, we restrict ourselves to these axioms. It is worth noting that I do not require that the interactions of Axiom 3 be classical. This is intentional. It is entirely possible that such an assumption will lead to unusual results that require a refinement of the model, but that is an area for future work.These are the k microsystems N possible choices n in Definition 3). The probability distributions associated with the choices in the ensemble are Now, suppose that some macrosystem a belongs to the D-dimensional space and ith component, respectively. This function can be used to identify the locations of the peaks in the distributions if they are not clearly known [A particularly useful analysis of multimodal distributions is based on the distribution’s overall topology. Ray and Lindsay give a method that utilizes what they refer to as the “ridgeline” function which describes the shape of ly known . Here, Ii and j in the mixed distribution i and j will be. Within the full ensemble of choices i depends on the minimum distance i and each other choice in the ensemble Axiom 7 above captures the assumption that choices can only be said to be free if they are reasonably distinct. If an agent is presented with two essentially identical choices, then there are fewer criteria by which the agent can distinguish them and thus their distributions will have a certain amount of overlap. If spatial information is included in their distributions, then they won’t overlap perfectly since the two choices will, at the very least, represent different spatial locations. For example, in choosing between two identical carrots, one is choosing between two objects that, though perhaps otherwise identical, are in different spatial locations. In any case, one expects that the more distinct the choices, the more free the ability to choose; if the choices are identical, the ability to choose is closer to a random process, e.g., the choice between two nearly identical carrots is not as free as the choice between a carrot and a pepper. A convenient measure of the distinctness of the choices in this case is given by the Mahalanobis distance ,19 betweT that depends on the total number of choices N and require that it be finite. However, one might suppose that different values for N as well as the general topology Capturing Axiom 5 proves to be a bit tricky. We could quite simply define a time function T be finite, we also need to require that it be minimized but greater than T is too small, the system is not extracting meaning from the process and thus the choice is minimally voluntary or instinctual. Hence the lower bound on T. On the other hand, one could argue that a shorter time means the system is more efficient. Greater efficiency does not necessarily mean less freedom. Thus it is that we also might require that T be as close to It is debatable whether, aside from requiring that The covariance, time function, and minimum Mahalanobis distance, then, provide a means of measuring Axioms 4, 5, and 7, respectively. This leads us naturally to a measure of the “freedom” of any particular choice in the ensemble.Definition4 (Free choice).Given a finite number of possible choices N from ensemble where I have assumed that maximizing the freedom of the choice entails minimizing its covariance since this increases the “sharpness” of the probability distribution within the ensemble. The larger the value of the free will would have a high measure of freedom. The question is whether freedom in this sense is additive or multiplicative. We can answer this by returning to the statistical arguments that underpin the first few axioms.This, of course, is only a measure of the freedom of a single choice. It is entirely possible that an agent could have many free choices and many non-free choices. One would assume that a majority of choices for any agent said to possess free will to be the following.In a certain sense, the freedom of a given choice is related to how easy it is to actually make that choice. Roughly speaking, then, the more ways in which one can make a specific choice, the greater the freedom of that choice. In that sense, freedom is similar to the outcomes of a process. The number of outcomes for a given macrostate of a process is measured by the multiplicity which is multiplicative. Thus, we can, by analogy, take freedom to be multiplicative in this particular model. As such, we can take a measure of Definition5 (Free will).Given some number of processes that result in choices, an agent’s free will is given by a partition functioni.e., the level of free will that a system possesses over N choices is dependent in a multiplicative way on the level of freedom in each of those choices.Z measure.A crucial component of the model is the set of weights given in Equation . It is winternal dynamics of the agent. That is, we could begin with a set of k fundamental subsystems each with some state could have evolved via an entirely different macroprocess There are two basic methods for assigning the weights in the function Establishing the weights in this instance relies solely on the internal dynamics of the agent and is an entirely upwardly causal process. It would be easy to dismiss this as a free choice either on the grounds that, as a closed system, the agent is causally deterministic or on the grounds that it is simply reacting to underlying random processes. It is important to note that this does not mean the system is free from external interactions. It simply means that the internal dynamics are what drives the state evolution. The external constraints may be what sets the initial conditions in the form of the initial state during the macroprocesses such that the dynamics are influenced by the environment in addition to internal factors. Axiom 2 is still satisfied since the underlying microstates of both the agent and its environment remain equally likely in the long run, but conditions are now contextual such that the macrostates are now partially constrained by external factors. These external factors can be construed as assigning a form of meaning to the weights. This is a form of downward causation as discussed by Ellis and others . Specifexample, . Howeveragainst free will . One might argue that both instances involve memory, but that really isn’t the case. Accessing memory is a necessarily downwardly causal process whereby a system in a higher-level state actively accesses stored information about lower-level states. Specifically, the process of choosing from a set of choices, which necessarily includes assigning them weights and accessing memory, is precisely a form of adaptive selection [Let’s return to the choice between carrots and peppers in my refrigerator. If I choose carrots, did I do so because my genetics and specific worldline in spacetime predisposed me to do so? If so, it was hardly a free choice. Indeed, this is a typical feature of arguments example, ). On theelection . Adaptivelection . The predo have free will. Indeed, most legal systems are built on the basis of that assumption. As O’Connor pointed out, then, the freedom that this assumed capacity of choice makes possible should come in degrees and can vary in time with individual agents [As I emphasized earlier, the model assumes that at least some complex systems possess a capacity of choice. That may or may not be a valid assumption. However, it should be clear that human behavior is at least partly guided by the assumption that we l agents . FreedomT. I suspect both may need to be determined through experiment. In addition, the model presented here employs simple Gaussian distributions, but it would be interesting to see if Fermi–Dirac, Bose-Einstein, or other such distributions give substantially different predictions.In that sense, this model is one of behavior. It considers a system as being contained within an environment that helps constrain that behavior. To a certain extent, there is a similarity here to the cause and effect repertoires of integrated information theory (IIT)  and, indClearly, the problem of free will is deeply tied to the problem of consciousness. As such, any model will include some of the same baggage as models of consciousness including, for instance, how to deal with qualia which appear to play a role in weighting the choices. More likely than not, the model will need considerable revision and alteration in order to truly capture what we think of when we think of free will, but it at least provides a basic framework for thinking about the problem.


