Apis mellifera), the dopaminergic system has been implicated in an elaborate pheromonal communication network between individuals and in the differentiation of females into reproductive (queen) and sterile (worker) castes. Here we have identified and characterized a honey bee dopamine transporter (AmDAT) and a splice variant lacking exon 3 (AmDATΔex3). Both transcripts are present in the adult brain and antennae as well as at lower levels within larvae and ovaries. When expressed separately in the Xenopus oocyte system, AmDAT localizes to the oocyte surface whereas the splice variant is retained at an internal membrane. Oocytes expressing AmDAT exhibit a 12-fold increase in the uptake of [3H]dopamine relative to non-injected oocytes, whereas the AmDATΔex3-expressing oocytes show no change in [3H]dopamine transport. Electrophysiological measurements of AmDAT activity revealed it to be a high-affinity, low-capacity transporter of dopamine. The transporter also recognizes noradrenaline as a major substrate and tyramine as a minor substrate, but does not transport octopamine, L-Dopa, or serotonin. Dopamine transport via AmDAT is inhibited by cocaine in a reversible manner, but is unaffected by octopamine. Co-expression of AmDAT and AmDATΔex3 in oocytes results in a substantial reduction in AmDAT-mediated transport, which was also detected as a significant decrease in the level of AmDAT protein. This down-regulatory effect is not attributable to competition with AmDATΔex3 for ER ribosomes, nor to a general inhibition of the oocyte’s translational machinery. In vivo, the expression of both transcripts shows a high level of inter-individual variability. Gene-focused, ultra-deep amplicon sequencing detected methylation of the amdat locus at ten 5′-C-phosphate-G-3′ dinucleotides (CpGs), but only in 5–10% of all reads in whole brains or antennae. These observations, together with the localization of the amdat transcript to a few clusters of dopaminergic neurons, imply that amdat methylation is positively linked to its transcription. Our findings suggest that multiple cellular mechanisms, including gene splicing and epigenomic communication systems, may be adopted to increase the potential of a conserved gene to contribute to lineage-specific behavioral outcomes.Dopamine is an important neuromodulator involved in reward-processing, movement control, motivational responses, and other aspects of behavior in most animals. In honey bees ( Drosophila melanogaster lacking the ability to synthesize dopamine show reduced activity, extended sleep-time, locomotor deficits, abnormalities in arousal and choice, and are hypophagic  and other eusocial insects  the functions of the AmDAT and AmDATΔex3 proteins, and (iii) the interactions of AmDAT with several monoamines and cocaine. Taken together, our findings reveal a complex picture for AmDAT and its splice variant, including novel properties that may play a role in animal social interactions. The insights presented here suggest that multiple levels of cellular regulation, including epigenomic modifications and alternative splicing, may be modulating AmDAT activity to generate complex phenotypic and behavioral outcomes. As such, this work provides a foundation for unraveling how these regulatory networks recruit relatively simple and highly conserved molecules, such as neurotransmitters, to perform lineage-specific roles  and inserted into the oocyte expression vector pGEM-He-Juel. Sequences encoding versions of AmDAT and AmDATΔex3 tagged with the human influenza hemagglutinin (HA) epitope were synthesized by GenScript and inserted into pGEM-He-Juel. A HA-tag was inserted into the second extracellular loop of AmDAT via the introduction of the nucleotide sequence gcaggagcttatccatacgatgttcctgactatgcagcaggagct between positions 495–496 of the AmDAT coding sequence (resulting in a HA-tag – YPYDVPDYA – flanked by an “AGA” peptide spacer at each end). This insertion point was selected because the presence of a HA-tag at the equivalent position of human DAT (HsDAT) did not affect the protein’s transport properties, expression, and/or trafficking, nor did it interfere with the nearby N-glycosylation sites  was amplified from the pJTIon sites . By conton sites  and we oNotI or SalI (ThermoFisher Scientific) and 5′-capped complementary RNA (cRNA) was transcribed in vitro using the mMessage mMachine T7 kit (Ambion) and then purified with the MEGAclear kit (Ambion). The samples were adjusted to the desired cRNA concentration using RNase-free elution buffer (Ambion) and the quality of the cRNA assessed via agarose gel electrophoresis.The plasmids were linearized with EL2-AmDAT, or HAEL2-AmDATΔex3. For the expression of EmGFP and the Plasmodium falciparum nucleoside transporter 1 (PfNT1), 15.4 fmol of cRNA was injected (equating to 5 ng of EmGFP cRNA and 7.6 ng of PfNT1 cRNA). The oocytes were stored at 16–18°C in OR2+ buffer .Oocytes were harvested and prepared as described in full elsewhere . BrieflyEL2-AmDAT or HAEL2-AmDATΔex3 were fixed and labeled with antibodies 3 days post-cRNA-injection using a protocol detailed elsewhere (Oocytes expressing HAlsewhere . A mouseEL2-AmDAT and HAEL2-AmDATΔex3 protein levels in preparations of oocyte membranes was carried out using a protocol described in detail elsewhere (EL2-AmDAT and HAEL2-AmDATΔex3) or whole oocytes (EmGFP) were then separated on a 4–12% Bis-Tris SDS-polyacrylamide gel (Life Technologies) and transferred to a Protran 0.45 μm nitrocellulose membrane . The membranes were probed with a mouse anti-HA antibody  or a mouse anti-GFP antibody , followed by a horseradish peroxidase-conjugated goat anti-mouse antibody . The protein bands were detected by chemiluminescence (Pierce), quantified using the Image J software . Total protein staining was used to evaluate sample loading and efficiency of transfer; the membranes were rinsed with ultrapure water, stained with the MemCodeTM reversible protein stain kit (Pierce), and destained with ultrapure water. Densitometric analysis was performed using Image Studio Lite version 5.2 software (LI-COR). At least three independent experiments were performed (using oocytes from different frogs), and within each experiment the measurements were averaged from two independent replicates.The semi-quantification of HAlsewhere  with thelsewhere , with thsoftware , and exp3H]dopamine  and [3H]hypoxanthine  was measured 1–3 days post-cRNA-injection using a protocol described previously (3H]dopamine transport assays were conducted over 15 min at 27.5°C and in the presence of 0.86 μM unlabeled dopamine. The [3H]hypoxanthine transport assays were performed over 30 min at 27.5°C. The reaction buffer was either ND96 pH 6.0 ([3H]hypoxanthine transport assays) or ND96 pH 7.4 ([3H]dopamine transport assays) and contained 96 mM NaCl, 2 mM KCl, 1 mM MgCl2, 1.8 mM CaCl2, and 10 mM Tris–base supplemented with either 10 mM MES (pH 6.0 buffer) or 10 mM HEPES (pH 7.4 buffer). In all cases, at least three independent experiments were performed (using oocytes from different frogs), and within each experiment the measurements were made from 10 oocytes per treatment.The uptake into oocytes of dopamine by non-injected oocytes was very low; this represented the background level of [3H]dopamine accumulation , with the inclusion of the HA-tag having no effect on the protein’s ability to mediate dopamine uptake (p > 0.05). By contrast, neither AmDATΔex3 nor HAEL2-AmDATΔex3 caused an increase in the accumulation of [3H]dopamine within oocytes , L-Dopa , serotonin , and tyramine  were significantly smaller than the currents induced with dopamine  was not significantly smaller than that induced by dopamine. These results indicate that dopamine and noradrenaline are major substrates of AmDAT, with tyramine being a minor substrate. Octopamine, L-Dopa, and serotonin do not appear to be substrates of AmDAT.The successful expression of AmDAT at the oocyte surface enabled the use of electrophysiology to investigate the protein’s transport properties. The substrate-specificity of AmDAT was investigated by superfusing oocytes with seven potential substrates in the following order: dopamine, octopamine, L-Dopa, dopamine, tyramine, serotonin, noradrenaline, and dopamine. All currents are expressed as a percentage of the first dopamine current. The currents induced with octopamine  and the Hill coefficient n (2.4 ± 0.2) indicate, respectively, that AmDAT is a high-affinity, low-capacity transporter of dopamine and that it forms a dimer and operates in a cooperative manner.The kinetics of dopamine transport via AmDAT were determined by superfusing oocytes with a series of dopamine solutions of different concentrations. The superfusions were first performed in decreasing order of concentration, after which the same series was performed in reverse (and with the same oocyte). The relationship between the dopamine-induced current and the dopamine concentration is shown in n = 11; p < 0.001). The presence of cocaine decreased the current induced by dopamine to 61.6 ± 4.9 , whereas octopamine was without effect (data not shown). AmDAT-expressing oocytes that had been superfused with cocaine or a dopamine + cocaine mix retained their ability to produce dopamine-induced currents , consistent with cocaine binding to AmDAT in a reversible manner  and in oocytes expressing AmDAT (positive control) or AmDAT + AmDATΔex3. Two unrelated proteins – the Plasmodium falciparum nucleoside transporter (PfNT1) and the Emerald Green Fluorescent Protein (EmGFP) – were included as additional negative controls  between fluorescence intensity and EmGFP protein levels in oocytes injected with 0–10 ng of EmGFP cRNA , and oocytes co-expressing EmGFP with AmDATΔex3 , oocytes co-expressing EmGFP with AmDAT (co-injection control), oocytes co-expressing EmGFP with HA3H]dopamine transport activities and/or regulatory effects as their non-tagged counterparts; EL2-AmDAT with AmDATΔex3 caused a marked reduction in the level of HAEL2-AmDAT protein relative to that measured in oocytes expressing only HAEL2-AmDAT (the positive control)  . By contnjection  and was njection . Moreovearations , which imDATΔex3 . That isamdat and its splice variant in female castes and drones. These analyses confirmed our in silico findings that both the full-length transcript and amdatΔex3 are expressed in adult brains, antennae, and queen ovaries. However, the amdatΔex3 transcript level is generally very low relative to the level of amdat transcript , we used brains extracted from newly emerged bees and older foragers. As shown in amdat versus amdatΔex3 appears to be maintained at a similar level, with the full-length transcript always being the predominant form. Although we were unable to uncover a clear correlation between the level of amdatΔex3 transcript and a specific situation in this study, the finding that this splice variant is always present at low frequencies in the brain and in some other tissues  methylation in the two introns and in exons 10–11 was ∼5–8% of all sequence reads in the whole brain and ∼10% in the antennae . This robust transport signal allows detailed investigations of the protein’s transport properties. We found that AmDAT exhibits a high specificity for dopamine and noradrenaline transport, that it translocates tyramine as a minor substrate, and does not appear to transport octopamine, L-Dopa, or serotonin. The finding that the concentration-dependence of dopamine transport via AmDAT is sigmoidal suggests that the transporter undergoes allosteric substrate activation. These transport properties of AmDAT in Xenopus oocytes, which are similar to those of the Drosophila dopamine transporter DmDAT  have been reported and associated with variations in susceptibilities to neurobiological disorders  suggests it has a physiological role. Although the mechanism underpinning the regulatory activity of AmDATΔex3 remains unresolved, it is possible that its activity is not limited to the modulation of full-length AmDAT. For example, the splice variant of rat NET was found to downregulate the expression of several rat neurotransmitter transporters – including DAT, other monoamine transporters, and the GABA transporter type 1 – but appeared to have no effect on EAAT1   . Given tesponses , could bamdat locus is modified by the methylation of cytosines in two introns and one exon . Only two of the remaining 13 SLC6 transporter genes in the honey bee genome show signs of methylation  of the components of epigenetic machinery  in accordance with the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes.Ethical approval of the work performed with the VZ, RK, CL, and SR performed the experiments and acquired the data. RM conceived the original project. REM, RM, VZ, RK, CL, SR, and SB contributed to the design of work as well as the analysis and interpretation of the data. RM, REM, and SB supervised the study. VZ, SR, and RK contributed to drafting the manuscript. REM and RM wrote the 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.


