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Official publication of Indian Society of Pedodontics and Preventive Dentistry
Volume 34 | Issue 1
Page Nos. 1-100
Online since Tuesday, February 2, 2016
Full text access is free in HTML pages; however the journal allows PDF accesss only to users from developing countries and paid subscribers.
Publicationomics: Delusion demystified p. 1
ND Shashikiran
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Effect of fixed space maintainers and removable appliances on oral microflora in children: An in vivo study p. 3
Ritesh Kundu, Abhay M Tripathi, Jagdish Narain Jaiswal, Ujjala Ghoshal, Madhuchanda Palit, Sonali Khanduja
Background: Oral habits and caries if left untreated may result in crowding and arch length discrepancy in developing dentition. Therefore, appliances are used to maintain the arch length and for proper relationship between dental arches. However, its insertion may cause an increase in bacterial concentration. Aim: This study aimed to evaluate the growth of Streptococcus mutans, Lactobacillus sp., and Candida albicans in saliva during the first 6 months of orthodontic therapy. Materials and Methods: Twenty children in the age group of 6-15 years were selected with regard to indication of orthodontic treatment, and subsequently grouped as patients to be treated with fixed space maintainers or removable appliances. Unstimulated saliva was collected in a sterile container at baseline and at 1-month, 3-month, and 6-month recalls for every selected child. Samples collected were processed for bacterial culture in different culture media at different time intervals. Statistical Analysis: SPSS (Statistical Package for Social Sciences) Version 15.0 statistical software was carried for bacterial counts. Chi-square test and t-test were performed to know the effects of each variable and to reveal the statistical significance. Result: Bacterial counts of Streptococcus mutans, Lactobacillus sp., and Candida albicans were found to be statistically significant (P 0≤ 0.001), (P < 0.05), and (P < 0.001), respectively in both the groups at all intervals. Conclusion: At different time intervals, the total numbers of bacterial count of Streptococcus mutans were comparatively higher, followed by Lactobacillus sp. and Candida albicans.
pH modulation and salivary sugar clearance of different chocolates in children: A randomized clinical trial p. 10
SVSG Nirmala, Mohammed Akhil Quadar, Sindhuri Veluru
Background: Sugars that occur naturally in foods and those added in processed foods may act as the source for fermentable carbohydrates and may initiate caries process. Among all the foods consumed by children, chocolates form an important constituent. A wide variety of chocolates are available in the Indian market and very few studies have compared their acidogenicity and salivary sugar clearance. Objectives: To compare the acidogenicity and salivary sugar clearance of 6 different commercially available chocolates in the Indian market. Materials and Methods: Thirty subjects aged 10-15 years were selected randomly from one of the available public schools in Nellore city. Six commercially available chocolates in the Indian market were divided into three groups, unfilled (dark and milk chocolate), filled (wafer and fruit and nuts chocolate), and candy (hard milk and mango-flavored candy) groups. Plaque pH values and salivary sugar clearance rates are assessed at baseline, 5, 10, 15, 20, and 30 min after consumption. All the data obtained were statistically evaluated using independent sample t-test and one-way ANOVA for multiple group comparisons. Results: Mango-flavored candy had maximum fall in plaque pH and least fall in plaque pH was recorded with milk chocolate. Fruit and nuts chocolate had a maximum clearance of salivary sugar and least fall in the salivary sugar clearance was recorded with dark chocolate. When the plaque pH and salivary sugar clearance of all the chocolates were assessed, it was seen that the values were statistically significant at all the time intervals (P < 0.05). Conclusion: Dark chocolate had a high fall in pH and milk chocolate had low salivary sugar clearance which signifies that unfilled chocolates are more cariogenic than other chocolates. Even though mango-flavored candy had maximum fall in plaque pH, its salivary sugar clearance was high.
Correlation of plaque nitric oxide levels with plaque Streptococcus mutans, plaque pH and decayed, missing and filled teeth index of children of different age groups p. 17
Sawinderjit Saini, Hina Noorani, PK Shivaprakash
Context: Dental plaque is considered one of the most important etiological factors for dental caries. Nitrate and Nitrite levels in saliva are considered protective against oral and gastrointestinal disease. Plaque nitric oxide (NO) levels and its role in dental caries has not be explored in the literature. Aim: Determine the correlation of the plaque nitric oxide (NO) with dental caries in vivo. Materials and Method: 75 healthy children between ages of 3-15 years were selected. The age, state of dentition and the level of caries in all individuals were determined by the same examiner, using DMFT/deft index. The plaque samples collected were subjected to nitric oxide estimation, streptococcus mutans count and pH for correlation. Statistical Analysis Used: SPSS computer based software was used for descriptive data analysis. ANOVA one way analysis and Karl pearson's correlation was carried out. Results and Conclusion: Statistically significant relationship was observed between plaque nitric oxide and dental caries in all age groups. Similarly, significant correlation of nitric oxide was found in relation to plaque streptococcus mutans and plaque pH. Thus concluded, plaque nitric oxide can be considered as a risk assessment tool for prediction of dental caries.