Drosophila larvae are commonly used to study peripheral nervous system biology, particularly dendrite arborization. We sought to test if EC-tagging, a biosynthetic RNA tagging and purification method that avoids the caveats of physical isolation, would enable discovery of novel regulators of md neuron dendrite arborization. Our aims were twofold: discover novel md neuron transcripts and test the sensitivity of EC-tagging. RNAs were biosynthetically tagged by expressing CD:UPRT (a nucleobase-converting fusion enzyme) in md neurons and feeding 5-ethynylcytosine (EC) to larvae. Only CD:UPRT-expressing cells are competent to convert EC into 5-ethynyluridine-monophosphate which is subsequently incorporated into nascent RNA transcripts. Tagged RNAs were purified and used for RNA-sequencing. Reference RNA was prepared in a similar manner using 5-ethynyluridine (EUd) to tag RNA in all cells and negative control RNA-seq was performed on “mock tagged” samples to identify non-specifically purified transcripts. Differential expression analysis identified md neuron enriched and depleted transcripts. Three candidate genes encoding RNA-binding proteins (RBPs) were tested for a role in md neuron dendrite arborization. Loss-of-function for the m6A-binding factor Ythdc1 did not cause any dendrite arborization defects while RNAi of the other two candidates, the poly(A) polymerase Hiiragi and the translation regulator Hephaestus, caused significant defects in dendrite arborization. This work provides an expanded view of transcription in md neurons and a technical framework for combining EC-tagging with RNA-seq to profile transcription in cells that may not be amenable to physical isolation.Obtaining neuron transcriptomes is challenging; their complex morphology and interconnected microenvironments make it difficult to isolate neurons without potentially altering gene expression. Multidendritic sensory neurons (md neurons) of  Drosophila peripheral nervous system (PNS) neurons provide a useful model for investigating these mechanisms. Sensory neurons of the larval PNS are classified according to dendrite morphology: external sensory and chordotonal neurons have a single dendrite, bipolar dendrite neurons have two unbranched dendrite projections, and multidendritic (md) neurons have more complex dendritic arborization. Md neurons innervate the larval body wall and function as touch receptors, proprioceptors, thermoreceptors or nociceptors. Md neurons have proven useful for investigating the molecular mechanisms that control dendrite arborization  toeriments , 12.S1 Fig). To identify transcripts enriched in md neurons, we performed differential expression (DE) analysis to compare 5EC-tagged RNA (EC-RNA) and 5EUd-tagged RNA (ref-RNA). This differential expression analysis was performed using two versions of the ref-RNA: 1) the complete RNA-seq dataset and 2) a randomly generated subset of 2.7 million reads , equal amounts of total RNA were biotinylated and applied to streptavidin beads. RNA captured on the beads was directly used to prepare sequencing libraries. The number of mapped reads per sample agreed with the expected yield of tagged RNA: 5EUd-tagging  gave 48–56.6 million reads, 5EC-tagging  gave 2.7–7.7 million reads, and mock-tagging (background) gave 0.11–0.13 million reads. 5EC-tagged biological replicates and 5EUd-tagged biological replicates had a high degree of RNA-seq correlation, while the correlation for mock-tagged replicates was much lower  and the background correction procedure removed 39 transcripts from the enriched list (. Enriched and depleted transcripts were similar regardless of the type of ref-RNA used (commonly enriched transcripts are denoted in S1 Table). We also performed gene ontology (GO) analysis on the complete ref-RNA DE results  and the subset ref-RNA DE results . Both approaches yielded multiple neuron-specific GO categories including “peripheral nervous system development” and “dendrite morphogenesis”. The “synaptic growth at neuromuscular junction” GO category reflects the fact that enriched genes in this category function in the synapses of motor neurons and md sensory neurons. Non-neural GO categories, “dorsal closure” and “border follicle cell migration”, reflect the fact that many neurite growth or morphogenesis genes  are also involved in these processes. In addition to the EC-tagged data, we performed GO analysis on DE results comparing mock-RNA to the complete or subset ref-RNA. Importantly, the mock-tagged DE data did not result in any significant GO category enrichment.DE analysis identified 937 enriched transcripts and 236 depleted transcripts . To address potential non-specific purification among the enriched transcripts, we compared transcript levels between EC-RNA and mock-RNA. This comparison identified 85 transcripts with no significant enrichment compared to mock-RNA, thus reducing the list of md neuron transcripts to 852. Similar background correction for depleted transcripts is not possible since transcripts that are rare or absent in md neurons are expected to be low in EC-RNA and mock-RNA. In the background-corrected dataset, known md neuron transcripts are among the most significantly enriched and known muscle-specific transcripts are among the most significantly depleted Fig 2, this dataset contains many previously described md neuron genes (according to Flybase annotations and associated references), including transcription regulators , RNA-binding factors , ion channels , signal receptors and transducers , and cytoskeletal factors . We also identified