A topological index of graph G is a numerical parameter related to G which characterizes its molecular topology and is usually graph invariant. In the field of quantitative structure-activity (QSAR)/quantitative structure-activity structure-property (QSPR) research, theoretical properties of the chemical compounds and their molecular topological indices such as the Randić connectivity index, atom-bond connectivity (ABC) index and geometric-arithmetic (GA) index are used to predict the bioactivity of different chemical compounds. A dendrimer is an artificially manufactured or synthesized molecule built up from the branched units called monomers. In this paper, the fourth version of ABC index and the fifth version of GA index of certain families of nanostar dendrimers are investigated. We derive the analytical closed formulas for these families of nanostar dendrimers. The obtained results can be of use in molecular data mining, particularly in researching the uniqueness of tested (hyper-branched) molecular graphs. There is a lot of mathematics involved in electrical and electronic engineering. Moreover, this depends on what area of electrical and electronic engineering: for example, there is a lot of abstract mathematics in communication theory, signal processing, networking and others. Networks involve nodes communicating with each other. A number of computers linked together form a network. Cell phone users form a network. Networking involves the study of the best way of implementing a network. Chemists are now equipped with a number of useful tools because of the merging branch of graph theory called chemical graph theory, e.g., molecular topological descriptors, indices and molecular topological polynomials. Many chemical structures and chemical compounds are usually modeled by a molecular graph to analyze underlying theoretical properties. A molecular graph is a pictorial diagram of the structural formula of a chemical structure in terms of graph theory, where the vertices represent the atoms of the given chemical compound and the edges represent the chemical bonds between the atoms. Cheminformatics is new subject which is a combination of chemistry, mathematics and information science. It studies Quantitative structure-activity (QSAR) and structure-property (QSPR) relationships that are used to predict the biological activities and properties of different chemical compounds. In the field of QSAR/QSPR research, theoretical properties of the chemical compounds and their molecular topological indices such as the Wiener index, Szaged index, Randić index, Zagreb index and ABC index are used to predict bioactivity of the chemical compounds. For more details about how to use the topological indices in predicating the bioactivity of chemical compounds, we refer the readers to consult ,2. For fA graph can be recognized by a numeric number, a polynomial, a sequence of number or a matrix. A topological index is a numeric number associated to a graph which completely describes the molecular topology of graph and this quantity is invariant under the automorphism of graphs. Among the interesting and most studied classes of topological indices in chemical graph theory are topological indices, which use the concept of distance called distance-based topological indices. The topological indices defined on the grounds of vertex degrees in a graph are called degree-based topological indices and the topological indices defined on the bases of counting are called counting related topological indices of graphs. Among these classes of topological indices described above, the degree-based topological indices have many more correlations to the chemical properties. In other words, a molecular topological index Top(G) of a graph is the number with the property that for each given graph H which is isomorphic to the graph G, we have Top(H) = Top(G). The concept of topological indices came from Wiener  when he Dendrimers are among the most complex chemical and interesting structures and hyper-branched macromolecules, with a precise tailored architecture. Dendrimers have gained a wide range of application in supra-molecular chemistry, particularly in host guest reactions and the self-assembly process. Their application in chemistry, biology and nano-science are unlimited. Recently, the topological indices of certain families of dendrimers have been investigated in ,13,14,15In this article, G is considered to be a simple and connected graph with vertex set om books ,16.Randić index denoted by The very first and oldest degree based topological index is  in 1975 . It is dOne of the well-known degree based topological indices is atom-bond connectivity (ABC) index introduced by Estrada et al. in , which wAnother well-known connectivity topological descriptor is the geometric-arithmetic (GA) index, which was introduced by Vukičević et al. in . It was The fourth version of ABC index is introduced by Ghorbani et al.  and defiRecently fifth version of GA index is proposed by Graovac et al.  and defiThus, the goal of this paper is to structurally characterize some dendrimers by investigations into these topological indices. The obtained results can be of use in molecular data mining, particularly in researching the uniqueness of tested (hyper-branched) molecular graphs.Recently, there has been a huge amount of research activity about the ple, see ,23. The ple, see . ABC4 anussed in ,21,24. Fussed in ,30,31,32In this section, we study the −24 (see ). DenoteIn the following theorem, the exact formula of Theorem 1. Consider the PAMAM dendrimer Proof. Let G be the graph of PAMAM dendrimer Now by using the partition given in After an easy simplification, we obtain:The following theorem computes the Theorem 2. Consider the PAMAM dendrimer Proof. Let G be the graph of the PAMAM dendrimer After an easy simplification, we get:Now we shall compute the In the following theorem, the exact formula of Theorem 3. Consider tetrathiafulvalene dendrimer Proof. Let G be the graph of tetrathiafulvalene dendrimer Now by using the partition given in After an easy simplification, we haveFollowing theorem computes the Theorem 4. Consider the tetrathiafulvalene dendrimer Proof. Let G be the graph of tetrathiafulvalene dendrimer After an easy simplification, we have:We shall now determine the ely (see ). FigureIn the following theorem, the exact formula of Theorem 5. Consider POPAM dendrimer Proof. Let G be the graph of POPAM dendrimer Now, by using the partition given in After an easy simplification, we getFollowing theorem computes the Theorem 6. Consider POPAM dendrimers Proof. Let G be the graph of POPAM dendrimers This implies that:After an easy simplification, we get:Molecular topology  has widely demonstrated its high performance in the discovery and design of new drugs. With this paper we seek to contribute to a better knowledge of molecular topology among mathematicians. Moreover: molecular topology can be employed to look for drugs that heal, in principle, any disease, based on the structural information provided by known active compounds. Once more, ‘pure’ mathematics comes to the rescue in practical problems, this time in the form of graph theory. If the book of nature is written with numbers, then molecular topology is certainly one way of reading it. Particularly, in this paper, we have considered some families of dendrimers, namely, PAMAM, tetrathiafulvalene and POPAM dendrimers, and studied their topological indices. The analytical closed formulas of the fourth version of atom-bond connectivity index


The filter bubble is an intermediate structure to provoke polarization and echo chambers in social networks, and it has become one of today's most urgent issues for social media. Previous studies usually equated filter bubbles with community structures and emphasized this exogenous isolation effect, but there is a lack of full discussion of the internal organization of filter bubbles. Here, we design an experiment for analysing filter bubbles taking advantage of social bots. We deployed 128 bots to Weibo (the largest microblogging network in China), and each bot consumed a specific topic (entertainment or sci-tech) and ran for at least two months. In total, we recorded about 1.3 million messages exposed to these bots and their social networks. By analysing the text received by the bots and motifs in their social networks, we found that a filter bubble is not only a dense community of users with the same preferences but also presents an endogenetic unidirectional star-like structure. The structure could spontaneously exclude non-preferred information and cause polarization. Moreover, our work proved that the felicitous use of artificial intelligence technology could provide a useful experimental approach that combines privacy protection and controllability in studying social media. A recent study found that social media has become the primary source for over 60% of users to obtain news . These uIn collective and individual levels, the term ‘polarization’ has two possible meanings. One is that like-minded people form exclusive clubs sharing similar opinions and ignoring dissenting views, i.e. opinion polarization ; the othThe formation mechanism of polarization and the method of restraining polarization are the most pressing issues in the field of information polarization ,4,7,8. MIn the chain from selective exposure to information polarization, the formation and evolution of filter bubbles play as critical a role as the amplifier ,10. TherAt present, research about online social networks relies mainly on observational methods ,7, whichet al. on Facebook was sharply criticized by a considerable number of scholars, who argued that scientific research should not interfere with national politics or users’ voting behaviour [Performing experiments on real-world social networks, while actively promoting innovation of sociology and communication studies, also brings about some serious problems . For insehaviour . TherefoIn this paper, we report an experimental approach for studying the polarization process by using social bots . We depl2.2.1.A social bot is a computer algorithm that automatically interacts with the social network environment . Social The social bot is based on two well-known hypotheses. The first is triadic closure . TriadicBased on the two hypotheses, the workflow of the bots includes five steps . (1) Ini2.2.V1:t, the probability can be quantified by the preferred topic ratio (R), which is the ratio of the amount of information matching the topic to the total amount of information from 0 to t. Since the running of each bot is not completely synchronized, we normalize the time scale from 0 to 1, where t = 0 represents the initial state after being given the initial user, and t = 1 represents the time when the bot ends the running.The probability that the bot is exposed to the preferred topic. For a given time V2:i, the word frequency (F) is defined as the ratio of the number of messages that contain the word (Ni) to all exposed preferred messages of the bot (Nt):The distribution of word frequency in the preferred topic. For a word V3:P). The preference of followers is judged according to their nickname as well as the user tags and content of their microblogs.The proportion of followings with the same preferred topic , where n is the number of nodes and m is the number of directed arcs) of a bot, including:  V4.1:in- and out-degree distribution.  