E. coli, B. subtilis, and thermophilic archaeon T. adornatum. We find that (i) messenger RNAs preferentially interact with their cognate genes and the genes located downstream in the same operon, which is consistent with polycistronic transcription; (ii) ribosomal RNAs preferentially interact with active protein-coding genes in both bacteria and archaea, indicating co-transcriptional translation; and (iii) 6S noncoding RNA, a negative regulator of bacterial transcription, is depleted from active genes in E. coli and B. subtilis. We conclude that the RedC data provide a rich resource for studying both transcription dynamics and the function of noncoding RNAs in microbial organisms.Proximity ligation approaches, which are widely used to study the spatial organization of the genome, also make it possible to reveal patterns of RNA-DNA interactions. Here, we use RedC, an RNA-DNA proximity ligation approach, to assess the distribution of major RNA types along the genomes of  Mapping of RNA-DNA interactions in two bacteria and one archaeon using RedC, an RNA-DNA proximity ligation approach, uncovers major features that broadly characterize prokaryotic gene expression. In genomes of higher eukaryotes, various types of contact domains  and chromatin compartments) have been identified5, and numerous spatial contacts between promoters and remote regulatory elements have been documented7. Contact domains have also been found in prokaryotic genomes, although their functional significance is currently unclear8. The application of C-methods in metagenomics has improved metagenomic assembly and allowed the study of plasmid-microbe and phage-microbe associations11.The last two decades have been marked by the development of DNA-DNA proximity ligation approaches, known as C-methods, which enabled deciphering the spatial organization of the genome across all domains of life, from bacteria to humans17. Using RedC, an RNA–DNA proximity ligation technique developed in our laboratory, we identified noncoding RNAs associated with active and repressed chromatin in human cells and revealed the kinetics of co-transcriptional splicing16. We also combined RedC with the chromatin immunoprecipitation assay and identified noncoding RNAs involved in Polycomb targeting and establishing CTCF-dependent chromatin loops18.More recently, RNA–DNA proximity ligation-based methods have entered the scene, being proved useful in the analysis of the genomic distribution of various noncoding RNAs, such as XIST, MALAT, NEAT, RoX, enhancer RNA, and spliceosomal RNAs, in several eukaryotic species, including mammals, flies, and plantsHere, we used RedC to analyze the RNA–DNA interactomes of two bacteria and one archaeon. We studied the distribution of noncoding RNAs along the genome and demonstrated that in both bacteria and archaea, ribosomal RNAs are enriched at active protein-coding genes, indicating the coupling between transcription and translation. We have also shown that in bacteria, 6S noncoding RNAs are depleted from active protein-coding genes, which is consistent with their negative role in the regulation of bacterial transcription. Finally, we use RNA–DNA interaction data to trace the movement of polycistronic messenger RNAs along operons during transcription.16 , which reflects the presence of only one copy of 23S, 5S, and 16S rRNA genes in the T. adornatum reference genome. This increases the yield of uniquely mapped rRNA fragments. In contrast, the presence of seven and ten rRNA operons with a similar sequence in the genomes of E. coli and B. subtilis, respectively, sharply reduces the possibility of unambiguous mapping of rRNA fragments. As a result, rRNA accounts for 9, 8, and 82% of unique RNA–DNA contacts identified for E. coli, B. subtilis, and T. adornatum, respectively . The same is true for the RNA–DNA contact maps . Thus, we combined the contacts for the biological replicates and obtained the RNA–DNA contacts maps at 100 bp resolution  and approaches a median operon length (2.7 kb). This result appears to reflect the polycistronic nature of bacterial mRNAs. As a polycistronic transcript is held by a moving RNA polymerase until the end of an operon, the coding regions of this transcript will establish contacts not only with the corresponding genes but also with the genes located downstream in the same operon. The inspection of the RNA–DNA contact map confirms this model. The characteristic triangular structures are visible above the diagonal for operons transcribed in the sense direction . We reasoned that mRNA fragments captured near the gene may better convey the transcriptional activity of the gene than, for example, the total RNA output as determined by RNA-seq.In contrast to mRNAs, rRNAs and tRNAs demonstrate widespread distribution and are not markedly enriched at the place of their synthesis Fig. . The absE. coli  where the proteins encoded by these mRNAs are required, rather than staying close to transcription sites25. Notably, in eukaryotes, where transcription and translation are physically separated by the nuclear membrane, rRNA preferentially interacts with inactive genes, likely reflecting the synthesis of rRNA in the nucleolus around which the repressed chromatin is accumulated26.In agreement with our hypothesis, we found a positive correlation between the number of rRNA contacts and the transcriptional activity of protein-coding genes. The correlation is more evident for the top 10% of most active genes in 28, was found to be depleted from active protein-coding genes in E. coli . T. adornatum strain 1910bT was grown on strictly anaerobic modified Pfennig medium supplemented with 0.1 g/L of yeast extract, 1/100 (v/v) of sterile culture broth filtrate of Desulfurococcus sp. 1910a, and 1.0 g/L of glucose as the substrate under N2 in the gas phase at 80 °C and pH 5.75 to a final growth yield of 3 × 107 cells/mL.16 with minor modifications. We used 2.5 mL of an overnight culture of E. coli and B. subtilis and 300 mL of T. adornatum culture per experiment. Cells were centrifuged for 10 min at 3200 × g and room temperature for E. coli and B. subtilis and for 15 min at 7200 × g and room temperature for T. adornatum. Cells were washed with PBS, centrifuged again, and fixed in 3% formaldehyde (Sigma-Aldrich F8775) in PBS for 30 min at room temperature, followed by quenching with 375 mM glycine. Cells were centrifuged at 4 °C as above, washed with cold PBS, and centrifuged again.RedC procedure was performed as described previously in ref. g and 4 °C and resuspended in 500 µL nuclease-free water (Qiagen) supplemented with 1× protease inhibitors (Bimake) and 100 U SUPERase.In RNase inhibitor (Invitrogen) followed by adding 15 µL 10% SDS and incubating for 30 min at 37 °C with shaking at 1200 rpm. SDS was sequestered by adding 50 µL 20% Triton X-100 followed by incubation for 30 min at 37 °C with shaking at 1200 rpm. After adding 200 µL warm 4× NEB buffer 4, the insoluble fraction containing the crosslinked chromatin was pelleted for 10 min at 16,100 × g at room temperature and resuspended in 500 µL 1× NEB buffer 4. DNA was digested with 20 µL NlaIII  for 3 h at 37 °C with shaking at 1200 rpm.Cells were resuspended in 1 mL PBS supplemented with 1× protease inhibitors (Bimake) and 200 U SUPERase.In RNase inhibitor (Invitrogen) and mechanically disrupted using a bead beater homogenizer  in 2 mL Lysing Matrix A tube at 6.0 m/s for 25 s at room temperature. Cells were centrifuged for 10 min at 16,100 × g and 10 °C and resuspended in 150 µL 1× NEB buffer 2, followed by mixing with 300 µL AMPure XP magnetic beads (Beckman Coulter). Beads with immobilized chromatin were collected on a magnet, washed twice with 1 mL 80% ethanol, and air-dried for 1 min. 3′ P ends of RNA were dephosphorylated by resuspending bead-chromatin complexes in 142.5 µL dephosphorylation solution (1× PNK buffer (NEB), 0.1% Triton X-100, hereinafter the concentration is given as for the enzyme-containing mixture) followed by adding 7.5 µL PNK . The mixture was incubated for 30 min at 37 °C with shaking at 900 rpm. Bead-chromatin complexes were pelleted for 10 min at 16,100 × g and 10 °C and resuspended in 189 µL blunting solution (1× T4 DNA ligase buffer (NEB), 0.25 mM each dNTP). The mixture was supplemented with 5 µL DNA polymerase  and 6 µL Klenow , and DNA blunting was carried out for 1 h at 22 °C with shaking at 900 rpm. Bead-chromatin complexes were pelleted as above, resuspended in 200 µL nuclease-free water, and mixed with 200 µL AMPure buffer . Bead-chromatin complexes were collected on a magnet, washed twice with 1 mL 80% ethanol, and air-dried for 1 min. Bead-chromatin complexes were washed with 200 µL 1× NEB buffer 2 supplemented with 1% Triton X-100, pelleted for 10 min at 16,100 × g and 10 °C, and resuspended in 198 µL A-tailing solution . DNA ends were A-tailed by adding 1.5 µL Klenow (exo-)  followed by incubation for 1 h at 37 °C with shaking at 900 rpm. Bead-chromatin complexes were subsequently washed with 200 µL 1× NEB buffer 2 supplemented with 1% Triton X-100, with 200 µL 1× RNA ligase buffer (NEB) supplemented with 0.1% Triton X-100, and with 200 µL 1× RNA ligase buffer (NEB) by repeating resuspending/pelleting.Digested chromatin was pelleted for 10 min at 16,100 × g and 10 °C, resuspended in 200 µL nuclease-free water, and mixed with 165 µL AMPure buffer. Bead-chromatin complexes were collected on a magnet, washed twice with 1 mL 80% ethanol, and air-dried for 1 min. Bead-chromatin complexes were washed with 200 µL 1× RNA ligase buffer (NEB), pelleted as above, and resuspended