multiple transcripts encoding general regulators of neurotransmission, including Frq1, brp, Csp, and Rim. While differences in experimental design and target cell populations limit the validity of broad comparisons between our data and prior transcriptome studies, we selected two gene lists from prior studies to compare with our enriched gene set. Iyer et al. identified 40 transcription factor genes enriched in class I and/or class IV da neurons compared to whole larvae / TM6C -1, was provided by Dr. Eric Lai.The following lines were obtained from the Bloomington Drosophila-specific AnyDeplete rRNA primer mixture. Libraries were amplified and purified according to the NuGen protocol and quality was assessed using an Agilent Bioanalyzer DNA high-sensitivity chip.5-ethynylcytosine was synthesized as previously described . BiologiTrimmomatic prior to mapping to the Drosophila melanogaster cDNA transcriptome (BDGP6) using kallisto. Differential expression analysis was performed using DESeq2 [shuf command in UNIX to randomly select 2.7 million reads from the EUd-RNA BAM files. Gene ontology analysis was performed using DAVID functional annotation clustering [Drosophila melanogaster gene set as background, high classification stringency and default settings for all other parameters. Unnamed genes (those identified only by CG number) were excluded from GO analysis. Only categories with an enrichment of ≥ 2.0 and Bonferonni-corrected p-values of < 0.01 were considered significant.Sequencing was performed on a HiSeq 2500. Sequence data were trimmed using ustering , with thDendrite morphology was analyzed in wandering larval stages. Larval fillet preparations were fixed using formaldehyde and stained with rat anti-mCD8 (ThermoFisher) at 1:100 followed by Alexa Fluor 488 anti-rat secondary antibody (ThermoFisher) at 1:200. Imaging was performed using a Zeiss LSM 880 confocal microscope. The number of branch termini and total dendrite length were quantified in Z-series projections. Branch terminal counting and dendrite tracing was performed using Adobe Photoshop and dendrite length (pixels) were quantified using Zeiss Zen Blue software. Dendritic field (% coverage) values were calculated by dividing the number of pixels in the dendrite trace by the total number of pixels in the area analyzed . All statistical analyses were performed in R. Normal distribution of the data for each genotype was confirmed using the Wilk-Shapiro test. Statistical significance was determined using ANOVA with Tukey HSD post-test.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


This includes inhibition of viral attachment and penetration, inhibition of viral RNA and protein synthesis, inhibition of viral key proteins such as 3-chymotrypsin-like cysteine protease (3CLpro) and papain-like protease 2 (PLpro), as well as other mechanisms including inhibition of the viral release and enhanced host immunity. We hope this compilation will help researchers and clinicians to identify the source of appropriate anti-viral drugs from plants in combating COVID-19 and, ultimately, save millions of affected human lives.The rapid outbreak of coronavirus disease 2019 (COVID-19) has demonstrated the need for development of new vaccine candidates and therapeutic drugs to fight against the underlying virus, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Currently, no antiviral treatment is available to treat COVID-19 as treatment is mostly directed to only relieving the symptoms. Retrospectively, herbal medicinal plants have been used for thousands of years as a medicinal alternative including for the treatment of various viral illnesses. However, a comprehensive description using various medicinal plants in treating coronavirus infection has not to date been described adequately, especially their modes of action. Most other reports and reviews have also only focused on selected ethnobotanical herbs such as Traditional Chinese Medicine, yet more plants can be considered to enrich the source of the anti-viral compounds. In this review, we have screened and identified potential herbal medicinal plants as anti-coronavirus medication across major literature databases without being limited to any regions or ethnobotanic criteria. As such we have successfully gathered experimentally validated  The outbreak originated from the Hunan seafood market in Wuhan, a main city of China which frequently sold live exotic animals such as bats, frogs, snakes, dogs, civets, and marmots , pigs , birds  and even human , SARS-CoV-2, Middle East Respiratory Syndrome-CoV (MERS-CoV), HCoV-OC43, HCoV-NL63 and HCoV-229E) with different disease severity . On Janu marmots . Althoug marmots . Most CO marmots .Nidovirales, family Coronaviridae and genus Coronavirus, which is comprised of single stranded positive RNA sense viruses with 29.7 kb in length (pro) and a papain-like protease (PLpro) (pro at three sites (yellow filled star) which release nsp1, nsp2, and nsp3, whereas the rest of the sites are processed by 3CLpro (red filled star) releasing nsp4 until nsp16 , followed by other structural proteins   . The proil nsp16 . Besidesil nsp16  (Du et ail nsp16 . The N pil nsp16  which isil nsp16 . Meanwhiil nsp16 . In geneil nsp16 . The virE and S) . EventuaE and S) .https://www.forbes.com/sites/moneyshow/2020/06/16/9-pharmaceutical-companies-racing-for-a-covid-19-vaccine/#3acd13c576ad). Current treatments used by physicians mainly depend on managing the symptoms such as breathing support using mechanical ventilation, and corticosteroids to reduce lung swelling, as well as