V4.2:C) [T(x) is the number of directed triangles involved node x, degtot(x) is the sum of in-degree (degin) and out-degree (degout) of x and deg↔(x) is the reciprocal degree of x.directed clustering coefficient (C) :2.2Cx=T(  V4.3:D):Connection density (  V4.4:in) of nodes in personal social networks.The dyadic and triadic motifs. Motifs are the special sub-structures indicating connecting patterns and functions in complex networks ,34. MotiThe structure of the personal social network . The entertainment topic includes celebrity gossip, fashion, movies, TV shows, music, etc. and the sci-tech topic involves nature, scientific theory, engineering, technological advances, digital products, the Internet, and so on. Compared with other topics, the two topics can be freely diffused in Weibo, have low overlap in content, and possess different user characteristics  ,37. For Compared with the conventional method, our design is characterized by using social bots as the agents to conduct experiments in real online social media. The approach can be seen as a Monte Carlo simulation in a real-world environment by adopting an artificial intelligence algorithm. These bots act entirely on predefined hypotheses and algorithms. Therefore, our experiment can be regarded as a type of randomized assignment.2.3.The most significant feature of the social bots is that they can autonomously identify whether information matches their preferred topic. Therefore, the criteria of text classification are fundamental. We defined a classification standard for the two topics in our experiment .Table 1The classification criteria shown in 3.3.1.In social media, selective exposure of information source and information has been considered the primary mechanism of polarization. By comparing the preferred topic ratio in four experimental treatments, we can reveal the link between selective exposure and information polarization.t = 0) preferred topic ratio can be as high as a). Because the topic group and the random group have the same initial followings, they also have the same initial R for each topic. That is, most of the messages exposed to four groups are concentrated in the corresponding topics. Even so, the entertainment topic is more polarized than the sci-tech topic initially. The p < 0.01, two-sided Mann–Whitney U test, 34 bots). Importantly, no matter how many different topics are exposed to them after a random wake-up, the bots in topic groups only select a message consistent with its preference and follow the direct source of the selected news. It means that the initial (t = 0) preferred topic ratio does not affect the primary experimental process of choosing messages and following users of the bots. Finally, the initial difference between EG and STG provides a baseline for our further analysis.For two topics, since the initial users have been set as the users who mainly post or repost the corresponding topic, the initial ( average a. Becausb). First, at the end of the bots (t = 1), p < 0.01, two-sided Mann–Whitney U test, 34 bots). The difference between the two topics suggests whether selective exposure causes polarization to be dependent on the topic.Compared to the initial state, we are more concerned about the diversity of messages consumed by social bots after their social networks have formed b. First,c); however, PEG and PREG suggests that selective exposure can effectively filter the information source, making most of the information sources consistent with the preferred topic and thus resulting in a filter bubble. For the sci-tech topic, however, d). Based on the information diffusion mechanism of Weibo and the design of social bots, a low proportion indicates that users preferring the sci-tech topic have a high possibility of following users liking other topics, and diverse users diffuse the sci-tech content. The overlap of topics can suppress the effect of selective exposure in forming filter bubbles.The followings of a bot are the direct source of its news consumption. Our results showed that for the entertainment topic, the average proportion of nodes with the preferred topic  73.47%) c; howeve¯=14.51% d. Based a). The power-law distribution (y ∼ xα−) indicates that there are some dominant words with high word frequency in the text. Moreover, the dominance of a few words is more severe in EG than in STG (α = 2.23 for EG and α = 4.74 for STG). In the EG treatment, the maximum frequency of a word can be up to 0.4 on average, which means that the same word can be found in 40% of messages on average. However, in the STG treatment, the maximum word frequency is no more than 0.15 (b). Even among the top 10 words of EG, the first and second words are more dominant than other words. By analysing the top 10 high-frequency words of the two treatments, we find that the high-frequency words of EG are usually the names of certain entertainment celebrities and related specific words, while the high-frequency words of STG are some common words, such as ‘China’, ‘America’, ‘technology’, ‘market’ and ‘company’ (c).The polarization is also reflected on the semantic level. In both EG and STG treatments, the word frequency of the preferred topic exposed to the social bots presents a power-law distribution a. The pohan 0.15 b. Even acompany’ c.Figure3.2.From the results in the previous section, we found that EG and STG treatments exhibit different levels of polarization. Therefore, by comparing the differences between EG and STG personal social networks, the structural characteristics of the filter bubbles can be found, which may be one of the mechanisms that cause polarization.3.2.1.α = 1.766 for in-degrees and α = 1.847 for out-degrees) and are almost identical . Moreover, the two groups of networks also have similar connection densities (c). Interestingly, figure 4d shows that the average clustering coefficient of STG networks is 35% higher than that of EG networks . Given the low level of information polarization in STG, this is a counterintuitive result, as a dense community spreads diverse information.By roughly comparing the in- and out-degree distributions of EG and STG’s personal social networks, we find that both present power-law distributions . Due to the high number of possible motifs, we do not distinguish centrality in calculating triadic motifs. Figure 5d shows that, compared with EG networks, STG networks contain more closed triangles with at least two reciprocal arcs.The previous counterintuitive phenomenon can be attributed to differences in the connection pattern of personal social networks. First, we consider all possible motifs between two nodes (i.e. followings of bots) in the networks. By considering the in-degree centrality of nodes (i.e. the number of followers of the node), there are five possible configurations, which are shown in figure 5The abundance of motifs 2D, 3F and 3G confirm the counterintuitive result derived from the statistical properties of the network, that is, the STG network is more clustering but less polarized. The form of motifs explains this result. In STG networks, if each peripheral node consumes only a small number of messages on other topics, the information has a relatively high probability of exposure to the central nodes. Consequently, the preference of central nodes may be affected, and broadcast the diverse messages to its followers, making various information more widely spread within the whole community.3.2.3.The visualization analysis also shows similar results. If all non-reciprocal arcs are removed, we find that the STG networks still have a high connection density, and the original central nodes are still maintained, while EG networks are the opposite, with the sparse connection, and the original central nodes degenerate into peripheral nodes . Based oet al. [Furthermore, our results may confirm the basic structure and behaviour of the scientific community. The bidirectional scale-free structure of scientific collaboration networks has long been recognized ,40. Our et al. , the resIn sum, the controlled experiments based on neutral social bots help us to reveal the two basic structural characteristics of the filter bubbles in microblogging platforms. First, the filter bubble contains a high proportion of users preferring an exclusive topic. Second, filter bubbles have a unidirectional star-like social structure, in which the central nodes play only the role of the information source, and rarely receives information from other nodes. The combination of two characteristics leads to the emergence of information polarization.4.In this paper, we report a new controlled experiment approach to study polarization in social media. This method can be seen as a real-time Monte Carlo simulation on real-world social networks using the computer and artificial intelligence technology. (1) Compared with the experimental method based on volunteers , social Taking advantage of the approach, we can compare the consumption of different topics. Previous studies focused on the dissemination of different opinions in response to particular content, such as political news ,10,43,44


The complement system is integral to innate immunity, and it is an essential deterrent against infections. The complement apparatus comprises of >30 fluid-phase and surface-bound elements that also engage with the adaptive immune system, clear harmful immune complexes, and orchestrates several salutary physiological processes. An imbalance in the complement system's tightly regulated machinery and the consequent unrestrained complement activation underpins the pathogenesis of a wide array of inflammatory, autoimmune, neoplastic and degenerative disorders. Antibody-mediated rejection is a leading cause of graft failure in kidney transplantation. Complement-induced inflammation and endothelial injury have emerged as the primary mechanisms in the pathogenesis of this form of rejection. Researchers in the field of transplantation are now trying to define the role and efficacy of complement targeting agents in the prevention and treatment of rejection and other complement related conditions that lead to graft injury. Here, we detail the current clinical indications for complement therapeutics and the scope of existing and emerging therapies that target the complement system, focusing on kidney transplantation. Complement proteins account for 3 g/l of plasma and make up ~15% of the globulin fraction  (a) the The approval of the complement-targeting anti-C5 antibody, eculizumab, by the United States Food and Drug Administration (FDA) for the rare disease paroxysmal nocturnal hemoglobinuria (PNH) in 2007 has led to a renaissance in complement therapeutics . GPI serIn atypical hemolytic uremic syndrome (aHUS), complement overactivity leads to systemic thrombotic microangiopathy involving the kidney, central nervous system, heart, and gastrointestinal tract. In 2011, the FDA approved eculizumab for use in aHUS following a clinical trial demonstrating significant improvement in renal function with its use . HereditKidney transplantation is the therapy of choice for eligible patients with end-stage renal (kidney) failure. However, mismatches in human leukocyte antigens (HLA) between the donor and the recipient can cause the recipient's immune system to reject the transplanted allograft. The mechanism of immune injury in rejection has been the subject of intense research over the last few decades. This has led to a detailed understanding of the molecular processes involved in rejection, and it is now clear that the complement system plays a central role, especially in the pathogenesis of antibody mediated rejection (AMR). Several groups have recently published results of clinical trials exploring the role of complement blockade in AMR of transplant kidneys.Kidney transplantation is the treatment of choice for end-stage renal disease (ESRD) due to the superior long-term survival, cost savings to the health care system, and the greater quality of life it offers relative to dialysis , 13. HowPreformed circulating anti-HLA antibodies are present in up to 30% of patients awaiting kidney transplantation as a result of previous exposure to allo-HLA antigens from blood transfusions, pregnancies, and previous transplants and may cause immediate graft failure due to hyperacute rejection . The incThe advent of sensitive solid-phase Luminex platform bead-based assays for the detection of HLA antibodies has enhanced our ability to detect DSAs and diagnose AMR , 23. In The presence of preexisting DSA increases the risk of AMR and may adversely impact the graft survival even in recipients of negative lymphocytotoxic cross-match kidney transplants . C1q-binEnhanced understanding of the etiology of AMR and the prominent role played by the complement system implies a possible