in 95 µL PNK solution (1× T4 DNA ligase buffer (NEB), 0.1% Triton X-100). Then, 5 µL PNK  were added, and the mixture was incubated for 1 h at 37 °C with shaking at 900 rpm. Bead-chromatin complexes were pelleted as above, resuspended in 980 µL 1.02× T4 DNA ligase buffer (Thermo Scientific), followed by adding 20 µL T4 DNA ligase . DNA proximity ligation was allowed to proceed for 6 h at 22 °C with rotation followed by pelleting bead-chromatin complexes as above.The 3′ OH ends of RNA were ligated with 5′ rApp ends of the bridge adapter , 30 µL proteinase K  were added, and incubation for 1 h at 55 °C and then for 2 h at 65 °C followed. To precipitate RNA–DNA chimeras, 3 μL GlycoBlue (Thermo Scientific), 50 μL 3 M NaAC, and 550 μL isopropanol were added, and after overnight incubation at −80 °C, the mixture was centrifuged for 30 min at 16,100 × RNA–DNA chimeras (4 μg) were digested with MmeI in a 120-μL reaction containing 1× NEB buffer 4, 0.1 mg/mL BSA (NEB), 80 μM SAM (NEB), 0.1 μM ds oligo with MmeI site  were washed twice with 400 μL tween washing buffer (TWB)  by repeating the resuspension/magnet separation. Streptavidin beads were resuspended in 120 μL 2× binding buffer  and mixed with the solution after MmeI digestion followed by incubation for 15 min at room temperature to bind the biotinylated bridge to streptavidin beads. Streptavidin beads with tethered RNA–DNA chimeras were washed twice with 600 μL TWB, once with 100 μL 1× NEB buffer 2, once with 50 μL 1× First-Strand Buffer (Clontech), and resuspended in 38 μL reverse transcriptase solution (1× First-Strand Buffer (Clontech), 2.5 mM DTT (Clontech), 1 mM each dNTP, 1 μM switch template oligo , and resuspended in 48 μL DNA ligase solution (1× rapid ligation buffer (Thermo Scientific), 3 μM NN-adapter  as described previously in ref. 16 with minor modifications. Briefly, forward and reverse reads were subjected to the scanning of adapters, bridges, and GGG/CCC oligonucleotides. The DNA portion was extracted as the region of the forward read upstream of the bridge, the RNA 3′ portion was extracted as the region of the forward read downstream of the bridge, and the RNA 5′ portion was extracted as the region of the reverse read downstream of the first GGG. DNA portions of 18–20 nucleotides, RNA 3′ portions of ≥10 nucleotides, and RNA 5′ portions of ≥10 nucleotides were selected for the next mapping step. Before mapping, the end of the DNA portion adjoining the bridge was supplemented with CATG (the 3′ overhang produced by NlaIII digestion and then blunted) to reestablish the original sequence. “Reattaching” CATG to DNA portions increases their length to 22–24 nucleotides, which improves the yield of unique mappings.The raw RedC reads were processed using the RedClib computational pipeline . We retained only such DNA-RNA 3′-RNA 5′ triples that were all successfully and uniquely mapped to the reference genome. If one of the portions was missing, non-uniquely mapped, or unmapped, the read pair was filtered out. Finally, to avoid artifacts of intermolecular template switching of the reverse transcriptase, we discarded cases when RNA 3′ and RNA 5′ portions were mapped far apart from each other (>10 Kb) or to the same DNA strand. Supplementary Table DNA portions, RNA 3′ portions, and RNA 5’ portions were independently mapped to the CP025520, CP053102, and CP006646 reference genomes for E. coli, the GCA_013009385.1 assembly for B. subtilis, and the GCA_000446015.1 assembly for T. adornatum. We require that the RNA 3’ portion be mapped to the strand opposite to that of the gene, as expected from the RedC procedure. Finally, we combine DNA portions mated with RNA 3′ portions originating from a single gene, thus obtaining a whole-genome contact profile for each respective RNA.We use RNA 3′ portions retrieved from the forward reads as described above. We assign RNA 3′ portions to genes by the featureCounts program using the GCA_002848225.1 assembly for For each RNA, we calculate the number of contacts this RNA establishes in four consecutive genomic intervals: ±5 kb of the middle of the encoding gene; 5–50 kb upstream and downstream of the middle of the encoding gene; 50–500 kb upstream and downstream of the middle of the encoding gene; and >500 kb of the middle of the encoding gene Fig. –3. ContaWe select a set of RNA–DNA contacts such that the RNA portion is mapped to the proper strand of a protein-coding gene, and the DNA portion is mapped anywhere in the genome. We calculate the distance between RNA and DNA portions d1 = |rna – dna|, where rna is the mapping coordinate of the RNA 3′ end, and dna is the mapping coordinate of the end of the DNA portion adjoining the NlaIII site. Because the genome is circular, we calculate the distance d2 = |d1 – L|, where L is the genome length, and consider the lesser of d1 and d2 as the distance at which contact occurred.We next determine if contact occurred upstream or downstream of the RNA 3′ end mapping coordinate, with respect to the direction of transcription. A contact is assigned upstream in the following cases: RNA is transcribed in the sense direction, rna – dna >0, and d1 < d2; RNA is transcribed in the antisense direction, rna – dna <0, and d1 < d2; RNA is transcribed in the sense direction, rna – dna <0, and d1 ≥ d2; RNA is transcribed in the antisense direction, rna – dna >0, and d1 ≥ d2. A contact is assigned downstream in the following cases: RNA is transcribed in the sense direction, rna – dna <0, and d1 < d2; RNA is transcribed in the antisense direction, rna – dna >0, and d1 < d2; RNA is transcribed in the sense direction, rna – dna >0, and d1 ≥ d2; RNA is transcribed in the antisense direction, rna – dna <0, and d1 ≥ d2.We then assign the contacts to upstream or downstream intervals of increasing length: [0, 64), [64, 128), [128, 256), [256, 512), [512, 1024), etc. Finally, we calculate the contact frequency by dividing the number of contacts in the intervals by the length of the intervals.E. coli, 3453 of 4326 protein-coding genes for B. subtilis, and 1141 of 1375 protein-coding genes for T. adornatum).We determine the number of contacts established with a protein-coding gene by all rRNA molecules 16S, 23S, and 5S) or by 6S RNA . We note that the number of contacts of individual mRNAs with this region is proportional to the total number of contacts of these mRNAs with the genome  and plot a 24 × 24 RNA–DNA contact matrix for each operon. We then sum the contact matrices obtained for operons transcribed in the sense direction or for operons transcribed in the antisense direction.All experiments were performed in two biological replicates. Pearson correlation coefficient analysis of RNA–DNA contact matrices (Supplementary Figs. Further information on research design is available in the Razin_Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


RNA from patients was extracted by two automated standard and high-throughput protocols on the KingFisher™ Flex instrument. The isolated RNA was evaluated for concentration and purity by spectrophotometry, for structural and functional integrity by electrophoresis and expression of the RNase P gene, and usefulness in serotype-specific DENV detection by conventional and real-time RT-PCR. For the evaluation of ZIKV RNA, the commercial TaqMan Triplex® assay was used, along with a well-tested in-house RT-qPCR assay. The concentration of RNA  and the number of integral bands (9 vs. 11) were higher with the high-throughput protocol. However, the number of specimens serotyped for DENV by RT-qPCR was comparable for both protocols. The cycle thresholds of the TaqMan Triplex® commercial kit and the in-house assay for the detection of plasma ZIKV RNA isolated with the standard protocol showed a strong association  and a Cohen Kappa index of 0.98 when all 249 samples were analyzed. These preliminary results suggest that automated instruments could be used in studies of cocirculating flaviviruses that have represented a public health problem in recent decades in Colombia. They boast advantages such as efficiency, precision, time savings, and lower risk of cross-contamination.The isolation of nucleic acids is a critical and limiting step for molecular assays, which prompted the arrival in Colombia of automated purification instruments during the SARS-CoV-2 pandemic. The local application of this technology in the study of tropical diseases, such as dengue and zika, is beginning to be tested. We evaluated the efficiency of the automated extraction of viral RNA for studies of pediatric dengue and zika. Clinical samples of children with dengue that were well characterized through RNA isolation by silica columns and serotype-specific nested RT-PCR (DENV-1  Since its description in December 2019, SARS-CoV-2 infection has caused hundreds of millions of infections and millions of deaths, in addition to a devastating impact on the global economy . Both thhttps://www3.paho.org/data/index.php/es/temas/indicadores-dengue/dengue-nacional/9-dengue-pais-ano.html). Currently, another large DENV epidemic is occurring, and up to August of 2023, more than 3 million symptomatic infections have been registered in the Americas.Dengue and zika are emerging and re-emerging viral diseases that have affected tropical areas for years without any of the measures taken have stopped the growth in cases, especially dengue in the last decade. In fact, in 2019, the Americas suffered a large dengue virus (DENV) epidemic, with more than 3,100,000 cases of symptomatic infections and 1,770 fatal cases, 90% in children . ColombiThe efficient