the current available antiviral and antibiotics medications  AND  AND corona* in the search field. We have identified and included research studies starting from year 2004 to year 2020. Relevant papers were selected after critical evaluation on their significance and were further divided based on their experimental designs, whether n silico –3. Howevanalysis , are notanalysis  due to tThe inhibition of viral penetration and attachment is an effective way to curb coronavirus infection. Infectious virion binds to cell membrane receptors, permeates the cellular membranes and removes the virion's protein coat once entered into the cell cytoplasm, releasing viral nucleic acid  Smith e. Coronavd-glucose (TGG) and luteolin, isolated from Rhus chinensis Mill. and Veronica linariifolia Pall. ex Link, respectively, were identified to have the highest affinity to the S2 subunit of the virus, thus postulating that these compounds may interfere with the viral cell fusion process  to inhibitory concentration of the extracts or compounds that can cause 50% of virus inhibition (IC50). The higher the value of SI, the lesser cytotoxicity for the host and hence it being potentially safe to be applied as a future antiviral agent  also has been identified to inhibit viral attachment and penetration  was shown to inhibit wild-type SARS-CoV (wtSARS-CoV) infection and HIV/SARS-CoV S pseudovirus infections by inhibiting clathrin-mediated endocytosis pathway through TfR receptor  Kuntze also have anti-viral activity towards RNA and protein synthesis of the coronavirus. For instance, N protein expression of mouse hepatitis virus, MHV-A59 was reduced in Sanguisorba officinalis L. treatment, suggesting this plant extract might negatively affect the viral nucleocapsid formation (Stephania tetrandra S.Moore (Strobilanthes cusia (Nees) Kuntze leaf methanol extract strongly suppressed HCoV-NL63-infected cells with IC50 of 0.64 μg/ml (S. cusia (Nees) Kuntze  Kuntze  that exhectively   DC. with a comparable reduction in viral protein and MHV-A59 production (in vitro (Forsythia suspensa (Thunb.) Vahl has been identified to reduce the viral yield and decreased the expression of infectious bronchitis virus (IBV) N gene (in vitro at concentration of 0.64 mM (Staphylococcus aureus (Forsythia suspensa (Thunb.) Vahl is typically used to prevent inflammation, pyrexia and emesis in traditional Chinese medicine  N gene  in contr) N gene . Forsyth 0.64 mM   showed a marked reduction in RdRp activity  Nutt., Phellodendron chinense C.K.Schneid., Sophora subprostrata Chun & T.Chen, Phoradendron meliae Trel., and Coptis chinensis Franch. were also known to inhibit RdRp activity  is a replicase enzyme that is crucial for the transcription and replication of coronavirus . Since Ractivity   are responsible for the synthesis and maturation of the various viral polyproteins as shown in pro, many more studies have been conducted on 3CLpro, possibly as it was able to generate 12 important non-structural proteins (nsp 4 to nsp 16), including the viral RdRp (nsp 12) and helicase (nsp 13)  .In silico docking analysis has identified 13 herbal compounds such as coumaroyltyramine, cryptotanshinone, kaempferol, N-cis-feruloyltyramine, quercetin, and tanshinone IIa that are able to inhibit 3CLpro and PLpro (S. cusia (Nees) Kuntze  Kuntze , Table 1in vitro   such as sinigrin, beta-sitosterol, and indigo (pro (50 of 217 µM was found more effective than indigo compound (IC50: 752 µM) or beta-sitosterol  in inhibiting the cleavage processing of the 3CLpro in a cell-based assay  viruses  and interleukin-10 (IL-10)  . These fal phase .Euphorbia neriifolia L. and Pelargonium sidoides DC. were identified as having appreciable antiviral activity against human coronavirus infection. Several compounds such as 3β-friedelanol, 3β-acetoxy friedelane, friedelin, and epitaraxerol (E. neriifolia L. leaves exhibited potent anti-human coronavirus (HCoV) activity on human fibroblasts (MRC-5) infected cells, as opposed to the positive control, actinomycin D  Herb. extract also was identified as having potent anti-coronovirus activity based on a screening through an in vitro study (L. radiata (L'Hér.) Herb. that has been treated on SARS-CoV BJ-006 strain showed the highest SI (370-422) compared with the other plants such as Lindera aggregata (Sims) Kosterm. (SI: 16-17), Pyrrosia lingua (Thunb.) Farw. (SI: 55-59), and Artemisia annua L. (SI: 27-31) (L. radiata (L'Hér.) Herb. with an IC50 value of 15.7 nM  greater than 900 (ro study . Ethanol: 27-31)  (Li et a: 27-31) . Further 15.7 nM . This cothan 900  (Li et athan 900 . This fithan 900 .Toona sinensis (Juss.) M. Roem. (Chinese mahogany) have been identified to possess antiviral activity toward coronavirus infection too. T. sinensis (Juss.) M. Roem  is popular in Taiwan, China, and Malaysia, has also been tested for SARS-CoV inhibition in vitro analysis and the water extract was shown to inhibit SARS coronavirus replication with an SI of 17  showed inhibitory effects against IBV, the coronavirus that contributes to tremendous economic loss in the poultry industry around the world (H. cordata Thunb. water extracts have been shown to have an inhibitory effect on the IBV (In vitro and in vivo study of H. cordata Thunb. conducted on specific pathogen free (SPF) chicken embryos and chickens demonstrated inhibitory activity of more than 90% against IBV infection in Vero cells and chicken embryo kidney cells as well as reduction of more than 90% of apoptosis-inducing cell death resulting from IBV infection  or post-treatment (62.5 mg/ml given