role for pharmacologic complement inhibition in clinical transplantation. The terminal complement inhibitor, Eculizumab, and C1 esterase inhibitor (C1-INH) have been the subjects of recent clinical trials exploring the potential of targeting the complement cascade in the prevention and treatment of AMR , 27, 28.DSA depletion by plasmapheresis combined with intravenous immunoglobulin (IVIg) is currently the mainstay of treatment for AMR . HoweverIn AMR, activation of the classical pathway is triggered by ligation of the C1 complex to HLA-antigens that are bound by DSA. Therefore, complement inhibitors targeting C1 can be leveraged to treat AMR while sparing the alternative pathway, thereby potentially averting infectious complications . AdditioC1 complex is a multi-molecular cluster constituted by the PRP, C1q, and two serine proteases, C1r, and C1s . Upon acSensitization to HLA is often a significant challenge to kidney transplantation . The calEculizumab's role in the prevention of AMR in HLA-incompatible kidney transplants was tested in an observational study of 26 highly sensitized patients by Stegall et al. . The ratP = 0.76)  .Post-hoc analysis demonstrated that the failure to show benefits in the treatment arm was likely related to trial design, inasmuch as there was discordance between the central and local pathology assignment of the diagnosis of AMR. When the central pathologists re-analyzed the biopsies with the clinical data that had been available to the local pathologists, the discordance went away, and the difference between the treatment and control groups became significant. Likewise, if all categories of AMR severity were assigned the diagnosis of AMR, the results were also significant. Most clinicians believe that the diagnosis of AMR is binary, and the grading system used in the study was not clinically relevant, again reinforcing a positive result to the study  to enter the trial. This had the effect of lowering rates of AMR in both study arms below expected based on prior reports in HLA incompatible transplant recipients /CRISPR-associated 9 (Cas9) technology, various groups have created genetically altered pigs to facilitate xenotransplantation . In addiMore recently, progress has been made by combining multiple glycan knockouts with human complement and coagulation regulatory gene knock ins . In 2016While currently available, transgenic knockout pigs may provide a solution to the problem of potent and immediate humoral responses due to pre-formed anti-Gal antibodies and allow transplantation. However, agents utilized in most of the successful preclinical primate studies do not have a track record of safety in humans. Therefore, to realize the goal of clinical xenotransplantation with sustained graft function in humans, tolerance induction is seen as a key element.Neisseria meningitidis and other encapsulated bacteria like Streptococcus pneumoniae have been well-documented in people with inherited deficiencies in complement components  and Shire ViroPharma; served as a paid consultant for Alexion, Shire ViroPharma and CSL, Behring; and received travel honoraria from Alexion and Shire VioPharma during the conduct of this study. He served on advisory boards for Genentech Scientific/ROCHE, True North/iPerian, Novartis, and Hansa Medical; received consulting fees from OrbidMed, GuidePoint Global, Sucampo, Astellas, and Shire; and received research grants from Immune Tolerance Network, ViroPharma, Hansa and Alexion. The remaining 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.


Whole-genome sequencing (WGS) has been vital for revealing the rapid temporal and spatial evolution of antimicrobial resistance (AMR) in bacterial pathogens. Some antimicrobialresistant pathogens have outpaced us, with untreatable infections appearing in hospitals and the community. However,WGS has additionally provided us with enough knowledge to initiate countermeasures. Although we cannot stop bacterial adaptation, the predictability of many evolutionary processes in AMR bacteria offers us an opportunity to channel them using new control strategies. Furthermore, by usingWGS for coordinating surveillance and to create a more fundamental understanding of the outcome of antimicrobial treatment and AMR mechanisms, we can use current and future antimicrobials more effectively and aim to extend their longevity. When antimicrobial drugs were introduced into clinical usage in the mid- 20th century, they had an astonishing impact on human health. Infectious bacteria that had threatened our survival were now at the mercy of a chemical arsenal. Previously fatal infections, from whooping cough and scarlet fever to tuberculosis and syphilis, were no longer considered a threat. Antimicrobials substantially reduced the risks associated with child birth, injuries, and invasive medical procedures. What has followed in the subsequent 70 years or so has been an uncontrolled microbiological experiment conducted on an unprecedented scale. Initially we identified a plethora of new antimicrobial classes targeting different essential bacterial functions, but we deployed them haphazardly in ever-increasing quantities. Now antimicrobial resistance (AMR) poses a genuine threat to human health, with the potential to return us to a situation where common infections are as untreatable as they were in the pre-antimicrobial era  and the genetic basis for resistance, which can be either intrinsic , mutation associated , or acquired via horizontal gene transfer (HGT) between organisms . The first reports of penicillin-resistant infections occurred early in the 1940s, but a penicillinase was described even before the continued clinical usage of the prototype antibiotic  in health care facilities in Europe (mec (SCCmec) element, as a consequence of the initial mass usage of penicillin . Notnicillin  (8).Mycobacterium tuberculosis during the treatment of individuals but that these are rarely transmitted , but all follow a similar basic trajectory, mirroring that observed in the recent MEGA-plate experiment (mec in MRSA. This suggests the AMR element(s) function as the “king maker” within the various pathogen populations, determining which clones dominate locally, regionally, and globally. Some mobile AMR genes have played this role in multiple organisms and clones; e.g., CTX-M-15 has driven the success of Escherichia coli ST131 and several Klebsiella pneumoniae clones   and the carbapenemases KPC and NDM-1  like SCCM. tuberculosis, in which resistance to isoniazid via a mutation in katG commonly precedes further AMR mutations , walKR mutations (vancomycin resistance) in S. aureus (lpx disruptions (colistin resistance) in Acinetobacter baumannii , but these have remained relatively localized. Why a combination of the KPC gene in the ST258 K. pneumoniae host background has been so successful remains an important unanswered question.Although AMR organisms arise continuously, national- and international-level WGS snapshots show that most AMR infections are attributable to a few clones within the broad population of the specific pathogen. Thus, only a small fraction of emergent AMR variants is sufficiently fit for broader dissemination. WGS investigations of Gram-negative opportunistic pathogens mimic the pattern of MRSA, with clonal spread that begins as localized expansions, rapidly progressing to intercontinental spread (within years) and even global dissemination (within decades). Particularly concerning is osporins . The arrE. coli ST131, whose global dissemination has been so rapid that its initial geographical origins were obscured  is probably the oldest multidrugresistant (MDR) hospital clone of A. baumannii and emerged in the 1980s after acquisition of a genomic island conferring resistance to all firstline antimicrobials. GC1 latterly accumulated resistance against fluoroquinolones and carbapenems . Moeminated  (21), wiNeisseria gonorrhoeae have been specifically linked to MSM communities  as being a major threat to human health  present particular complications for understanding AMR in community-acquired disease, as their transmission is driven by complex human behavior. AMR in STIs share the same general evolutionary characteristics as AMR in health care–acquired infections, but have distinct transmission, diagnosis, and treatment parameters that result in distinct spatiotemporal dynamics. AMR in STIs are a major concern; data from locations with good STI surveillance systems suggest a general upward trend in bacterial STI incidence disproportionately associated with specific communities , there is no evidence for the stable maintenance of these mutations during human infection . However, we are missing key epidemiological information on many STIs. In well-resourced clinical settings, there is a move away from microbiological culture as the “gold standard” for the diagnosis of bacterial STIs and increasing reliance on molecular testing , which is among the most common pathogens of humans and animals, are key for understanding AMR dynamics from a one-health perspective. In 2004, the Infectious Disease Society of America (IDSA) issued a report that presented a plausible catastrophic scenario of a highly fatal epidemic of MDR-NTS, illustrating how virulent AMR strains could rapidly escalate into major foodborne outbreaks threatening our food security. Indeed, large foodborne NTS outbreaks have been observed in recent decades, and NTS exhibiting resistance to last-line antimicrobials are beginning to be isolated.Humans are exposed to animal sources of AMR genes and bacteria through the food chain. The need for a “one-health”  strategy for AMR and infectious disease for surveillance and containment across the different sectors is well recognized. Nontyphoidal Salmonella Typhimurium (one of the most common types of NTS) as a cause of human infection personifies the one-health aspect of AMR and also highlights repeating patterns of AMR evolution. Antimicrobials have been used to treat and prevent infections in livestock since their discovery but were also used as growth promoters from the 1950s. In the early part of the 1960s, an increasing number of S. Typhimurium with transferable MDR phenotypes began to be isolated in the United Kingdom, with the first outbreak of MDR S. Typhimurium (phage type 27) in humans reported in 1959. This outbreak affected 102 patients; ~5% of isolates were resistant to streptomycin, sulfonamides, and tetracycline , which emerged in Europe in the mid-2000s, as highlighted by spread of the clone in France from 2008 . SeS. Typhimurium. This hypothesis was controversial, owing to the complexities of NTS epidemiology and the lack of molecular tools allowing high-resolution tracking of the incriminated bacteria in the different ecosystems. In the 1980s, epidemiology, combined with early molecular typing techniques, concluded that most AMR variants of NTS in the United States could be traced to animals . Such data sharing is essential to harness the power of genomic surveillance to identify and monitor evolutionary trends and population dynamics and to identify superfit AMR clones as they emerge and spread. The rapid pace of the global spread of AMR organisms, such as fluoroquinolone-resistant AMR is a truly global health problem, one that we cannot ignore or attempt to counter with increasingly powerful antimicrobial agents. WGS has allowed us to understand the dynamics of AMR and the chaos we have created through haphazard antimicrobial usage. The data are stark. However, recognizing the complexity and assessing the magnitude of the task ahead is the first fundamental step in tackling the global AMR crisis. We are now at a pivotal point, and what happens next is likely to dictate the future of infectious disease control. Genomics has outlined several repeating patterns in the emergence and spread of AMR bacteria, and although we cannot stop bacterial evolution, we can try to channel it. Through coordinated efforts, intelligent surveillance, and a more fundamental understanding of AMR mechanisms, we can learn to use antimicrobials more effectively and extend their longevity.