and adequate extraction of RNA is a critical and limiting factor for the diagnosis and study of viral infections, and many of the failures of downstream molecular studies are due to problems in RNA purification . RNA is The efficiency of this technology in the study of DENV and ZIKV infection in children has not been proven and their performance will have a high impact on public health. Here, we evaluate the efficiency of automated RNA extraction applied to the identification of DENV and ZIKV in pediatric natural infection and spiked experiments, respectively.n = 7), DENV-2 (n = 5), and DENV-negative infections (n = 8). To perform serotype-specific DENV nested RT-PCR, RNA isolated by silica columns and by centrifugation through the QIAamp® Viral RNA mini-Kit  was used, following the manufacturer's instructions. The manually purified RNA had a median (range) concentration of 42.9 ng/µL (9.4–496) and a λ260/λ280 nm ratio of 3.2 (1.6–3.9), as evaluated by spectrophotometry.Twenty plasma samples that had been collected from febrile pediatric patients (younger than 14 years old), and were previously characterized to have confirmed or rejected DENV infection by both a conventional nested RT-PCR  and ELISin vitro known amounts of ZIKV strain PRVA BC59 previously titrated by a focus-forming assay [For the ZIKV analyses, automated RNA isolation was performed on 249 plasma samples from healthy pediatric patients taken between 2012 and 2014 (before ZIKV had come to the Americas). For the spiked experiments, 40 samples were added to This study followed all the ethical standards in research in the Helsinki Declaration and was approved by the Ethics Committee of the Facultad de Salud of Universidad Surcolombiana and the Ethics, Bioethics, and Research Committee of the Hospital Universitario de Neiva .µL of proteinase K was placed in each well of the extraction plate, and 200 µl of plasma was added. Then, 275 µl of the extraction mixture containing binding buffer (265 µl) and the magnetic beads (10 µl) were added. In an automated manner, the equipment agitated the plate for 5 minutes and performed a first wash with 500 µl of wash buffer, followed by 500 µl of 80% ethanol and a final wash with 250 µl of 80% ethanol. After drying the beads for 5 minutes at 72°C, the purified RNA was collected in 50 µl of elution buffer. The high-throughput protocol is recommended for the isolation of RNA from plasma. The second protocol tested was MVP_2Wash_200_Flex, hereafter called “standard.” Unlike MVP_FLEX, the standard protocol has no last washing step with 80% ethanol. This modification reduces the amount of supplies and reagents and shortens the extraction time of 96 clinical specimens to 25 minutes from the beginning of the mounting of the plate in the equipment. During the COVID-19 pandemic, this protocol was recommended by the manufacturer for the isolation of RNA from nasopharyngeal swabs preserved in a viral transport medium for the diagnosis of SARS-CoV-2 by RT-qPCR [For RNA purification, the commercial MagMax™ Viral/Pathogen II kit  and the automated purification instrument KingFisher™ Flex  were used. Two purification protocols programmed in the instrument were evaluated. The first one was called MVP_FLEX, henceforth called “high-throughput” . Briefly RT-qPCR .The automated isolates included culture supernatants from Vero-76 cells that had been mock-infected (negative control) or a mix of supernatants of Vero-76 cells infected with DENV-1 or DENV-2 serotypes (positive control). In some experiments, ultrapure water was included as a no-template control (NTC). The purified RNA was immediately used to perform the assays or, in some cases, frozen at −80°C.λ 260/280 nm), as previously reported [The concentration and purity of the isolated RNA were evaluated by spectrophotometry with a NanoDrop 2000  following the manufacturer's instructions. To establish the purity, the absorption ratio of the RNA was obtained at wavelengths of 260 and 280 nm .The appearance of the total purified RNA migrating in an electrophoretic field was used to evaluate the integrity of the RNA. A 2% agarose gel was prepared, and 5 µM. Amplification was performed using the SuperScript™ III Platinum™ One-Step qRT-PCR System , following the manufacturer's recommendations. Of this mixture, 20 µL was placed in each well. Subsequently, 5 µL of the purified RNA was added, obtaining a final volume of 25 µL. The following controls were included in the plate: NTC, mix Vero-76 cells DENV-1- and DENV-2-infected (positive control), and mock-infected (negative control). The plate was amplified in the QuantStudio™ 5 thermocycler  and the amplification was performed with the following thermal profile: 15 min at 50°C, 2 min at 95°C, 15 sec at 94°C, and finally 30 sec at 58°C, the last two repeating 42 cycles.To evaluate the presence and quality of the purified RNA, the constitutive expression of the ribonuclease P (RNase P) gene was measured. For this, specific primers, and probes  , were prµL of the isolated viral RNA was added. The assembly was enhanced on the Eco™ Illumina or QuantStudio™ 5 instruments with the following thermal profile: 20 min at 50°C; 2 min at 95°C; and 40 cycles of 15 seconds at 95°C and 1 minute at 60°C. In all experiments, a mixture of culture supernatant from Vero-76 cells infected with each of the DENV serotypes or supernatant from uninfected cells was used as a positive and negative control, respectively.The utility of purified RNA for the detection of DENV-1 and DENV-2 was tested by conventional RT-PCR and RT-qPCR. For the first case, a widely used conventional nested RT-PCR was used . To deteµL that was used for purification with the automated protocol. To corroborate the specificity of the automated isolation, 209 plasma samples from healthy volunteers taken before 2015, to whom ZIKV was not added in vitro, were also included for automated RNA purification.Spike experiments were used to detect ZIKV in human plasma specimens. The supernatant of Vero-76 cells  infected by ZIKV strain PRVA BC59 was titrated by focus formation assays , then adFor the detection of ZIKV, the commercial TaqMan™ Zika Virus Triplex® Kit , kindly gift from ARC Análisis de Colombia S.A.S, was used as gold standard following the manufacturer's instructions. This commercial multiplex RT-qPCR kit is widely used for the identification of arboviruses. This assay detects DENV, ZIKV, and CHIKV arboviruses in a single well. To detect ZIKV by a second RT-qPCR assay, we followed a previously described and widely used test .r, Pearson test) was calculated. The Cohen Kappa index was performed to compare the adjusted agreement of the detection among molecular methods. P < 0.05 was considered significant in all analyses.Statistical analysis was performed with GraphPad Prism version 7 software . Nonparametric statistical tests were run, and medians and ranges were reported. For the analysis of two independent groups, the Mann‒Whitney test was used. As a measure of association, the association coefficient . The purities obtained with the two automated protocols were generally acceptable (median 260/280 nm ratio >1.71) and similar to each other, regardless of the protocol used   .P=0.3761, Fisher's exact test). The intensity of the bands present was regularly higher with the use of the high-throughput protocol compared to the standard  among both groups  and Vero-76 cells and analyzed the RNase P through RT-qPCR with the same primers and probe sequences. As shown in Supplemental To test the presence and functionality of the RNA isolated automatically from clinical pediatric samples, we analyzed the expression of the RNase P gene. As shown in In summary, with automated purification, was noted an efficient amplification of a widely used human constitutive gene to evaluate the presence and quality of purified RNA.tThe most important test of the functionality of isolated RNA was its usefulness for the detection of serotype-specific DENV from clinical specimens by RT-PCR. Therefore, conventional RT-PCR was initially performed for serotype-specific detection from the RNA isolated by both protocols. With the high-throughput protocol, amplification was noted in 11 of the 20 samples analyzed , with siNext, the 20 clinical specimens were also subjected to an RT-qPCR assay that evaluated each of the serotypes. Positive and negative controls worked as expected. Of the 20 clinical specimens analyzed by serotype-specific RT-qPCR for DENV after the two automated extraction protocols, most cases with confirmed infection were identified regardless of the extraction protocol used . SerotypIn summary, our results show that the automated extraction of RNA from the plasma of clinical specimens of pediatric patients with confirmed natural DENV infection using the KingFisher™ Flex instrument is useful for downstream molecular studies. Because the standard protocol was at least comparable for the detection of DENV by RT-qPCR than the high-throughput protocol , added tTo extend the previous findings to ZIKV, which is another flavivirus important in human health and currently cocirculates with DENV in Colombia, spiked experiments were designed. This was due to the low availability of naturally infected febrile specimens. Known quantities of ZIKV strain PRVA BC59 that had been titrated by a focus formation assay  were addAmplification of the constitutive controls of the TaqMan® Triplex  and in-house (RNase P gene) assays demonstrated the presence and good quality of RNA after automated isolation in tested specimens  was found, suggesting that