after IBV infection), curing more than 50% of the infected chickens , and Strobilanthes cusia (Nees) Kuntze (tryptanthrin), and F. suspensa (Thunb.) Vahl (forsythoside A) were able to inhibit the viral RNA and protein synthesis. Other compounds such as kaempferol, N-cis-feruloyltyramine, and quercetin have targeted and inhibited the viral proteases such as 3CLpro and PLpro, important enzymes for the co-translation of non-structural proteins. We also identified a few compounds that may act on the viral release mechanism such as through the 3a ion channel (emodin and kaempferol) as well as enhancing the host immune systems (E. koreanum Nakai and H. cordata Thunb. extracts). Last but not least, several plants such as L. radiata (L'Hér.) Herb., A. sativum L., E. neriifolia L., P. sidoides DC., and T. sinensis (Juss.) M. Roem. have also been discovered to have anti-coronavirus activity, yet their exact mechanisms in targeting the coronavirus infection are still unknown, thus becoming exciting candidates for the development of new anti-coronavirus agents. Perhaps the most promising extracts for this current pandemic are from L. radiata (ethanol extract and lycorine) and S. cusia  and I. indigotica (hesperetin) with excellent in vitro IC50 and SI values against human coronaviruses, as well as quercetin and kaempferol from various herbs as evidenced from in silico experimentation. However, more preclinical and clinical studies are needed to justify their use and efficacy against the current SARS-CoV-2 virus. Altogether, this compilation will aid researchers or clinicians to better evaluate some targeted plant extracts and bioactive compounds for an effective potential treatment against this devastating pandemic.A considerable amount of research strongly indicates that both excellently characterized and less familiar medicinal plants around the world, either crude extracts or bioactive compounds from these plants, are very convincing as therapy for the new emerging coronavirus infection. Several compounds such as TGG, emodin, glycyrrhizin, aurantiamide acetate, and caffeic acid have been identified to have inhibition on viral attachment and penetration. Besides that, several plants and compounds such as JR analysed, interpreted and reviewed the research articles as well as drafting the article. WA designed the research framework and critically revised the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This study was funded by UKM Research University grants, DIP-2018-001 and GP-2019-K019471.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilated convolutions are introduced into a specific convolutional layer of the MobileNet model. Without increasing the number of parameters, dilated convolutions are used to increase the receptive field of the convolution filters to obtain better classification accuracy. The experiments were performed on the Caltech-101, Caltech-256, and Tubingen animals with attribute datasets, respectively. The results show that Dilated-MobileNets can obtain up to 2% higher classification accuracy than MobileNet. Computer image classification is one of the research hotspots in the field of computer vision. It can replace human visual interpretation to some extent by analyzing the image and classifying it into one of several categories. Image classification research mainly focuses on image feature extraction and classification algorithm. The features are very critical to the image classification, but traditional image features such as SIFT , HOG 2]2], and NDeep learning technologies , 5 have To solve the above problems, more and more researches have focused on lightweight networks, which have fewer parameters and less computation while maintaining high accuracy. When analyzing the number of network parameters, Denil et al.  found thAlthough the parameters or computation of lightweight network is reduced, the accuracy of classification also decreases correspondingly. Therefore, by introducing the dilated convolution filter into MobileNet, a Dilated-MobileNet approach is proposed based on local receptive field expansion. Without increasing the parameters, the dilated convolution filter can make the network obtain larger local receptive field and improve the classification accuracy.Convolutional neural network usually consists of convolutional layer, pooling layer, and full connection layer , as showThe convolutional layer, which contains several convolution filters, extracts different features from the image by convolution operation. The convolution filters of the current layer convolute the input feature maps to extract local features and get the output feature maps. Then, the nonlinear feature maps can be obtained by using activation function.The pooling layer, also known as the subsampling layer, is behind the convolutional layer. It performs downsampling operation, using a specific value as output in a certain subregion. By removing the unimportant sample points from the feature map, the size of input feature map of the subsequent layer is reduced, and the computational complexity is also diminished. At the same time, the adaptability of the network to the changes of image translation and rotation is increased. The most common pooling operations are maximum pooling and average pooling.The structure based on convolutional layer and pooling layer can improve the robustness of the network model. The convolutional neural network can get deeper through multilayer convolutions. With the number of layers increasing, the features achieved through learning become more global. The global