The latest animal neurophysiology has revealed that the dopamine reward prediction error signal drives neuronal learning in addition to behavioral learning and reflects subjective reward representations beyond explicit contingency. The signal complies with formal economic concepts and functions in real-world consumer choice and social interaction. An early response component is influenced by physical impact, reward environment, and novelty but does not fully code prediction error. Some dopamine neurons are activated by aversive stimuli, which may reflect physical stimulus impact or true aversiveness, but they do not seem to code general negative value or aversive prediction error. The reward prediction error signal is complemented by distinct, heterogeneous, smaller and slower changes reflecting sensory and motor contributors to behavioral activation, such as substantial movement (as opposed to precise motor control), reward expectation, spatial choice, vigor, and motivation. The different dopamine signals seem to defy a simple unifying concept and should be distinguished to better understand phasic dopamine functions. The complexity defies the notion of “one neuronal system equals one function” but likely reflects the workings of an evolutionarily ancient system that governs the individual’s requirements for survival.The question “What is dopamine doing?” keeps stubbornly popping up after the discovery of the brain’s dopamine system and its relationships to Parkinson’s disease, psychosis, and drug addiction. Although the efficacy of dopamine receptor–stimulating drugs in alleviating Parkinsonian movement disorders pointed initially to a mere tonic, modulatory role, it became increasingly clear that dopamine is a neurotransmitter not unlike other transmitters and has its own synapses and phasic activity related to stimuli and actions. The ensuing research efforts revealed an amazing array of heterogeneous functions at various time courses and levels of specificity that range from general behavioral activation to precise reward signaling for biological learning, machine learning, and economic choiceThis overview describes further conceptual, biological, and economic characterizations of the dopamine reward signal in animals from the past few years, its involvement in social processes, and its distinction from aversive, novelty, sensory, and motor processing. I will follow the notion that the function of an information-processing system can be defined by the relationship of its internal signals to behavior. This knowledge would provide a firm basis for investigating molecular, cellular, and circuit mechanisms. However, detailed descriptions of the recently elucidated fine network properties of dopamine neurons would exceed the topic and limits of this brief review, nor will I be able to discuss molecular signaling, human brain signals, and effects of lesions and systemic dopaminergic drugs that indicate tonic permissive rather than phasic driving influences.1.Rather than coding rewards and reward-predicting stimuli as they appear in the environment, phasic, sub-second responses in the majority of midbrain dopamine neurons code a reward prediction error. Their activity is increased for one hundred or two hundred milliseconds when a reward or reward-predicting stimulus is better than predicted, their activity is unchanged when these events have the same reward value as their prediction, and their activity is briefly depressed when these events have lower reward value than predicted6; conversely, optogenetic dopamine inhibition induces place avoidance and behavioral inhibition9. These behavioral effects likely reflect the elicitation of positive and negative reward prediction error signals, respectively. Recent research shows that these behavioral learning functions extend to neuronal learning: monkey dopamine neurons acquire stronger responses to an intrinsically neutral visual stimulus that is followed by optogenetic dopamine stimulation added to juice reward, as compared with a stimulus associated with only that reward  are first presented sequentially. Then reward occurs only with the later stimulus presented alone (B). Then the earlier stimulus (A) is tested for reward prediction. Indeed, dopamine neurons are activated by the test stimulus (A) although it had never been explicitly paired with the reward. Thus, the neurons access a reward representation via the test stimulus (A) that had earlier been associated with the then-unrewarded stimulus (B), defying the simple requirement for direct stimulus–reward contingency.Standard reward learning paradigms rely on the contingent association with a stimulus, whereas higher learning theories postulate a role for representations beyond explicit reward contingency. Dopamine neurons follow this latter notionThe reward prediction error response depends on both the reward and the prediction: reward received minus reward predicted. If we know the reward and measure the dopamine response, we can infer the prediction the neuron is accessing.14. A recent experiment confirmed this result in mice but tested also slightly uncertain rewards (probability ofP = 0.9). Here, the animal never knew for sure whether the reward would ultimately come and might increasingly expect none as time advances (like humans giving up waiting for an unreliable bus). But when the reward does occur, the prediction error and the dopamine response are higher the longer the wait was15. Thus, the dopamine response reflects access to reward predictions that are inferred from the temporal structure of reward probabilities rather than deriving entirely from the occurrence or omission of last rewards. Interestingly, reward-predicting responses in amygdala reflect also temporal reward probability16, indicating that reward neurons in general may access more sophisticated reward representations than hitherto assumed.The idea started with a stimulus sequence that always ends with a reward after a short but random number of steps. A monkey registering only repeated reward omissions would expect progressively less reward, but with experience it would know the reward would come more likely the longer the wait is (increasing hazard rate). Thus, with longer waiting, reward prediction increases and the error when the reward occurs decreases. Indeed, the dopamine response to the reward decreased during waiting, indicating that the neurons accessed the temporally increasing reward prediction derived from the overall task experience (rather than a decreasing prediction derived from the repeating reward omissions)20. Dopamine responses change their gain depending on the variance of the distribution21, suggesting access to at least the first two statistical moments of distributions. By contrast, with a predicted distribution of only two fixed reward amounts, something unexpected happens in mice: there is no response when either of the two predicted rewards occurs but a graded response in rare probe trials that tends to increase with the absolute difference to each of the two predicted rewards; the response is positive for amounts slightly above the lower reward, negative for amounts slightly below the upper reward, and zero for amounts right between the two rewards22. For an intuitive example, imagine a restaurant with two randomly alternating chefs with widely different ability: when the food is almost but not quite spectacular, we realize the good chef was cooking but may have overlooked something, thus generating a negative prediction error , even though the food was better than from the other chef and above the mean from both chefs. Thus, dopamine neurons access rich reward probability distributions via their statistical moments but can access individual elements when distributions are very restricted. As seen during waiting15 and reward reversal23, the reward predictions accessed by dopamine neurons derive not only from recent rewards but also from the overall reward structure of the environment.Reward predictions accessed by dopamine neurons derive from probability distributions of reward amounts. A larger reward compared with the expected value (predicted mean) of a predicted distribution activates dopamine neurons in monkeys, and a smaller reward induces a depression25. The chosen value response occurs to the stimulus and partly precedes and thus predicts the choice. In these straightforward tests, the animal chooses, with some stochasticity, between values that are firmly associated with the options. By contrast, in perceptual random-dot motion choice tasks, the value depends on the animal’s discrimination of motion direction, and the reward probabilities are not firmly associated with constant, unequivocally marked options. Higher motion coherence allows better discrimination and thus increases the probability of getting a reward. Thus, with higher coherence, reward value increases monotonically when choosing the correct motion direction but decreases monotonically when choosing the opposite, incorrect direction. Dopamine neurons in monkeys and mice show exactly this graded chosen value response during random-dot motion and contrast detection tasks27. The value responses before each choice derive from the combination of the animal’s stimulus assessment and the subjective probability of making a correct discrimination . As the targets are not distinctly marked for value, the responses cannot simply reflect the experienced reward probability for a given target.Perceptual choices help to further reveal what’s on dopamine’s mind. Dopamine responses to a set of choice options reflect the animal’s future choice. When a monkey chooses the more frequently rewarded option, the stimulus response is stronger compared with choosing the less often rewarded option, despite identical option presentation. As reward probability constitutes value, the neurons code “chosen value”  rather than the mean value of all options26. These representations or beliefs are parts of reward predictions that affect dopamine neurons, which report their deviation from the actual obtained primary and conditioned rewards as “reward prediction error”.Taken together, dopamine neurons have access to representations of future rewards that not only are associated with explicit stimuli but also derive from environmental factors like context, task structure, and time. These internal representations may be more globally called belief states and, when they reflect prior probabilities, Bayesian belief states19. This neuronal result aligns biological reward to economic choice and constitutes a prerequisite for understanding how individuals maximize utility for momentary and evolutionary benefit.Rewards don’t exist; they are made up by our minds. The third steak during a dinner is not attractive although it is pretty similar to the first two appetizing steaks. Plenty of other examples confirm that reward value is subjective and depends on non-physical factors like satiety, delay, and risk. While we can forever test individual cases of subjective value, economic theory provides concepts for understanding subjective value and preferences and predicting behavioral choices under various conditions, including risk. An example is the utility signal of dopamine neurons that transcends the ad-hoc coding of subjective value28. Stimulation at the reward-predicting cue has the opposite effect . This well-conceptualized situation, even with the restrictions imposed on an initial study, demonstrates that the dopamine utility signal has a very practical application; it affects daily consumer choice by influencing the value of a good. This beautiful result, outside the beaten path, suggests many follow-up experiments.But what would a dopamine signal for such a theoretical decision variable do in a real-world scenario? One of the most intuitive and reliable phenomena in economics is the price–demand relationship. As the price goes up, consumption goes down; people buy less stuff when it gets more expensive. But if the good becomes more valuable, demand increases, which shifts price–demand curves to the right. Price can be modeled as number of lever presses in rats, and value can be enhanced by dopamine stimulation, although further known factors affecting consumption may be too extensive for an