the two methods identified the same relative amount of viral RNA.Although the fluorochromes of the probes used to detect ZIKV in the commercial and the in-house assays were different, we determined whether there was a relationship between the In summary, the RNA isolated automatically with the KingFisher™ Flex instrument allowed us to reliably detect different amounts of ZIKV particles in pediatric plasma.The purity and quality of RNA is a key factor in the performance of molecular assays. Different purification methods based on RNA chemistry and the use of a solid phase, such as guanidinium thiocyanate-phenol-chloroform , silica λ260/280 nm ratio, and this purity was in accordance with that obtained in a recent study that used the same KingFisher™ Flex automated extraction instrument and commercial purification kit for purification [The amount of RNA expected in plasma is lower than that expected when the original specimen is solid tissue or whole blood. The concentration obtained with the automated extraction of RNA from plasma was generally comparable to that reported with chemical methods, such as TRIzol-based isolation . A highefication .High fragility and easy degradation, even during the purification process, are critical characteristics of the RNA . TherefoThe expression of human RNase P was measured to further evaluate the quality and suitability of the isolated RNA for subsequent molecular assays. RNase P is an endoribonuclease that has a single-copy gene in each human cell, with constitutive and relatively constant expression levels of the gene . TherefoThe RNA isolated automatically from clinical samples of DENV infection was used for the serotype-specific detection of DENV-1 and DENV-2 by conventional and real-time RT-PCR. The KingFisher™ Flex instrument has been used to isolate RNA in studies of the interaction of flaviviruses in hosts such as mosquitoes and humans –33 and eCt for ZIKV obtained with the two RT-qPCR methods that we compared on the purified RNA, supporting and extending the usefulness of automated isolation for the amplification of target sequences within the ZIKV genome by different platforms. The agreement of the RT-qPCR assay with the commercial kit was high. The range of detection of spiked ZIKV RT-qPCR experiments showed comparable results with the low number of circulating infecting viral particles reported in naturally infected patients [A strong and significant relationship was found between the patients , althougThus, with this work, we demonstrated the potential utility of the automated instrument for RNA isolation for molecular studies of cocirculating arbovirus in pediatric settings.The automated isolation of RNA allowed the identification of DENV in a serotype-specific manner, in addition to ZIKV in an efficient manner. Evidence is presented of the potential use of automated instruments applied after the SARS-CoV-2 pandemic to other cocirculating viruses that have represented a serious problem to the public health of Colombia in recent decades.


TM photoresist. High-fidelity definition of 3D surfaces on IR transparent CaF2 substrates allowed to achieve ∼50% transmittance in the chemical fingerprinting spectral region 2–5 Microlens arrays (MLAs) which are increasingly popular micro-optical elements in compact integrated optical systems were fabricated using a femtosecond direct laser write (fs-DLW) technique in the low-shrinkage SZ2080 Microlens arrays (MLAs) are widely used micro-optical elements in integrated optical systems for specific applications such as laser beam homogenisation ,2, wavefTM photoresist :(1):(1)U2, thus polymerising a flat base platform with the microlens effectively protruding out from the platform. The height of the protrusions can then be measured with the optical profiler, which should ideally be the designed height, 9.9 r is the waist (radius) at the focal point when written with the focus exactly at the resist–substrate interface. Furthermore, the surface profile of the fabricated microlens only showed a sub-wavelength surface roughness of 90 nm (min–max), smaller than the required roughness of <2 and SZ2080TM.To further characterise the dimensions and surface profile of the fabricated MLA, SEM and an optical profiler were utilised. The diameter of the fabricated MLA was measured with SEM, as shown in ed light . This isP, ribbon width w, and film thickness d  in MATLT (and reflectance R) from real and imaginary parts of the refractive index T and R polarisation dependence.The GO grating polariser contributes to polarisation by the transmittance w is the width of one GO ribbon (2 n and extinction coefficients The GO grating is a form birefringent structure with the extraordinary tructure :(4)ne=n1onstants . The detThe GO polariser also works as a diffraction grating to linearly disperse polychromatic light into its constituent wavelengths (colours), arisen from the wavefront division and interference of the incident radiation from the periodic GO grating structures. Diffraction gratings are indispensable and fundamental optical elements in applications for measuring atomic spectra in both laboratory instruments and telescopes. The MLAs are used to perform observations and imaging in the microscopic range. When these two optical elements are integrated, they enable imaging of microscopic areas while simultaneously providing spectral analysis. The dispersed light with different wavelengths can be focused at different positions by the MLA. For flexibility of the test, the GO grating and MLA were made as two separate components and assembled together for possible azimuthal rotation along the optical axis. Such an assembly provides flexibility for angular dispersion orientation .2 substrate (thickness of 1 mm) was stacked on the GO grating polariser, also fabricated on a CaF2 substrate. The collimated polychromatic light from a white LED was dispersed by the GO grating polariser, focused by the MLA, and then captured by an optical microscope (Nikon TU Plan Fluor 50 × 2 substrate). Therefore, dynamic tuning of the dispersion distribution can be achieved by increasing or decreasing the spacing between the GO grating and the MLA. Moreover, as the GO grating polariser was separated from the MLA, it was flexible to rotate the GO grating polariser, as demonstrated in The optical layout of the angular dispersion-tunable assembly is displayed in TM MLA was close to 0.5 up to TM resist were present; however, they would be compensated for via normalisation in spectroscopic applications. The focusing performance at IR wavelengths for the The MLA and GO gratings fabricated and characterised in the previous sections for the visible spectral range could be useful for the IR spectral fingerprinting region 1–10 elengths . The trato 10 μm a. Polymen and imaginary d is the sample thickness. A generic expression of the combined Malus and Beer–Lambert laws has been introduced and demonstrated to exactly fit the additive contributions of retardance and absorbance to the transmittance through a pair of aligned polariser and analyser  [The real ittance) . This seHere, we used the fitting formula to retrieve the two contributions of retardance and absorbance to the transmittance, as expressed below:Tθ=aκcos22 (cubic structure) substrate rather than the glass substrate, due to its IR-transparent and isotropic (no birefringence) properties. The measured IR transmittance spectra 2 substrate was close to 100% (T by the fit using Equation (A, while for the flat spectral range without distinct absorbance bands and at the telecom spectral window at ∼To investigate the absorbance and retardance contributions in the fabricated GO polariser, the transmittance spectra  to 100% a. DuringAccording to Equation , the phaT at 1.58 In contrast, for the flat spectral range that is out of the absorption band, the retardance contribution is present. The experimentally measured The extinction ratio termined b and reaand food ,56,57.In800 cm−1 b, at whiP:In terms of the phase matching condition for the guided-mode resonance ,50, the ondition .P, w and d under both the TE and TM incident polarisations and the difference between them. The difference indicates the polarisation efficiency of the GO polariser, which can also be considered as a filter. P of the GO grating, which conforms to the guided-mode resonance, as expressed in Equation (P and GO thickness d at 4 and 1 T modulation of the GO polariser. The influence of the GO film thickness d is as follows: a thicker GO film can increase the absorption of the TE polarisation as well as the TM absorption. Therefore, to select the appropriate thickness of the GO film can effectively optimise the polarisation selectivity of the GO polariser. Hence, the performance of the GO polariser is dependent on its geometrical parameters; thus, the working wavelength determined by the period of the GO grating and polarisation selectivity can be optimised by tuning the duty cycle and thickness of the grating.Equation . The effTM MLA on a 1 mm thick CaF2 substrate was measured. The polymerised SZ2080TM material has a relatively high transmittance from 3.5 to 5.5 Additionally, the IR transmittance spectrum of the SZ2080TM photoresist. The surface morphology of the fabricated microlenses mirrored the design with high-fidelity at high-irradiance, ∼The fs-DLW technique was utilised as a 3D lithography tool to precisely fabricate an MLA in the low-shrinkage SZ2080Additionally, a GO grating polariser was fabricated with the fs-DLW technique in a 1- charges . The hig charges  as well  charges .