feature map learned at last is transformed into a vector to connect the full connection layer. Most of the parameters in the network model are at the full connection layer.MobileNet, as shown in N standard convolution kernels  layer, the depthwise convolution filters is expanded by dilated convolution filters with an expansion rate of 2, while the other layers remain unchanged. This approach does not increase the amount of computation and parameters nor does it change the size of the output feature map of any layer, as shown in D3-MobileNet sets the convolutional stride in first convolutional layer as 1 and replaces the standard convolution filters with dilated convolution filters by using an expansion rate of 2. After the convolution operation in the first convolution layer, it is normalized through batch normalization layer . Then, aIn terms of receptive field expansion, there are also different ways of expansion. For example, Sun W combined dilated convolution and depthwise separable convolution to form standard blocks for network construction . Their aI are h, w, and m, the convolution filter K is s × s, the output channel number is n, and the output feature maps O=K × I can be obtained by the convolution of I and K with no padding zeros and stride 1, as shown in the following formula:O represents the value of point  in jth output feature map, K represents the value of point  on channel i in jth convolution filter, and I represents the value of point  on ith input feature map. From Formula  × (w − s+1) × n and the number of parameters is s × s × m × n.In the standard convolutional layer, assuming the height, width, and input channel number of the input feature maps K of the same size and expansion rate of 2. So, we can get the output feature map Od by the following formula:When Dilated-MobileNet introduces the dilated convolution in the standard convolution layer, with feature map I, the dilated convolution is performed with no padding zeros by using convolution kernel s × s × m) × (h − s − (s − 1)(r − 1)+1) × (w − s − (s − 1)(r − 1)+1) × n, and the number of parameters is s × s × m × n. With no padding zeros, the computation of dilated convolution with expansion rate r > 1 is less than that of standard convolution, and the number of parameters is the same, but the receptive field of dilated convolution is larger than that of standard convolution. Under the convolution operation with padding zeros, the map size of the dilated convolution is the same as that of the standard convolution, both of which are h × w × n, and the computation and the number of parameters are the same too.So, the total computational amount of the dilated convolution layer is  represents the value of point  in jth feature map. Since the depthwise convolution filter has only one channel, K represents the value of point  on jth convolution filter and I represents the value of point  on jth input channel.When introducing dilated convolution filters to the depthwise convolution, the above feature maps s × s × n) × (h − s − (s − 1)(r − 1)+1) × (w − s − (s − 1)(r − 1)+1) × m, and the total number of parameters is s × s × m+m × n. It can be seen that the parameter of the depthwise separable convolution are reduced compared with the standard convolution:The total computation of the depthwise separable convolution is  × (w × s − r+1), thus can expand the receptive field without increasing the number of parameters and calculation amount.From the above analysis, it can be seen that the receptive field of the deep convolution kernel with expansion rate rk denotes the receptive field size of the kth layer, fk denotes the size of filter, and si denotes the stride of the ith layer. The receptive field of the first layer equals to the size of the filter. By using Formula  dataset, as shown in The experiments are under TensorFlow framework and the programming language is Python. The experimental server is equipped with an NVIDIA TITAN GPU. RMSprop optimization algorithm is used in the experiments. RMSprop is an adaptive learning rate method, which can adjust the learning rate. In the experiments, the initial learning rate is 0.1. Since the Xavier initialization method can determine the random initialization distribution range of parameters according to the number of inputs and outputs of each layer, we use it to initialize the weight coefficients. ReLU is used as the activation function in the experiments, and a total of 50,000 batches are trained, with 64 samples per batch.In the following experiments, all the results are the averages of 10 times experiments, and the best classification accuracy rates are in bold in the tables. As seen from It can be seen from In the above 3 kinds of experiments, the Dense1-MobileNet and Dense1-MobileNet based on dense connection also achieved good classification effect. The results of the experiments on caltech-256 dataset are slightly better than those of Dilated3-MobileNet and a little worse than those of Dilated1-MobileNet and Dilated2-MobileNet. The design idea of Dense-MobileNets is different from that of the Dilated-MobileNets, and the network structures are also different, so the two approaches can be used together in the practical application.3The memory-intensive and highly computation-intensive properties of deep learning approaches restrict their applications in portable devices. At the same time, the compression and acceleration of network models will reduce the classification accuracy. So, this paper uses the dilated convolution in the lightweight neural network (MobileNet) to improve the classification accuracy without increasing the network parameters and proposes three Dilated-MobileNet models. The experimental results show that Dilated-MobileNets have better classification accuracies on Caltech-101, Catech-256, and AWA datasets.In recent years, new lightweight networks, such as mobilenetv2  and mobi