initial, well-controlled study, such as availability of alternatives, time, and effort. How then would a dopamine economic value (utility) signal affect consumer choice? Indeed, inducing a positive dopamine reward prediction error signal by optogenetic excitation at the reward shifts the curves upward and rightward, indicating that the stimulation enhances value, thereby increasing demand at same price and maintaining same consumption despite higher price 28. Stim29. It seems that this disadvantageous reward inequity has negative reward value for dopamine neurons. Thus, dopamine neurons register everybody’s rewards but value them only relative to their host. Their primary concern with own reward resembles that of most reward neurons in the striatum30, some of which sense disadvantageous reward inequity31.Rewards are fine for me but may not be so great when somebody else receives them instead of me. Monkeys see it the same way; they value rewards more when they occur more frequently for themselves but not so much when they occur for another monkey, as shown by licking and binary choice. Dopamine neurons follow this social reward valuation; higher probability of own water reward elicits stronger responses, confirming standard reward value coding, whereas higher reward probability for the other monkey reduces own dopamine responses32 and constitutes a default signal for any potential reward in the environment; it is quickly replaced, before any behavioral action, by the subsequent prediction error component that codes reward value35; recent studies confirm this notion36. Thus, the initial, non-reward activation constitutes an integral part of the dopamine reward response. Its identification requires temporal resolution in the ten-millisecond range and is often difficult, in particular with unrewarded, value-less stimuli not allowing independent variation of sensory and reward parameters.Environmental rewards and reward-predicting stimuli contain a non-value component that impacts on sensory receptors, but their identification and evaluation take a few tens or hundreds of milliseconds. Dopamine neurons, in analogy to other neuronal systems, show an early unselective activation, which reflects sensory detection of the stimulus34. Second, it is elicited and enhanced by neutral or punishment-predicting stimuli that resemble rewards or occur in rewarding contexts39. Finally, it occurs with novel stimuli in humans, monkeys, and mice42. The novelty component decays during conditioning (due to repetition), whereas the reward-predicting component increases42. The unpredicted occurrence of an unrewarded picture and positive sensory prediction errors enhance the initial-component response but, in contrast to bidirectional reward prediction error coding, picture omission does not seem to elicit a dopamine depression in monkeys and rats43 45. Dopamine reward neurons are also activated by negative punishment prediction error, which has positive value (double negative)46, by rebound from aversive depression45, and by prediction of relief from punishment47, which is rewarding49. Thus, some of the recently reported activations by aversive air puff, sound, and foot shock45 might reflect rewarding relief from the threat these stimuli might pose to the animals, even if these neurons do not code standard reward.For 40 years, many studies, including our own, reported activations by aversive stimuli in some dopamine neurons . The physically less intense quinine is ineffective despite its behavioral aversiveness44, which argues for a contribution of physical impact and against general negative value coding.In contrast to these reward responses, recent studies report activations in dopamine subgroups in lateral substantia nigra, striatum tail, and ventro-medial nucleus accumbens shell in response to air puff, intense sound, and foot shock but not with physically less intense aversive quinine nor much with reward45, after 40 years of trying to nail them. If so, they might be parts of an ancient system detecting fear  rather than disgust (quinine)44 and contrast with the abundant reward-coding dopamine neurons that are depressed by aversive stimuli and code outcome value monotonically from negative to positive44. Dopamine neurons in fruit flies show similar response diversity—about 130 neurons code reward and 12 neurons code punishment50 suggesting preservation across a huge evolutionary range. So, ten years from now, will we know whether the dopamine activations by aversive stimuli reflect physical impact or aversiveness or maybe both?Thus, if physical impact remains an option for explaining activations by aversive stimuli, we might be dealing with the opposite tails of two continuous probability distributions: one for physical impact and one for value. Then dopamine neurons with activations by aversive stimuli might lie at the high end of the physical impact distribution, and their weak reward coding would be at the low end of the value distribution. On the other hand, despite all the caveats, optogenetics may have uncovered groups of dopamine neurons that are truly activated by specific punishers and thus differ qualitatively from reward-processing dopamine neurons1, such multifunctionality seems perplexing and gives rise to the question “What is dopamine doing?”Even though the common assumption of one brain system equals one function may not hold for dopamine55. Dopamine changes are associated with task events such as large contralateral or ipsilateral arm reaching movements , self-initiated arm movements (12%), reward delivery and mouth movements (9%), and full trial duration (5%). However, such changes are absent with more concise movements, such as well-controlled arm flexion-extension56, stereotyped reaching41, sluggish reaching elicited by offset of a stimulus57, and spontaneous and stimulus-driven eye movements57. The monitoring of large numbers of individual muscles in monkeys . By contrast, some motivation-related changes in striatal dopamine concentration are not associated with dopamine impulse changes in the soma67 and may derive from local presynaptic influences that have long been recognized75. The advent of dopamine voltammetry, molecular identification, optogenetics, and optical recording allows us to further characterize these behavior-related changes, associate them with different neuronal populations and their projection territories, and distinguish them from reward prediction error responses. Recent studies describe dopamine changes when rodents move in open fields, small chambers, levers, nose poke ports, T-mazes, running wheels, and trackballs36. The prediction error response stands out more; it is more phasic and has a higher instantaneous impulse rate and a shorter duration than the changes related to behavioral activation. These differences are particularly evident with the high temporal resolution of neurophysiological impulse responses. Nevertheless, the detection of prediction error responses requires explicit events that allow to identify predictions and to subtract their value from that of the reward. Analyses using reinforcement models help to further identify dopamine prediction error responses in elaborate tasks76.The amazing spectrum and heterogeneity of dopamine relationships to behavioral activation contrast with the rather stereotyped reward prediction error response that varies across neurons in only a single scalar parameter77, it is currently unclear how the heterogeneous relationships to behavioral activation might emerge from prediction error coding. One may dissociate the behavioral activation from prediction error coding by their respective spatial and non-spatial specificity78 or explain the dopamine voltammetry signal during movement and reward expectation by prediction error coding81, or behavioral activation and reward prediction error might be coded in different dopamine groups. In rodents, movement relationships are more frequent in substantia nigra dopamine neurons and their striatum-projecting regions, whereas reward prediction error coding is abundant in ventral tegmental area neurons and their nucleus accumbens projection68. These differences are gradual and do not constitute the strong medio-lateral midbrain or the ventro-dorsal striatum dichotomy seen in regional lesion experiments. Similar graded, rather than strict, differences are seen in monkeys, whose dopamine neurons in substantia nigra signal reward less frequently (<60%) than in ventral tegmental area (>70–80%)82; in corresponding striatal projection territories, reward expectation affects 40 to 50% of caudate and anterior putamen neurons and more than 75% of nucleus accumbens neurons83.How might these seemingly separate modes of dopamine action relate to each other? Despite attempts to derive a common activational roleThus, the notion of one neuronal system having exactly one function may not be valid for dopamine neurons, however hard we try. Maybe such an evolutionarily ancient system, which exists already in fruit flies, has multiple functions that are difficult to capture in a single term. A common denominator for the role of phasic dopamine activity might be to get the animal what it needs to survive, like detecting reward and coding the action for obtaining it (the two key components of motivation), although that sounds awfully superficial given the intricate complexity of the system.The investigation of dopamine function and the underlying networks are currently in full swing. The past several years have revealed many details that help us get a better understanding of dopamine function, and lots of mysticism has disappeared. We are not dealing with a system with clear-cut and well-parcellated functions, but we know that some of the dopamine functions are crucial for the animal’s survival. What we don’t know are at least two things.How does the dopamine reward signal, as the strongest component of dopamine function, get us the best reward and thus help evolutionary fitness? An obvious approach is to study economic decision-making, which has well-developed concepts for maximizing utility. This approach assumes that decision makers identify, process, and deliberate about all available options and have clear preferences, which underlies the first Von Neumann–Morgenstern utility axiom (“completeness”). But there are many exceptions to rational decision-making, and many decisions are not based on identifiable options. We often just do what we do without actively considering the alternatives. What is the role of dopamine neurons in these processes?84? And what about serotonin— would it have several, diverse functions86 but ultimately a coherent denominator? And what about acetylcholine? We have tons of work to do.As the investigation of dopamine function has revealed a number of important processes, then what are the other “neuromodulatory” systems hiding? Can we get a handle on norepinephrine after its attentional functions have been so well describedOf course, all of these processes may go wrong in brain disorders, which affect more than 20% of the population and present a major human challenge. For that reason, we should invest substantial portions of our wealth into all fields of neuroscience.


Molecular factors and pathways promoting human longevity and healthy aging can potentially delay or prevent multiple chronic diseases and conditions, but identifying such factors that can be pharmacologically targeted requires an integrated multidisciplinary approach. We describe the design of the five research projects and three cores of the Longevity Consortium (LC) and how their cooperative research is designed to discover molecular factors and pathways that can predict healthy human aging and longevity, associate with extreme human lifespan, show relevance to chronic age-related conditions, respond to interventions to slow aging in mice, and show evidence for association with lifespan across species. A systems biology approach is undertaken to identify common molecular features across human traits and organisms, and a chemoinformatics approach to link molecular targets to candidate healthy aging interventions. The LC results are made publicly available and we provide funding opportunities to the scientific community to support pilot projects.