When deciding between options that do or do not lead to future choices, humans often choose to choose. We studied choice seeking by asking subjects to first decide between a choice opportunity or performing a computer-selected action, after which they either chose freely or performed the forced action. Subjects preferred choice when these options were equally rewarded, even deterministically, and traded extrinsic rewards for opportunities to choose. We explained individual variability in choice seeking using reinforcement learning models incorporating risk sensitivity and overvaluation of rewards obtained through choice. Model fits revealed that 28% of subjects were sensitive to the worst possible outcome associated with free choice, and this pessimism reduced their choice preference with increasing risk. Moreover, outcome overvaluation was necessary to explain patterns of individual choice preference across levels of risk. We also manipulated the degree to which subjects controlled stimulus outcomes. We found that degrading coherence between their actions and stimulus outcomes diminished choice preference following forced actions, although willingness to repeat selection of choice opportunities remained high. When subjects chose freely during these repeats, they were sensitive to rewards when actions were controllable but ignored outcomes–even positive ones–associated with reduced controllability. Our results show that preference for choice can be modulated by extrinsic reward properties including reward probability and risk as well as by controllability of the environment. Human decisions can often be explained by the balancing of potential rewards and punishments. However, some research suggests that humans also prefer opportunities to choose, even when these have no impact on future rewards or punishments. Thus, opportunities to choose may be intrinsically motivating, although this has never been experimentally tested against alternative explanations such as cognitive dissonance or exploration. We conducted behavioral experiments and used computational modelling to provide compelling evidence that choice opportunities are indeed intrinsically rewarding. Moreover, we found that human choice preference can compete with maximizing reward and can vary according to individual risk attitudes and the controllability of the environment. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Ulrik R. BeierholmAcademic EditorPLOS Computational BiologyMarieke van VugtSection EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Title: Choice seeking is motivated by the intrinsic need for personal controlIn this paper, the authors carefully investigate whether choice-seeking is intrinsically motivating. Across three experiments, they show that people have a preference for free choice, even when free choice does not have greater relative value than forced choice. The authors use computational modeling to explore this behavior further, and the winning variation of their model could explain individual differences in choice seeking. The paper explores a classic topic using a modern framework, and is likely of interest to a broad audience. The thoughtful experimental designs are a particular strength of this paper. I have some comments and questions below that I think it would be useful for the authors to clarify/address.1. One of the primary strengths of this paper is the experimental design/behavioral analyses. Therefore, it will be especially important for the authors to make certain that the complicated task designs are clearly explained.a. For example, while it is discussed in the main text, there is no explanation of what the red “V” is in the Figure 1 caption. In addition, it wasn’t clear to me the purpose of both the red “V” and the black arrows, which both seem to indicate participant (or computer) selection.b. In Experiment 3, I assume the black arrows/red “V” on incoherent trials simply point to the participant’s perceived selection, but I don’t think this is ever mentioned in the text.c. Were participants paid additional compensation depending on their task earnings/performance? This should be mentioned in the text regardless of whether they were or not.d. On Line 602, the authors state, “Data from the training phase consisted of 2nd-stage actions and rewards, but we also presented subjects with the 1st-stage cues corresponding to the condition being trained (forced or free).” This is the first mention that 1st stage cues are shown during the 2nd stage choice phase. It would be helpful if this was clarified earlier in the manuscript .2. Figure 2D: It’s not clear to me why in the “High” panel, there’s only data for 24 trials. If this is simply a case of me missing the explanation in the text, then perhaps it would be helpful to reference the figure in the text where this is explained.3. I appreciated the clever controllability manipulation in Experiment 3. The authors explain that the repetition of free choices across incoherent trials suggests that participants are trying to “regain control.” However, I would imagine that this manipulation could cause the participants some confusion. In other words, pressing a “left” button and getting a “right” response probably seems like a bug or an error, and they might just be repeating their actions to test whether they have actually lost control. Because there typically isn’t any uncertainty around an action like a key press and a response, I’m wondering whether participants reported being confused by this manipulation, and also whether there were RT differences for repeated choices after incoherent trials.4. In the context of Experiment 3, the authors talk about manipulating “perceived controllability.” I think it is worth being cautious of the language here. “Perceived” controllability implies that participants both explicitly reported a lack of control and yet truly had control over the choice. Instead, it might be best to simply talk about this as a manipulation of the actual controllability .5. The authors mention that they pooled two subject groups for Experiments 1a and 1b and that they did not have any “substantial differences.” It is important to include the actual evidence that these two subject groups did not differ behaviorally.6. Beginning on Line 312, the authors write: “Consistent with previous studies, subjects preferred the opportunity to make a choice when expected rewards were equal between terminal actions (P = 0.5).” It would be helpful to cite some of this previous work here.7. The authors should consider including a table in the supplement with each model name, brief details of the model , and model fit values.Reviewer #2: In this article, the authors aimed to identify the features triggering the well-known preference for free choices over forced choices. The authors replicated this effect in a 2-step task with two arms at step 1 –one leading deterministically to a free choice at the second step, the other to a forced option. The second-step terminal cues were probabilistically related to a binary outcome with the probability P of getting a reward being the same for both the best free choice outcome and for the forced cue outcome. Importantly, the experiment was divided into two phases: a training phase allowing participants to learn the terminal option-reward contingencies followed by a testing phase measuring free choice preference above and beyond learning. The main results are that participants prefer free choices after learning despite the risk of making a bad decision in step 2 free choice –even with a risk is maximised with P = 1 (Experiment 1). This result rules out that exploration would explain free choice preferences. Yet, this preference is reversed when the expected value of the best free choice option is much lower than the expected value of the forced option (Experiment 2). The authors offer a model-based mechanism posing that free choice preferences emerges from a bias placed on each free choice reward in a reinforcement learning model. Finally, they claim that free choice preference depends on personal control experience when the task is controllable.Overall, this paper is well-written and I found it very interesting and exciting as free choice and more globally intrinsic motivation seem to be ubiquitous in everyday life and yet remains very poorly understood. It is also a growing topic of interest in the computational and cognitive neuroscience community. Here, the authors carefully designed experiments to test for various factors potentially influencing free choice preference. Moreover, they offered a mechanistic account for free choice preference using a model-based approach that is carefully and purposefully driven, ruling out many potential motivations for free choice preference. Most of the results are informative and neat.My main concern is that the paper seems to fail identify what is behind free choice preference. Although the authors rule out many potential explanations through experiment 1 and 2, the data seem to rule out their proposed explanation in experiment 3 –personal control. See below for more details.1) Experiment 3 aims to show that free choice preference is due to personal control or at least that it is influenced by personal control. To test this idea, stage 2 choice controllability (i.e. how likely it is that the chosen option is indeed selected) is altered, with the prediction that free choice preference should decline as controllability decreases.1a) Yet, this may not to be the case in the data . In fact, the crucial test for the effect of controllability on free choice preference is not reported. Thefore, the following claim lack of statistical support and is currently not appropriate: “Increasing the incoherence of the 2nd-stage actions progressively reduced choice preference (block 2 and 3: 67% and 64% in favor of free respectively).”