Importantly, multiple cis-acting RNAs interact with trans-acting regulatory RNA-binding proteins (RBPs) and affect the MBFV lifecycle process, including viral replicase binding, viral RNA translation-cyclisation-synthesis and nucleocapsid assembly. Considering that extensive structural probing analyses have been performed on MBFV cis-acting RNAs, herein the homologous RNA structures are online folded and consensus structures are constructed by sort. The specific traits and underlying biology of MBFV cis-acting RNA are illuminated accordingly in a review of RNA structure. These findings deepen our understanding of MBFV cis-acting RNA biology and serve as a resource for designing therapeutics in targeting protein-viral RNA interaction or viral RNA secondary structures.Mosquito-borne flaviviruses (MBFVs) spread between vertebrate  and invertebrate (mosquitoes) hosts. The  In term (DENVG) , while b (DENVG) .cis-acting RNAs that contain a number thermodynamically stable and shape-conserved structural elements. At the same time, sequence and structural polymorphisms of cis-acting RNAs are observed across MBFVGs. The 5′-terminal cis-acting RNA contains two stem-loops , and the adjacent hairpin in the capsid-coding region (5′cHP)   (Supplem (5′cHP) . The 3′- domains , b, 2007 domains : Domain al MBFVs . Howevert manner , which fcis-acting RNA (5′-3′cirRNA) has been shown to position the flaviviral RNA-dependent RNA polymerase (RdRp) close to the transcription start site  that comprises three stems  characterized by the U bulge, top loop (5′TL) and side structure domain (SSD). Although the size and shape of the 5′SLA are highly conserved, sequence conservation isn’t restricted to the local stems and loop regions . For insAmpN2 cap (5′cap) is absolutely conserved in MBFVGs, which enables translation through the canonical cap-dependent translation initiation . Residuals D290 and R538 of DENV NS3 also have specific interactions with the 2nd G and 5th G, respectively, The 5′m7Gpppitiation . The additiation . Under titiation ; Table 2” primer . The ini” primer . DENV NSInterestingly, the 5′cap of viral RNA functions to subvert innate host antiviral responses through escape of IFIT-mediated viral suppression . In addicis-acting RNA and NS5 transferring to the 3′cis-acting RNA. However, the G-C base-pairings might increase the potential stability of 5′UFS, as in WNV and USUV  their putative interaction with dimeric proteins and (b) their requirement to sustain replication in a dual-host system of vertebrate hosts and invertebrate vectors. However, the latter hypothesis is proven. Deletion of DENV DB1 reduces viral replication both in human and mosquito cells. It’s intriguingly that deletion of DENV DB2 was greatly advantageous for mosquito infection, with low impact in human cells. Analysis of viruses obtained from infected mosquitoes showed selective mutations mapped in the DB2 . Importacis-acting RNA would affect the viral replication via a more stable 5′-3′cirRNA. The overlapping sequences within the 3′sHP regulate the equilibrium between the two alternative conformations of the genome -3′ terminated  were essential for virus replication but did not affect translation (cis-acting element in the 3′(-)SL more efficiently than the one in the 3′(+)SL for the synthesis of plus-strand JEV RNA  is observed, which are associated with viral replication, pathogenesis and cytopathicity  and insect-specific flaviviruses (ISFVs), possess differences in their host ranges. On one hand, structure organization and regulation mechanism of cis-acting RNA are more restricted to MBFVGs. On the other hand, the dynamics of host-virus interaction drive the heritable genetic diversity and structural polymorphisms on cis-acting RNA of MBFVGs.Mosquito-borne flaviviruse cis-acting RNAs of MBFVGs as follows: (I) MBFVG divergent 5′-3′cirRNAs regulate MBFV replication in an alternative conformation (II) MBFVG 5′cis-acting RNA compromises structurally homologous promoter and enhancer despite the low sequence identity (III) 3′cis-acting RNA has shown apparently group-specific elements exemplified by 3′DB and 3′sHP structure (IV) Structurally heterogenous 3′SL is endowed with a critical VRS. Additionally, sfRNA can regulate the host cell upon infection. Combining structural and sequence analysis, a large amount of structural heterogeneity between the bird-adaptable and non-bird-adaptable groups is observed. After that, further experiments are necessary to clarify the molecular biology of these structural differences across MBFVGs.We review the feature and biology of MZ conceived and wrote the manuscript, and made the figures. YD and WZ read and revised the manuscript. MW, RJ, DZ, ML, XZ, QY, YW, SZ, YL, LZ, YY, and AC contributed materials and analysis tools. SC contributed to figure modification and revised the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The coronavirus disease of 2019 (COVID-19) has caused an unprecedented global crisis. The etiological agent is a new virus called the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). As of October, 2020 there have been 45.4 million confirmed cases with a mortality rate of 2.6% globally. With the lack of a vaccine and effective treatments, the race is on to