Diverse peanut varieties are widely cultivated in China. However, few studies have investigated the effects of germination on the phenolic profiles and antioxidant activities of specific Chinese peanut cultivars. Therefore, this study was conducted to evaluate the effects of germination on total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity, and phenolic profiles of seven peanut cultivars in China. The TPC, TFC, and antioxidant activities were determined by spectrophotometry, while phenolic profiles were analyzed by using ultra-high performance liquid chromatography coupled with quadrupole/time-of-flight mass spectrometry (UPLC-QTOF-MS). The results found that germination significantly increased TPC, TFC, and antioxidant activity. Antioxidant activity was found to be closely related to TPC in germinated peanut extracts, which indicates that phenolics are the main contributors of antioxidants in germinated peanuts. In addition, germination induced significant changes in polyphenolic profiles. In the analyzed samples, 36 phenolic compounds were identified in which most were flavonoids. Overall, these findings highlight that germinated peanuts can be a good natural source of natural antioxidants for human consumption and functional food development. Peak 10 was tentatively identified as catechin-O-hexose because it gave a molecular ion [M–H]− at 451.1244 and a mass spectrometry (MS2) signal at m/z 289.07164, which is consistent with the loss of hexose, thus releases the catechin moiety −, and m/z 137.06046 −. Peak 32 had a molecular ion [M–H]− at 441.0826, which was associated with deprotonated catechin-O-gallate [m/z 163.03966 [M–ring B–O-galloyl]−, m/z 149.06054 −, m/z 131.04992 −, m/z 121.02923 −, and m/z 93.03425 [ring B–H2O]−.Peak 7 was tentatively identified as kushenol X due to its molecular ion [M + HCOO]m/z 461.10846 [M–CH3]−, m/z 443.10154 [M–CH3–H2O]−, m/z 435.12843 [M–acetyl]−, m/z 371.13447 −, 357.11921 −, m/z 327.10736 [M–ring B–O-acetyl]−, m/z 321.09529 −, m/z 315.1086 −, m/z 299.11352 −, m/z 281.10318 −, m/z 233.06661 −, m/z 181.05017 [M–ring B–allosepyranosyl]−, m/z 137.02418 −, and m/z 135.04472 −. Peak 21 was tentatively identified as naringenin-4′-glucoside-7-rutinoside with a molecular ion [M–H]− at 741.2263, according to its fragment ions and mass spectrometry database. The fragment ion at m/z 473.13097 was obtained by losing ring B and a methyl group from its parental ion, while the m/z 325.09298 was obtained by losing a rhamnosyl and ring B. Other fragment ions including m/z 445.13519 −, m/z 433.13527 , m/z 299.07755 −, and m/z 137.02411 −, could further confirm its structure. Peak 25 was tentatively identified as narirutin with a molecular ion [M–H]− at 579.1729, which was also detected by He et al. −, m/z 289.07185 [M–ring B–O-mannopyranosyl–2H2O]−, m/z 283.04641 −, and m/z 162.05562 [O-mannopyranosyl]−. Peak 31 was tentatively identified as eriocitrin with a molecular ion [M–H]− at 595.1671, which can lose ring B and two molecular water giving m/z 437.14277, or can lose rutinosyl obtaining m/z 285.03977. Other fragment ions at m/z 300.08585 −, m/z 161.04521 [O-mannopyranosyl]−, m/z 152.019098 −, and m/z 135.04415 − could further confirm that it was eriocitrin, which was also detected in Citrus aurantium L. var. amara Engl. −, m/z 271.02475 −, and m/z 243.03003 −. Based on the above molecular ion and fragment ions, peak 20 was tentatively identified as isoetin-O-hexose-O-xylose. Peak 26 was tentatively identified as apigenin-O-rhamnose-6’’-O-acetyl–glucoside, according to its molecular ion [M–H]− at 619.1643 and fragment ions. The molecular ion could lose an acetyl group giving m/z 577.15336, or lose ring B, O-acetyl, and two molecules of water giving m/z 435.12702, or lose ring B, acetyl, one molecule of water, and O-rhamnosyl giving m/z 300.02716. Other potential cleavage pathways further confirmed the potential structure of the compound, such as m/z 457.13497 −, m/z 325.09243 −, m/z 281.10271 −, m/z 163.03943 −, m/z 145.02891 −, and m/z 119.04974 −. Peak 28 was identified as isoscoparin-3’’-O-glucopyranoside with a molecular ion [M–H]− at 623.1614, which further lost ring B and two molecules of water give m/z 463.08797. The fragment ion at m/z 299.05591 corresponding to [M–3-O-glucopyranosylglucopyranoside]− and the fragment ion at m/z 285.04021 [M–3-O-glucopyranosylglucopyranosid–CH3]−, which further lost one molecule of water and gave m/z 269.04569. Fragment ion at m/z 306.09794 corresponded to [3-O-glucopyranosylglucopyranoside–H2O]−, and the fragment at m/z 301.03039 was obtained by losing ring B, O-glucopyranosyl, and one molecule of water from a molecular ion. This type of flavone was also reported in a previously published paper [Peak 20 was tentatively identified as isoetin-O-hexose-O-xylose because its molecular ion [M–H]n moiety . Other f− at 479.0985 and a fragment at m/z 432.08485 [M–H2O–CH2OH]−. The m/z 325.10441 was obtained by losing two molecules of water from −, while m/z 271.09352 was obtained by losing one molecule water from ring B, which further lost hydroxymethyl obtaining m/z 255.06350. The fragment at m/z 153.01842 corresponded to − of benzopyran. The compound also had fragment ions at m/z 123.04478 corresponding to the molecular ion of 2-methoxyl-phenyl, which further lost one methyl obtaining m/z 109.02871. Peak 15 and 19 had a molecular ion [M + HCOO]− at 785.2145, which was tentatively identified as kaempferol-3-O-(2G-α-L-rhamnosyl)-rutinose. The fragmented ion at m/z 597.18020 corresponding to loss of ring B and three molecules of water, m/z 472.18237 was [3-O-(2G-α-L-rhamnosyl)-rutinosyl]− and the fragment ion at m/z 443.15701 was −. The m/z 161.4529 corresponds to [O-rutinosyl]−. Other fragment ions can be obtained through proper cleavage pathways including m/z 542.10944 [M- O-rhamnosyl–methyl–H2O]−, m/z 395.04263 [M–CH2-O-rhamnosyl–rhamnosyl]−, and m/z 325.05632 [M–CH2-O-rhamnosyl–rhamnosyl–ring B]−. Peak 16 was tentatively identified as isorhamnetin-O- gentiobioside with a molecular ion [M–H]− at 639.1667. The molecular ion lost a methyl moiety, which gave a fragment ion at m/z 623.12088 or lost a glucopyranosyl obtaining m/z 477.10398. The fragment ion at m/z 465.10444 was obtained by losing ring B and three molecular water. Fragment ion m/z 315.05086 was obtained by losing gentiobiosyl and releasing isorhamnetin [m/z 300.02680. While the m/z 178.02665 was obtained by losing ring B and O-gentiobiosyl from the molecular ion. Peak 23 was tentatively identified as kaempferol-3-getiobioside with a molecular ion [M–H]− at 609.1458 and fragment ions at m/z 283.02432 [M–getiobiosyl]−, which was consistent with the molecular ion of keampferol −, m/z 243.02971 −, and m/z 151.00330 −. Peak 24 had a molecular ion [M–H]− at 609.1458, which was tentatively assigned as kaempferol-di-O-hexoside. The fragment ion at m/z 300.02716 was obtained by losing ring B, one molecular glucopyranosyl, and two molecular water from the parental ion, while the m/z 283.04641 was obt− at 445.1145 and fragment ions at m/z 287.05591 −, m/z 281.10175 −, m/z 167.03443 −, m/z 117.03423 −. According to the parental ion, fragment ions, and database, it was tentatively identified as prunetin-4’-glucoside.Peak 35 had a typical molecular ion [M–H]− at 741.1884 and fragment ions at m/z 515.17736 [M–glucopyranosyl–H2O]−, m/z 475.08874 [M–acetyl-β-D-glucopyranosyl–CH3]−, m/z 343.04591 [M–ring B–glucopyranosyl–2H2O]−, m/z 300.02761 [M–ring B–acetyl-glucopyranosyl-2H2O]−, m/z 163.03987 [M–ring B–glucopyranosyl–O-acetyl-glucopyranosyl]−.Peak 18 was identified as malvidin-O-(O-acetyl- glucopyranoside) -O- glucopyranoside with a molecular ion [M + HCOO]− at 147.0449 − at 319.1187 and fragments at m/z 245.08175 [M–CH3–CH3]−, m/z 217.5002 [M–2-methyl allyl]-, m/z 203.10720 −, m/z 185.05750 −, m/z 171.01148 [M–methoxyl–3-methylbut-2-enyl]−, m/z 165.05497 −, and m/z 151.03940 −. This compound has also been reported in Toddalia asiatica root bark [Peak 8 and 22 were tentatively assigned as dihydrocoumarin isomers due to its typical molecular ion [M–H]147.0449 , and fra− at m/z 153.0189 and fragment ion at m/z 109.18336 [M–COOH]−, which has also been reported in peanut skin extracts , one molecule of water, and phenylmethyl from the parental ion. Peak 3 with a molecular ion [M–H]− at m/z 133.0148. A fragment ion at m/z 101.02393 [M–H2O–H2O]− was tentatively identified as 2-hydroxy-succinic acid − at m/z 575.1731 and a fragment ion at m/z 437.14155 [M–phenyl–H2O]−. Peak 12 had a fragment ion at 149.04538 corresponding to [O-apiofuranosyl]−, a fragment ion at m/z 161.04525 corresponding to [O-glucopyranosyl]−, and a fragment ion at m/z 137.05051, which was associated with phenylethanol in addition to its molecular ion [M–H]− at m/z 431.1556. Based on the above analysis, it was tentatively assigned as hydroxyphenylethyl-O-apiofuranosyl-O-glucopyranoside. Peak 13 was tentatively identified as renifolin with a molecular ion [M + HCOO]− at m/z 397.1505 and fragment ions at m/z 269.10306 −, 263.0917 −, 173.09739 [M–O-glucopyranosyl]−, 162.05543 [glucopyranosyl]−, and 147.04470 −. Peak 14 was tentatively identified as tetrahydroxy- hydroxychalcone, according to its molecular ion [M–H]− at m/z 289.0716 and fragment ions at m/z 163.03974 [M–dihydroxyphenyl–H2O]−, 119.04989 [M–dihydroxybenzaldehyde–2H2O]−, 109.02898 [dihydroxyphenyl]−, and 93.03428 [hydroxyphenyl]−.Peak 1 was identified as protocatechuic acid with a molecular ion [M–H]nut pods . The fraIn conclusion, peanut germination induced compositions of phenolic compounds that varied markedly. Meanwhile, the total phenolics and total flavonoids were observed to significantly increase. These variations of phenolic compounds were responsible for the antioxidant activities of germinated peanuts. The positive correlation between phenolic contents and antioxidant activity has profound guiding significance for improving the efficacy of natural antioxidants. In addition, this study found that germinated peanuts of Xiaosilihong and Xiaosuangli cultivars overall had the highest TPC, TFC, and antioxidant activities, which indicates that these two varieties can be selected to produce peanut sprouts rich in antioxidant phenolics.