. Statistics should be reported and interpreted with the corresponding claim.1b) Similarly, the following statement can remain as such only if statistics indeed support an overall decline in free choice preference as incoherence increases. It should be removed otherwise. LL 261-262 “We found that *the decline in choice preference* depended on the 1st-stage choice on the previous trial.”I found the other arguments supporting the need for personal control explanation of free choice preference rather vague:2) I do not see how to interpret that “as incoherence increased, subjects tended to stay more with the forced option, while maintaining a preference to repeat free selections” and I am not sure that it is necessary to show that the motivation for personal control is what drives free choice preference. I suggest to better explain how important it is for the demonstration or to move it in the supplementary material.3) The principal remaining argument to support that free choice preference depends on choice controllability is rather indirect. For all controllability levels and at stage 2, participants were sensitive to the previous reward when the selected option was the chosen one (i.e. if the previous choice was coherent) but were not so when the selected option was not the chosen one (i.e. if the previous choice was incoherent).3a) Comparing statistically the incoherent stay proportion rewarded versus unrewarded would be required for this pattern to be fully demonstrated . It should not be significantly different.3b) Even then, I do not quite see how it proves that free choice preferences (measured at stage 1) depends on personal control. Perhaps it is because of the bonus placed on the reward in the learning model. Yet, a model with a choice bias may equally capture free choice preference (see below fore more details on that point).Without necessary evidence, abstract, results and discussion sections would need to be rephrased. I have other less problematic concerns and some suggestions which I hope will help improve this article.4) The authors interpret the observed consistent free choice preferences over different reward probability values  as evidence ruling out curiosity, exploration, variety-seeking and selection-based explanations. While I can see why it rules out curiosity and exploration, I am not sure regarding the other alternative explanations at least as described in the introduction.4a) Regarding the selection-based explanations, it is not clear to me in the results section why cognitive dissonance would have reduced effect after learning when terminal reward probabilities are clearly different between each free choice options. It may be worth to unpack this explanation (as it is done in the discussion)4b) Regarding variety seeking, I guess that people may keep checking for changes in the testing phase when the reward probability is <1 but not when it is 1 and that is why curiosity and exploration explanations would be ruled out when reward probabilities are very different. Yet, there is still potentially more variety following the free choice option (as one can choose the worst option) whatever the reward probabilities are. Can the authors clarify the link between the results and how this explanation is ruled out?5) The model space the authors used missed a model that could equally capture free choice preference: a choice bias in the softmax. The current winning model assumes a bonus plugged to the reward which predicts that Pfree will be inflated with respect to Pforced due to the boost, and it would predict an increasing free choice preference as the number of learning trials increases. Yet, this is not testable in the current design precisely because the participants already learned when they had the choice between the step 2 free or forced choice. Currently, I do not see any data supporting the current best model. I suggest the authors to either add the model I suggest in the model space and stress that these are two possible mechanisms that cannot be distinguishable with the current experiment, or to run a new experiment to add the possibility for participants to make step-1 decisions at various learning stages.6) Here is another alternative model I suggest to add to the model space. The current best model includes a bonus placed on rewards following a free choice. If it actually predicts an increasing free choice preference, and if learning still occurs in the testing phase as suggested LL 184-188 pp 10-11, then it would predict an increasing free choice preference over trials in the testing phase. Yet, free choice preferences are constant . I suggest to decrease the bonus strength linearly or exponentially for example as follows: rho_0 + rho_1 * trial number, or something like : rho_0 .* [rho_1 .^]./rho_1 to account for the emergence of free choice preferences, with a reduced influence with time.7) It clearly appears that participant learned which free-choice option was the best in the Experiment 1 training phase. Yet, it is unclear what is learned: the actual probability or that one option is better than the other? In other words, were participants really learning P? If so, did they learned accurately P to the same extent for the forced and for the free choice condition or is P estimation inflated in free as predicted by the learning model with a boost placed on rewards? One way to check that would be to explicitly ask the participants for the probability of each cue to lead to a reward after the training phase. Was it measured in anyway?8) Are participants aware that the free choice corresponds to a 1-arm bandit  and not to a 2-arm bandit ? A possibility is that participants may overestimate the worst outcome reward probability depending on their prior  and given that they did not choose them a lot and therefore given that they did not update them a lot. As a consequence, the free choice expected-value maybe higher than the forced choice expected value. This could be tested by mean of model comparison (even when the objective reward probability was set to be 1). I think that it would require Q to be initialised at 0.5.9) Why are Q initialised at 0? Does model comparison show that it is better than 0.5?10) Results showing free choice preference are a weakened by the fact that the extrinsic rewards were actually not implemented. It may be that participants only prefer free choices over forced choices when the cost is very low. In the current experiment, a reward probability difference of 0.2 is enough to reverse preferences at the group level. Yet, if extrinsic rewards were real, a much lower difference (maybe no difference) would be enough to reverse the effect. Adding this condition would make the result stronger. Alternatively, I suggest the authors to stress that the results validity is limited by the lack of actual extrinsic rewards.11) I would not frame Experiment 2 as a titration. In this experiment, Pforced is either the same as Pfree, (Pforced=Pfree=.75), either larger (Pforced = .85 > Pfree), or much larger (Pforced = .95). The group average when Pforced =.85 is indeed around .5. Yet, the variance is very high and the distribution seems rather bimodal with a very few participants being indifferent between free choice and forced choice. I suggest the authors to frame it as a reversal: when the difference between Pforced and Pfree is 0.20 the vas majority of participants reversed their preference compared to when Pfroced=Pfree.12) Results regarding the free choice preference are convincing . Yet, there is a lot of variance in the data. Do the authors have a clue about why some participants always prefer the forced choices? Are they the ones who learned faster in the training phase?13) It is not really clear what tests were performed in the result section. I suggest to report an effect size and to specify the test .14) I found it hard to get information about the model comparison results. In particular, figure 4A is not so informative as the models differs in terms of free parameters number. It is unclear what is the winning model at the group level and by how much. Reporting a figure or a table would be informative to get a sense of the second-best model, etc. In fact, reporting each model frequency on top would also be informative.15) Lines 250-257 p 14: “Choice preference was high (70%) in block 1 when coherence was not altered, similar to block 1 from experiment 2 where extrinsic reward was equal between free and forced options”. Can the authors report some statistics to support that claim?16) I wonder how the optimistic learner can be fully distinguishable from the pessimistic one as the former is embedded in the later. Can the author keep the pessimistic model and compare it to a version with beta = 0 and to another version with beta = 1? That would allow the authors to show that whether the best model is more optimistic, more pessimistic, or in between.17) I suggest to add lines between each participant dot across all condition in the figure similar to figure 2B to provide the reader with an idea about preferences stability.18) How were the sample sizes determined?Reviewer #3: In this paper, Munuera and colleagues report results from three experiments using a two-stage decision task that aims to computationally characterize peoples’ preference for making choices. In Experiment 1, they found that even after learning the reward probabilities associated with second-stage choice options, participants demonstrated a consistent preference for choosing to make their own second-stage choice, rather than being forced to choose a maximally rewarding option. In Experiment 2, they found that participants continued to prefer to make their own second-stage choices, even when the forced choice option was more rewarding. And in Experiment 3, they demonstrated that in less controllable environments, when actions did not deterministically lead to expected states, participants’ preference for making free choices was reduced.Overall, I think the experiments nicely demonstrate people’s preference for making choices, but many prior studies (which the authors cite) have demonstrated similar effects. The coherence manipulation in Experiment 3 is interesting and more novel, but I found the results difficult to interpret and the explanation the authors put forth for the effects overly speculative. Below, I lay out my concerns in more detail.I think the authors’ hypothesis that participants might be ‘seeking personal control’ in response to the incoherence of the environment is very interesting. However, I found it difficult to follow their reasoning for why the specific pattern of observed results supported this hypothesis. For example, it is not clear to me why participants’ were necessarily “seeking personal control” by repeating their target selections regardless of reward outcomes on incoherent trials. It seems like an equally (or more) compelling explanation for these results is that participants rationally discounted the outcomes of events that were not caused by their own choices.In addition, it is unclear how the computational account put forth for the findings from Experiment 1 and 2 can be extended to account for the findings from Experiment 3. The authors found that the majority of subjects in Experiment 1 and 2 were best fit by an optimistic reinforcement learning model with a free choice bonus that simply inflated the value of rewards obtained after choice. However, simply inflating the value of the rewards obtained from chosen options does not seem like it can explain the findings from Experiment 3 — instead, the model would need to estimate the coherence of the environment, and/or the probability that rewards are the result of one’s own actions, and modulate the bonus accordingly. Thus, while I found Experiment 3 interesting, I was unsure how to interpret the findings within the framework set up by the rest of the paper.I also wondered about alternative computational accounts for the findings from Experiments 1 and 2. Here, the authors implemented the enhanced valuation of free choice outcomes via a bonus added to the reward outcome. It seems like other computational mechanisms could also explain these effects. In particular, prior studies have found that different higher learning rates for positive vs. negative outcomes following free choice can explain the preference. Is this model distinguishable from the choice bonus model described here?In addition, it seems as though an alternative account could be that making the choice is in and of itself rewarding. Rather than inflating the value of the outcome obtained through free choice, an alternative model could add a bonus directly to the first-stage free choice option. In other words, do participants learn more from free vs. forced choices or simply prefer them?Though perhaps outside the scope of the present work, one way to address this question might be to have an additional phase of the task where participants must choose between the forced choice fractal and the high-value free choice fractal. Does the high-value free choice fractal remain more valuable to participants? Similarly, do participants have accurate explicit knowledge of the reward probabilities associated with the fractals, or are their estimates for those following free choice inflated?A few minor concerns:It does not seem reasonable to initialize the Q value estimates at 0, since the actual outcomes values range from 0 - 1. Initializing at .5 seems less biased.While the description of the task in the methods section at the end of the article is very clear, certain information that was critical for interpreting the results was not presented upfront. In particular, it would be helpful to know how long the training phase was, whether participants were instructed about the relationship between the reward probabilities, and how many participants completed Experiment 1.