find a cure for the virus infection using specific antivirals. The viral RNA-dependent RNA polymerase, proteases, spike protein-host angiotensin-converting enzyme 2 binding and fusion have presented as attractive targets for pan-coronavirus and broad spectrum direct-acting antivirals (DAAs). This review presents a perspective on current re-purposing treatments and future DAAs. A new disease, coronavirus disease of 2019 (COVID-19), caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was announced in December, 2019 in Wuhan, China . Within The symptoms of COVID-19 range from asymptomatic to severe . Criticapro), the nsp5 main protease (Mpro) or 3C-like protease (3CLpro), the nsp12 RNA-dependent RNA polymerase (RdRp) and the spike protein are the focus of this review.The etiological agent of COVID-19 is SARS-CoV-2 . It is aAn effective antiviral should have a high potency and a high therapeutic index i.e., effective against the virus and yet non-toxic to the host. As a result, a high therapeutic index can be achieved with targets that are unique and do not have human homolog. An antiviral should have a high barrier to resistance. Due to the high mutation rate the virus will soon generate escape mutants that are resistant to the drug. A target that is conserved presents a high barrier to resistance. An antiviral should also be universal in its ability to target a population of viral variants. The holy grail is an antiviral that is pan-coronavirus and broad-spectrum for the current and future pandemics.RNA-dependent RNA polymerase is a unique target with no human homolog. The structure of RdRp resembles a right hand with thumb, fingers and palm subdomains . The palThere are two types of RdRp inhibitors: nucleoside analogs and non-nucleoside analogs . Nucleosmax of 4–9 μM. Apparently due to premature hydrolysis, GS441524 appears as the main plasma metabolite with a plasma half-life of 24.5 h and a Cmax of 500 nM. The active metabolite, GS443902, accumulates to a high intracellular concentration with a half-life of 40 h  in tissue cultured cells in the presence of GS441524 . These min vitro is much higher than that of Remdesivir  is an anti-retroviral used to treat human immunodeficiency virus (HIV) patients . It has  in vivo . When us in vivo .Azvudine was developed as an RdRp inhibitor in treating hepatitis C patients . In a smTriazavirin is a broad-spectrum purine nucleoside base analog developed by the Russians to treat flu patients . A smallin vitro against SARS-CoV-2 because SARS-CoV-2 encodes its own capping enzyme  is approved in the United States and Japan in 2018 for treating flu. It targets the virus polymerase complex to inhibit the endonuclease activity of the PA subunit, preventing cap-snatching and hence viral mRNA synthesis . It is vg enzyme . Neverthg enzyme .Despite being a unique and conserved target, the use of RdRp inhibitors in coronavirus therapy is particularly challenging due to the presence of an exonuclease (ExoN) activity . Unlike The efficacy of Remdesivir against SARS-CoV-2 may be explained by its resistance to ExoN . The actin silico virtual screening and molecular docking of libraries of FDA-approved drugs and natural compounds  exhibited promising virus inhibition in cell-based assay  is developed by the Russian and has been used in the treatments of influenza viruses in Russia and China for decades . Arbidol in vivo . The pri in vivo . Its mod in vivo . Arbidolvesicles . Clinicavesicles . Meta-anvesicles .The use of convalescent human plasma has shown promise in the treatment of COVID-19 . Howeverin vitro  and HR2, the transmembrane domain and the cytoplasmic tail . Fusion The S2 fusion domain presents a more attractive, druggable target than the S1 RBD by exhibiting conservation in sequence and function. This is reflected in the wide range of receptor usage among coronaviruses whereas fusion is a conserved mechanism among coronaviruses and across the virus family, making fusion an attractive target for pan-coronavirus and broad spectrum inhibitors. Compared to the S1 subunit which demonstrates 75 and 50% identity in the RBD and RBM between SARS-CoV-2 and SARS, the S2 subunit shows 88 and 100% identity in the fusion domain and fusion peptide . MoleculIn addition, small molecule inhibitors can be designed to target internal cavity. A druggable, highly conserved homotrimeric cavity is formed by the HR1, the central helix and the connector domain . An inneThe broad spectrum Arbidol is a fusion inhibitor in a number of viruses . ArbidolPeptidic fusion inhibitors suffer similar drawbacks as peptidic protease inhibitors: short half-life and poor oral bioavailability. Lipidation is a common strategy to increase the half-life of the peptide inhibitors. Enfuvirtide lipopeptide showed increased potency and half-life . The panCoronavirus disease of 2019 has caused an unprecedented global crisis. With the possibility of getting re-infection, doubt has been casted over the promise of a vaccine. Therefore, the race is on to find a treatment. With the risk associated with using steroids in the alleviation of symptoms in COVID-19 patients, the ultimate aim is to cure virus infection using specific antivirals. The viral RdRp, proteases, spike protein-ACE2 binding and fusion have presented as attractive targets for pan-coronavirus and broad spectrum inhibitors. This is key in combating not just the current pandemic but the many future pandemics to come.S-WC conceived the idea, researched and critiqued the literature, and wrote the manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.