Increasing evidence indicates that long noncoding RNAs (lncRNAs) function as important regulators in biological processes and are dysregulated in various tumors. The lncRNA DANCR functions as an oncogene in various cancers, but elucidation of its role in pancreatic cancer (PC) requires further investigation. In the current study, we demonstrate that DANCR was increased in PC tissues and cell lines. Knockdown of DANCR significantly suppressed cell proliferation, migration, and invasion and influenced the levels of epithelial‐to‐mesenchymal transition‐associated proteins, as demonstrated by the observation of enhanced E‐cadherin levels and reduced N‐cadherin levels in PC cells. In addition, we identified direct binding to the predicted miR‐33b binding site on DANCR. We also showed that there is reciprocal repression between DANCR and miR‐33b. Furthermore, a miR‐33b inhibitor partially abrogated knockdown of DANCR and caused inhibitory effects. We also demonstrated that DANCR functions as a miR‐33b sponge to positively regulate MMP16 expression in PC cells. Collectively, the data reveal that DANCR exerts its function by regulating miR‐33b/MMP16 expression, implying an important role for a lncRNA–miRNA–mRNA functional network and suggesting a novel potential therapeutic target for PC. Long noncoding RNAs commonly exert their biological effects by functioning as sponges for miRNAs to counteract miRNA‐mediated mRNA suppression. Upregulation of lncRNA DANCR suppressed miR‐33b expression by binding with the miR‐33b, thereby increasing the expression of the miR‐33b target MMP16, and promoting PC migration and invasion. Our results reveal that DANCR promotes PC progression by functioning as a miR‐33b sponge to positively regulate MMP16 expression. DANCR‐Mtmutant DANCRDANCR‐Wtwild‐type DANCRECLelectrochemiluminescentEMTepithelial‐to‐mesenchymal transitionlncRNAslong noncoding RNAsMTT3‐‐2,5‐diphenyl‐2‐H‐tetrazolium bromidePCpancreatic cancerSDstandard deviationshDANCRshort‐hairpin RNA targeted DANCRPancreatic cancer (PC) is one of the deadliest malignancies in the world and is an aggressive cancer with almost uniform lethality Long noncoding RNAs (lncRNAs) are noncoding RNAs (> 200 nt) with diverse and largely uncharacterized biological functions et al. et al. and Jiang et al. The lncRNA DANCR (differentiation antagonizing nonprotein coding RNA) (NONCODE ID: NONHSAG037936.2) is located at human chromosome 4q12 and has been shown to be upregulated in various malignant tumors In the current study, we showed that DANCR levels were enhanced in PC cell lines and tissues. Downregulation of DANCR remarkably suppressed PC cell viability and invasion by negatively regulating the expression of miR‐33b. In conclusion, our data suggest that the lncRNA DANCR promotes PC progression and is a potential therapeutic target for PC intervention.pancreatic cancer tissues (30 pairs of PC tumor samples and their corresponding nontumorous tissues) from patients who underwent surgical resection at General Hospital of Ningxia Medical University were collected. None of the patients had received radiotherapy or chemotherapy before surgery. Written informed consent was obtained from each patient, and the experimental protocol was authorized by the Ethics Committee of General Hospital of Ningxia Medical University. A total of five PC cell lines  and the normal human pancreatic epithelial cell line HPDE6‐C7 were obtained from the Shanghai Cell Bank, Type Culture Collection Committee of the Chinese Academy of Science . The cells were cultured in Dulbecco's modified Eagle's medium or RPMI 1640 medium (containing 10% FBS) . The study methodologies conformed to the standards set by the Declaration of Helsinki.2-ΔΔCt method was performed to analyze relative expression.Total RNA was obtained from tissues and cells using TRIzol reagent in accordance with the manufacturer's instructions. cDNA was synthesized using the PrimeScript RT Reagent Kit . qRT‐PCR was performed using SYBR Premix Ex Taq II (Takara). Glyceraldehyde‐3‐phosphate dehydrogenase and small nuclear RNA U6 were normalized as endogenous controls. The −1 of G418 . After approximately 4 weeks, G418‐resistant cell clones were established.miR‐33b mimics, miR‐33b inhibitor, and their negative controls were obtained from RiboBio . To suppress the expression of DANCR, sequences of short‐hairpin RNA targeted DANCR (shDANCR) were constructed in a U6/GFP/Neo plasmid . The sequence used for shDANCR is as follows: 5′‐GGAGCTGAACTGCACTGTTGT‐3′. The plasmid carrying a nontargeting sequence was transfected and served as a control (shNC). Transfections were performed using the Lipofectamine 2000 reagent. At 48 h after transfection, stably transfected cells were selected using culture medium containing 0.5 mg·mLA pmirGLO luciferase expression vector  was used to construct the reporter plasmid. A wild‐type DANCR (DANCR‐Wt) reporter plasmid was cloned by inserting the fragment from DANCR containing the predicted miR‐33b binding site. A mutant DANCR (DANCR‐Mt) reporter plasmid was created by mutating the seed region binding site of miR‐33b. HEK293T cells were plated in 6‐well plates and were cotransfected with a luciferase reporter vector containing DANCR‐Wt or DANCR‐Mt fragments and miR‐33b mimics or a negative control. After 48 h, luciferase activity was assayed.An MTT ‐2,5‐diphenyl‐2‐H‐tetrazolium bromide) assay was used to detect cell proliferation. Transfected PANC‐1 or SW1990 cells were seeded in 96‐well plates, and cell proliferation was measured at different time points . Briefly, MTT solution  was added to the cells for 4 h, the medium was removed, and then, dimethyl sulfoxide was added. The absorbance at 570 nm was determined using a Quant Universal Microplate Spectrophotometer .Cells were added to 6‐well plates (500 cells/well) after transfection. After 2 weeks, the cells were fixed, stained, and then imaged and counted using a light microscope.4 cells were added into the upper chamber of a transwell insert, and medium (10% FBS) was added to the lower chamber. For invasion assays, chamber inserts were precoated with BD Matrigel and medium overnight under sterile conditions. Then, 1 × 105 cells were seeded in the upper chamber. After 24 h, cells on the top side of each insert were scraped off and fixed in methanol, and stained using crystal violet. Three random microscopic fields were counted for each group.For migration assays, 2 × 10Total protein was obtained from the cells. Protein lysates were measured using the bicinchoninic acid assay method, and the lysates were electrophoresed through SDS/PAGE and transferred to polyvinylidene fluoride membranes, which were blocked, incubated with primary antibodies overnight, and then incubated with a corresponding horseradish peroxidase‐conjugated secondary antibody. Antibody binding was visualized using an electrochemiluminescent (ECL) substrate. The primary antibodies used were E‐cadherin, N‐cadherin, and β‐actin . Protein bands were determined using an ECL detection kit .t‐test and one‐way ANOVA. Statistical analysis was performed using graphpad prism 5.0 . P < 0.05 was considered to indicate statistical significance.Experimental data are presented as mean ± standard deviation (SD) and were assessed using Student's In the current study, the levels of DANCR were first determined in 30 pairs of PC tissues and healthy adjacent samples. As shown in Fig. To determine the potential biological role of DANCR in PC cells, two PC cell lines, PANC‐1 and SW1990, with higher expression of DANCR were chosen to assess the effects of shRNA‐mediated knockdown of DANCR on cell proliferation and colony formation. DANCR‐specific shRNAs (shDANCR) were evaluated for their knockdown efficiency, and we showed that shDANCR had a higher silencing efficiency compared with a negative control vector (shNC) Fig. A,B. We oNext, we evaluated the role of DANCR in PC cell migration and invasion, which are critical factors in cancer progression and metastasis. Transwell migration assays revealed that knockdown of DANCR visibly suppressed the migratory activity of PANC‐1 and SW1990 cells when compared with the indicated controls Fig. A,B. TranTo explore potential molecular mechanisms, a target prediction program was used with the starBase v2.0 database to predict the possible targets of DANCR. Through the bioinformatics database, we found a seed sequence for miR‐33b in the DANCR mRNA Fig. A, consisGiven that DANCR can repress miR‐33b expression, we investigated whether miR‐33b could affect DANCR knockdown‐mediated inhibitory effects on PC progression. As expected, we observed that downregulation of miR‐33b expression abrogated the reduction in PANC‐1 cell proliferation and colony formation caused by shDANCR Fig. A,B. The To further explore whether DANCR functioned as a miRNA sponge to positively regulate mRNA expression in a ceRNA‐dependent manner, we predicted miR‐33b target sites in an online bioinformatics database and predicted that DANCR and MMP16 mRNAs contain the same binding site for miR‐33b (Fig. LncRNAs are involved in various cellular processes and are linked to the initiation and progression of malignant tumors Increasing evidence suggests that lncRNA DANCR functions as a tumor promoter in numerous types of tumor. For example, remarkably high expression of DANCR was observed in patients with colorectal cancer, and overexpressed DANCR increased HSP27 levels and promoted proliferation and metastasis through miR‐577 sponging To explore the effects of DANCR on the biological behaviors of PC cells, a series of biological experiments were performed. We showed that knockdown of DANCR significantly inhibited PC cell growth and proliferation. Furthermore, depletion of DANCR inhibited PC cells’ migratory and invasive abilities. These findings revealed that downregulation of DANCR had tumor‐suppressive effects in PC, suggesting that DANCR might contribute to PC progression.Various studies have suggested that lncRNAs can function as ceRNAs, abolishing the endogenous suppressive effect of miRNAs on their targeted transcripts et al. Furthermore, we explored whether the effects of DANCR in PC cells were affected by miR‐33b. Here, we revealed that a miR‐33b inhibitor rescued the suppressive effects that downregulation of DANCR had on PC cell viability and invasion. All our findings indicate that downregulation of DANCR inhibited PC cell viability and invasion through regulation of miR‐33b. However, the current study cannot exclude the possibility that DANCR might also affect other miRNAs or associated genes in PC, and this requires further investigation. In addition, we performed the online bioinformatics database and predicted that DANCR and MMP16 contain the same binding site for miR‐33b. Lin In summary, we observed that DANCR levels were remarkably reduced in PC samples and cell lines. Downregulation of DANCR expression reduced cell viability and invasion through upregulation of miR‐33b levels. These results suggest that DANCR is a key molecular marker in predicting prognosis and is an important target for PC therapy.The authors declare no conflict of interest.YYL and QW performed the experiments, and were responsible for data acquisition; they are co‐first authors of this study. LLT and JZ were responsible for data analysis and statistical analysis; they are co‐second authors of this study. JJS, WPB, and DL were responsible for data interpretation and visualization. YYL and QW were major contributors in writing the manuscript. YQH conceived and designed the study, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. ART made significant contributions to interpretation of data and revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.Fig. S1. DANCR directly interacts with miR‐33b in PC cells. DANCR expression was detected using qRT‐PCR in PANC‐1 and SW1990 cells transfected with miR‐33b or its negative control. Data are expressed as mean ± SD, Student's t‐test. *P < 0.05.Click here for additional data file.