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.The No: The authors said they *would* make the data available upon publication, but it was not available at the time of review.Reviewer #1: No: The authors claimed that the code will be made available but they were not at the time of this review.Reviewer #2: Reviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocolsTo enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at  30 Mar 2023AttachmentMunuera_etal_ResponsesToReviewers.pdfSubmitted filename: Click here for additional data file. 26 Apr 2023Dear Dr Lau,Thank you very much for submitting your manuscript "Choice seeking is motivated by the intrinsic need for personal control" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.------------As you will see below all reviewers were impressed with the amount of work that went into the revision of the paper. While reviewers 1 and 3 were happy with the current version reviewer 2 has some significant objections to the interpretation of the results.I am interested in what your response is to the reviewer comments, although the response is not likely to be sent to the reviewer.Upon re-reading the paper I tend to partly agree with the reviewer. The in-coherence effect on free/forced choice is not significant, and the interaction between previous trial and coherence level is harder to interpret. Unlike the reviewer however I am much more open to merely changing the interpretation, including softening the title as one way forward. Ulrik Beierholm, Academic Editor---------Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.When you are ready to resubmit, please upload the following:[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Ulrik R. BeierholmAcademic EditorPLOS Computational BiologyMarieke van VugtSection EditorPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have gone above and beyond to address my previous comments and I have no further suggestions. In terms of adding Figure R1 (RT) into the supplement, I think it would be useful, as it helps bolster the claims of the paper. However, I will leave the decision to include it up to the authors and the editor given space constraints and other considerations.Reviewer #2: While I acknowledge that the authors put efforts in addressing my (and very similar reviewer 3’s) concerns, I still do not see any data supporting the title and the conclusions.What is straightforward in the paper is not new (experiment 1 & 2), and what is new is not straightforward (experiment 3). In particular, the direct test of the hypothesis that “Choice seeking is motivated by the intrinsic need for personal control” is that free choice preference should be altered by lower level of controllability. That would show that when people are in control in free choice, they choose free choice, but when they are no longer, they equally prefer forced choice. This test was not reported in the first version of the manuscript and is in fact not significant in the revised version.While the fact that incoherent choices are repeated regardless the outcome can be interpreted as a compensatory mechanism to feel in control when incoherency is high, it can be equally interpreted differently. For example, people may be model based learner, but they may attribute positive outcomes to their credit and they would repeat the same action.  It is anyway not a direct proof that “Choice seeking is motivated by the intrinsic need for personal control”.Overall, I can only see three options here: the title not being supported by the data, the paper cannot be published as such (hence rejected); at least a pre-registered experiment 3’ including more incoherent levels (e.g. up to 0.5) shows that free choice preference is altered when personal control is lower ; the paper is reframed to focus on the interesting  experiment 3 result, which is no longer about free choice and personal control.In addition, I have other suggestions:It remains unclear to me how the learning model with a bonus differs from a model with a choice bias.Can the authors report a qualitative signature of this model that a model with a bias could not capture?Model comparison is often relevant, but may be biased.On that note, model recovery results could be performed .In the same vein, I cannot explain why a q value initialised at 0 is better. If it is added as a free parameter, is it indeed estimated to be 0?Reviewer #3: The authors have done an excellent job responding thoroughly to my concerns as well as those raised by other reviewers. I believe the changes they made to the manuscript greatly improve its clarity, and the additional modeling analyses provide stronger support for their original conclusions. I believe this paper will make a valuable contribution to the literature and am happy to recommend its acceptance.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: NoneReviewer #3: None**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocolsTo enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at References:Review your reference list to ensure that it is complete and correct. 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Editorial on the Research TopicUnderstandings and conceptualizations of hope and how it influences engagement with sexual and reproductive health (SRH) services among adolescents in LMICsMehta and Seeley) (Adolescents and young people 10–24 years) constitute about 1.2 billion of the world's population, with 70% of adolescents living in lower- and middle-income countries (LMIC) . It is d Seeley) . In this years coMuch research on hope has been driven by psychologists in the global North, with individualised understandings of hope essential to overcoming adversity and fostering resilience and positively linked to health and wellness –7. WhileUnderstanding the link between hope and health behaviours for adolescents can inform SRH interventions that address how young people understand themselves, their risk perception, their context, and their future. Developing understandings of how hope operates dynamically in the lives of adolescents and how hope shapes SRH outcomes is important to consider.In this collection, we have brought together quantitative and qualitative research that examine the relationship between hope and SRH-related experiences and behaviours, including how “risks” and opportunities are constructed and navigated and how this influences adolescents' engagement  with SRH services, including for HIV. The collection includes a review exploring the role of agency and hope in adolescent behaviour and access to SRH services and articles on risky sexual behaviours that predispose young people to poor SRH outcomes and understanding traditional social norms and contexts that increase young women's vulnerability and affect access to SRH services.Groenewald et al. focused on the roles that agency and hope play in the SRH of adolescents, including accessing SRH services. They view hope as the driver of agency associated with more positive outcomes in young people, including enhanced self-esteem, self-worth, and self-care agency. Given that the link between hope and agency, access, and uptake of SRH services is unclear, their review explored how these factors converge and interact to influence adolescents' SRH and access to SRH services. Their work has highlighted the importance of understanding the role of individual factors such as hope, self-efficacy, agency, and autonomy in adolescent SRH, behaviours, and access to services and how these interact with the social environment, norms, and practices that often shape young people's risks and protective behaviours and access to services in African contexts. They cited studies demonstrating cultural norms that view girls publicly expressing sexuality and talking about sex as taboo.Maruwo et al. aimed to identify factors associated with long-acting (LARC) and short-acting (SARC) reversible contraceptive use among 10–24-year olds in Lilongwe, Malawi. Their study analysed data of clients accessing family planning services from 64 youth outreach sites in Lilongwe district. This study has highlighted the high unmet contraception needs among young people, especially acute among younger compared to older clients, and its findings are essential to address the complex burden prompted by unplanned pregnancies. Furthermore, LARC uptake was found to vary by age, education, number of living children, and marital status, among other factors, underlining the importance of individual social contextual factors in shaping access to and uptake of preventive opportunities.Workye et al. focused on gender-based violence (GBV), which is a key risk factor for poor SRH and influencing SRH services uptake. The study found that the overall prevalence of GBV among women university students in southwest Ethiopia was very high, with almost half of the students reporting experiencing GBV, mostly physical and sexual violence. Students who were married or living with a man, who consumed alcohol, and who did not discuss SRH issues openly with their families were more likely to experience GBV, highlighting the role of locally specific social norms in shaping risk behaviours and the need for tailored SRH for youth that responds to these conditions in any context.The third article by Muraguri et al. reported on the finding of a bio-behavioural survey of male sex workers (MSWs) who have sex with other men in Nairobi, Kenya, utilizing a respondent-driven sampling recruitment approach. They estimated the prevalence of HIV and STIs and documented high levels of alcohol and drug use, risky sexual practices, and experiences of violence and discrimination. Although they found both younger (18–24 years) and older (>25 years) MSWs were engaged in high-risk sexual behaviours, older MSWs were more likely to be living with HIV. Although overall rates of violence and discrimination were low, a higher proportion of younger MSWs reported verbal, physical, and sexual violence than older MSWs. These findings suggest the importance of understanding the interplay of risks across the life course for specific groups and the public health imperative of ensuring that preventive services are accessible, for example, through the provision of safe clinical spaces, even in settings where homosexuality is criminalised and where there are few or no tailored services for key populations.Lastly, Taken together, these four articles make an important contribution to the understanding of how hope and context shape adolescents' experiences, risk behaviours, and uptake of SRH services and outcomes in LMIC settings. Understanding how “risks” and opportunities are constructed and navigated is crucial for informing the development and scale-up of SRH interventions to improve SRH uptake and outcomes among adolescents in LMIC settings.


Inhibition is reversed upon outgrowth from the stationary phase by synthesis of small product RNA transcripts (pRNAs). 6S and its complex with a pRNA were structurally characterized using Small Angle X-ray Scattering. The 3D models of 6S and 6S:pRNA complex presented here, demonstrate that the fairly linear and extended structure of 6S undergoes a major conformational change upon binding to pRNA. In particular, 6S:pRNA complex formation is associated with a compaction of the overall 6S size and an expansion of its central domain. Our structural models are consistent with the hypothesis that the resultant particle has a shape and size incompatible with binding to RNA polymerase-σ70. Overall, by use of an optimized in vivo methodological approach, especially useful for structural studies, our study considerably improves our understanding of the structural basis of 6S regulation by offering a mechanistic glimpse of the 6S transcriptional control.The non-coding 6S RNA is a master regulator of the cell cycle in bacteria which binds to the RNA polymerase-σ E. coli 6S is encoded by the ssrS gene as a precursor molecule which is processed by RNases to generate the 183 nt mature functional form  holoenzyme (Eσ70), 6S RNA inhibits binding of many DNA promoters to RNAP and impedes the transcription from σ70-responsive promoters of the majority of genes during stationary phase  has been undergoing a rapid expansion in recent years. A prominent member of this class of molecules is 6S RNA, isolated more than 50 years ago  but onlynal form . 6S, whiry phase . RecentlRNase BN . Due to ry phase . 6S alsory phase . Moreovehenotype . The higtype  during outgrowth from stationary phase   . When pRupshift) . Mutatio release . 6S RNA y RNases   involved in critical biological processes . These 3E. coli 6S, free and in complex with pRNA with Small Angle X-ray Scattering. To improve the yield of 6S RNA , possibly reflecting a limited ability of the construct to properly process the transcript to the canonical 6S sequence. On the other hand, both prom6S and synth6S constructs produced high levels of 6S expression higher even than most other RNAs in E. coli ; Lane M.in vitro transcription. In addition, the quality of the product was sufficiently good to allow for the acquisition of excellent quality SAXS data for the structural analysis of 6S. We also examined a longer protocol including hydroxyapatite and arginine-sepharose affinity chromatography. However, the extra steps resulted in a substantially prolonged protocol (and lower yields) without significantly improving the purity of the final RNA.Bacterial cultures containing the prom6S construct were induced by different IPTG concentrations  at varying durations   at 37°C Rg were 60 Å and 49 Å for the free 6S and the complex, respectively, indicating a strong compaction of the molecule upon pRNA binding. A shift of the SEC peak to larger elution volumes for the 6S:pRNA complex compared to free 6S indicates a decrease in the hydrodynamic radius of the complex while the homogeneity of the samples is evidenced by the stability of their Rg throughout their respective peaks (Dmax is observed for the complex (240 Å vs. 200 Å). Interestingly, the dimensionless Kratky plot , shown in ve peaks . The disve peaks  further tky plot  of the ftky plot , possiblI to V. The first free 6S arrangement (fold I) contains no stem loops in the central domain bulge (II) contains a commonly reported (III) a much larger 3′ stem loop, involving partial unfolding of the closing stem, commonly associated with pRNA synthesis (IV) or presence of a 5′ stem loop (fold V) at ∼60 nt  are compatible with the SAXS data in the case of free 6S, in agreement with our previous work (I-like conformation when in complex with Eσ70 (without pRNA). Strikingly, no such bias is observed for the 6S:pRNA complex (I to V) of I). The hybridization of even a few nucleotides, while looking inconspicuous at the secondary structure level, requires the formation of a small double helix which, in turn, causes a significant compaction of the molecule (fold II) incompatible with our experimental data. This effect is even more pronounced in the presence of a larger loop (fold III). On the contrary, the best fit to the SAXS data of the complex is achieved when one considers a more equimolar ratio of the two conformations presented in IV and fold V). Both 6S:pRNA arrangements have a very similar overall shape, despite their differences at the secondary structure level, with extended “double-helical” closing and internal stems and a “swollen” central domain due to the presence of the pRNA. MM-GBSA free energy analysis of the 3D conformations corroborate the above observations (II having the lowest mFold ΔG), the 3D structure free energy of Fold I is significantly lower than II and III, suggesting that it is the most stable free 6S form, in full agreement with the EOM observation. In the case of the 6S:pRNA complex, the free energy difference of the 3D structures of the two conformations is smaller (while the mFold difference is larger than in the case of free 6S), with fold IV being preferable to fold V, also similar to the EOM analysis. Binding free energy analysis of pRNA to 6S  from which the EOM program  can seleructures  illustraous work  and the ous work  which cl complex . To bett complex  with resrvations . While trvations  shows noNA to 6S  shows thI-like 6S can accommodate the structural changes required for the change from the solution form to the Eσ70–bound open promoter-like form  for their fit into the Eσ70. The presence of the 3′ stem loop at ∼140 nt  and even then it would first require the disengagement of σ70 from RNA polymerase. This renders the re-integration of the complex back to the Eσ70 holoenzyme very unlikely, rescuing it after pRNA transcription and allowing it to resume transcription of genes.The free, flexible, fold ike form  while thal model  a fold Ial model  the nucl0 cavity  when 6S E. coli 6S with biophysical methods (Aquiflex aeolicus 6S (E. coli Eσ70 with 6S (Bacillus subtilis 6S with Atomic Force Microscopy (AFM)  .in vivo RNA overexpression . Since the supply of pure as well as chemically and structurally homogeneous RNA samples in mg amounts is a prerequisite for structural studies, we decided first to develop an optimised methodology for in vivo overexpression of 6S and purification under mostly native conditions. Various 6S constructs were designed and tested for best results. After refinement of growth conditions, we achieved excellent overexpression of 6S. The product has the same electrophoretic behavior as the endogenous material and produces a strong sharp band with Urea-PAGE. Our protocol is superior to commonly applied in vitro transcription methods used for RNA production at the following points: i) it readily produces exceptionally large amounts of material; ii) the cost per milligram of pure RNA material is substantially low, at least two orders of magnitude cheaper than in vitro transcription kits, comparable with the cost per milligram of pure protein material. iii) The produced RNA material is highly pure and homogeneous as can be judged by Urea-PAGE and size-exclusion chromatography. Accordingly, we have illustrated that in vivo overexpression of RNA is an approach that merits further exploration, e.g., by using stable structured RNAs such as tRNAs . The one strand serves as a pRNA template while the other strand is released and forms a 3′ stem loop at ∼140 nt. A more compact 6S particle is gradually formed and at the same time nucleotides U134-G143, which were initially single-stranded and involved in interactions with Eσ70, are engaged in the stem’s double helix formation, consequently unable to participate in interactions with the protein. Therefore, favorable 6S:Eσ70 interactions are lost and the overall fit of 6S inside Eσ70 worsens. Once pRNA reaches a certain length the above described phenomena reach a breaking point. The significant conformational changes in conjunction with the displacement of 6S due to transcription and the disruption of several 6S:Eσ70 interactions lead to the release of the 6S:pRNA particle from Eσ70. On the other hand, 6S is unlikely to dissociate from pRNA until its degradation, explaining why, even though 6S is constitutively expressed, it has no effect during the exponential growth phase. In other words, once pRNA of a certain length  competent cells for RNA expression. A single colony was cultivated in 20 mL LB medium containing 50 μg/mL ampicillin (LB amp50) at 37°C, 250 rpm and used to inoculate 2 L of Terrific Broth Medium (TB) containing 50 μg/mL ampicillin (TB amp50). It was subsequently incubated overnight at 37°C, 250 rpm. The cells were pelleted and washed with 20 mM Tris-HCl pH 7, 200 mM NaCl. The RNA was isolated by acid-guanidinium thiocyanate-phenol-chloroform extraction in an analogous manner to that previously described by Chomczynski , Q Sepharose anion exchange column and a Sephacryl S-200 column . The Q Sepharose column was pre-equilibrated with buffer A . After loading the sample onto the column 400 mL of buffer A was used to wash the column and subsequently a 800 mL gradient of buffer B  up to 100% (620 mM NaCl) was performed while collecting 8.5 mL fractions. The system was cleaned with 200 mL of 100% buffer B to wash off remaining uncleaved RNA and DNA remains. Absorbance was monitored continuously at 260 nm. The fractions were analyzed by 7.2% Urea-PAGE in 1X TBE containing 8 M urea. Fractions with the 6S RNA were merged, heated to 95°C for 5 min and subsequently placed on ice for 20 min to refold. After centrifugation, the sample was loaded onto a Sephacryl S-200 column , and 6S RNA eluted with a buffer containing 20 mM Tris HCl pH 8.0, 200 mM KCl and 5 mM MgCl2. The purified 6S RNA was concentrated by ultrafiltration with 10 kDa cut-off filter and analyzed by 7.2% Urea-PAGE in 1X TBE containing 8 M urea. RNA quantification was performed as described before (RNA pellets from 2 L of culture were diluted to 300 mL of a buffer containing 50 mM Kd before .2 buffer) was mixed with molar excess of pRNA in ratios 1:3 and 1:7 to a final volume of 75 μL. The mixture was heated at 94°C for 4 min and subsequently placed at 37°C for 1.5 h.The pRNA in our experiments is the 20 nt synthetic sequence 5′-AUCGGCUCAGGGGACUGGCC-3′ . For the−1 . Primary data reduction, radial averaging, averaging and subtraction were performed on-site with the beamline software . The majority of SAXS data of 6S as well as the 6S:pRNA complex were collected at the SWING Beamline of Synchrotron SOLEIL  with an Aviex charge-coupled device detector . Using the Foxtrot software, the data were averaged radially and converted to absolute units, analyzed for radiation damage, averaged and subtracted. Subsequent analysis was performed with the ATSAS program suite (Rg and the forward scattering intensity I(0)  from the slope of Guinier plot (lnI(q) versus q2) (p(r) and to estimate the maximum particle dimension (Dmax). The molecular mass (MM) of the solute was estimated from the SAXS data from the I(0). Twenty dummy beads models of the SAXS data were created for both free 6S and 6S:pRNA complex with DAMMIF  using a Pilatus 2M (DECTRIS) photon counting pixel detector . The meadetector . The meaam suite . PRIMUS am suite  was usedsus q2) . GNOM (Ssus q2)  was usedh DAMMIF  and averh DAMMIF .G) were calculated with mFold  vs. the subset of the selected ensembles. MM-GBSA free energies (G) and binding free energies of pRNA to the 6S:pRNA complex (ΔG) were calculated with the MMPBSA module   of free th mFold . These sth mFold . To bettth mFold  with theth mFold . All simth mFold . The Ensth mFold  was usedA module  of AMBER