An in-vitro evaluation of antibacterial effect of Amalgomer CR and Fuji VII against bacteria causing severe early childhood caries p. 23
Rajesh Hemant Bariker, Praveenkumar S Mandroli
Objective: To evaluate the antibacterial action of Amalgomer CR and Fuji VII against bacteria causing S-early childhood caries. Materials and Methods: Antibacterial activity of Amalgomer CR and Fuji VII was assessed using the agar diffusion test in triplicate. The powder and liquid of each test material was mixed and inserted in the punched wells (6 mm × 2 mm). A composition of 0.2% chlorhexidine digluconate acted as control. The agar plates were incubated at 37°C for 24 h for Streptococcus mutans, S. salivarius, S. parasanguinis and Actinomyces viscosus, whereas Lactobacillus casei was incubated for 48 h. Sizes of the inhibition zones were calculated by subtracting the diameter of the specimen (6 mm) from the average of the three measurements of the halo. For each test material against each bacteria, 9 measurements were made (3 measurements × 3 times). Kruskal-Wallis test was done to compare the zones of inhibition of test materials against individual bacteria. Pair-wise comparison was done by Mann-Whitney U-test. Results: Amalgomer CR had the most antibacterial against S. mutans (31.0 mm), followed by A. viscosus (21.87 mm), S. salivarius (13.87 mm), S. parasanguinis (10.80 mm), and L. casei (9.69 mm). Fuji VII had the most antibacterial action against S. salivarius (10.65 mm), followed by A. viscosus (9.10 mm). However, it did not inhibit the growth of S. mutans (0 mm), S. parasanguinis (0 mm), and L. casei (0 mm). Conclusion: Amalgomer CR and Fuji VII showed wide variation in antibacterial action against all test organisms.
A survey of the dentist attire and gender preferences in dentally anxious children p. 30
Alexander Asokan, Haalaswamy V Kambalimath, Raju U Patil, Sathish Maran, KP Bharath
Background: Anxiety about doctors among children is very common. White coat phobia which is a classical conditioning phenomenon is said to be prevalent among children. Objectives: To analyze the association between anxious states of children about dentists and their preference of dentist attire and gender in the dental office. Materials and Methods: The 9-12-year-old middle school children were selected for the study. Children were asked to fill a questionnaire containing children's fear survey schedule-dental subscale (CFSS-DS) and asked to prefer dental attire from four different pictures displaying the single anonymous dentist in four different attires in the same operator. The CFSS-DS gave the anxiety scale, and age, gender, and experience were also plotted against children preference in an apron, protective wear, and gender preference. Results were tabulated and statistically analyzed using Chi-square test. Results: About 718 (62%) were scored as anxious children. Of all anxious children, 502 (69.9%) had preference of colored attires of the dentist, and 408 (66.8%) anxious children preferred dentist with protective wear. Female dentists were preferred by 452 (66%) anxious children. Same gender preference was also prevalent.
Association of severe early childhood caries with iron deficiency anemia p. 36
Kalpana Bansal, Meetu Goyal, Renuka Dhingra
Background: Severe tooth decay is known to affect the health and well-being of young children. However, there has been minimal research showing the association of severe early childhood caries (S-ECC) and iron deficiency anemia (IDA). Aims: The primary aim of this study was to investigate an association between S-ECC with IDA. The secondary objective was to find an association between severe caries and body weight of the child. The oral health-related quality of life of children with S-ECC was also assessed. Materials and Methods: Following the ethical approval, 60 children aged 2-6 years (30 with S-ECC and 30 controls with caries status <2) were recruited for this cross-sectional association study. Each child received a clinical examination for dental caries status using deft index and a blood investigation to determine various parameters; hemoglobin (Hb), mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration, and packed cell volume (PCV). Children underwent venipuncture after due parental consent, and 2.5 mL blood was collected from each child to evaluate the above parameters. Following this, the parents filled up a 10 point questionnaire to determine the child's quality of life. Data were then analyzed by t-test and Fischer's exact t-test. Result: On comparison of percentage of children with IDA in S-ECC and control group, it was found that children with S-ECC were more likely to have IDA odds ratio (95% confidence interval): 10.77 (2.0, 104.9), (P = 0.001). In addition to this, S-ECC children were significantly more likely to have low Hb, MCV, and PCV levels (P < 0.001) which imply that S-ECC may be a risk marker for the development of anemia. More children (93%) with severe caries were found to have less body weight than ICMR standard weight for age as compared to children with low caries (P < 0.05%). Conclusion: S-ECC is strongly associated with the anemia due to iron deficiency, and efforts should be made toward the preventive and curative aspects of ECC, which may improve the general well-being and quality of life of a child.
Impact of verbal, braille text, and tactile oral hygiene awareness instructions on oral health status of visually impaired children p. 43
P Brahmanna Chowdary, KS Uloopi, C Vinay, V Veerabhadra Rao, Chandrasekhar Rayala
Background: Visually impaired children face limitations in interacting with the environment, as they cannot see the facial expression of parents, teachers and cannot perceive social behavior. These children are challenged every day in learning basic life skills and maintenance of oral hygiene being one among them. Aim: To evaluate the impact of verbal, braille text, and tactile oral hygiene awareness instructions on oral health status of visually impaired children. Materials and Methods: One hundred and twenty institutionalized visually impaired children aged 6-16 years were selected and divided into three groups (40 children each). Group I: Verbal and tactile, Group II: Verbal and braille, Group III: Verbal, braille, and tactile. Instructions regarding maintenance of good oral hygiene and brushing technique were explained to all the children, and oral health status of these children using plaque index (Silness and Loe) and gingival index (Loe and Silness) was evaluated at 1, 3, and 6 months interval. Statistical Analysis: ANOVA test was used to analyze the intra- and inter-group comparisons and Tukey post-hoc test for multiple group comparisons. Results: Children in all the groups showed reduction in plaque and gingival scores. There was the highest percentage of reduction in plaque scores in Group III (70.6%), and the decrease in gingival scores was the highest in Group II (84%). Conclusion: Severity of dental plaque and gingivitis in visually impaired individuals can be reduced by a controlled and supervised educational program. The combination of all three, i.e., verbal, braille, and tactile mode of oral health educational aids proved to be effective.
Cheiloscopy and dermatoglyphics as genetic markers in the transmission of cleft lip and palate: A case-control study p. 48
K Saujanya, M Ghanashyam Prasad, B Sushma, J Raghavendra Kumar, YSN Reddy, K Niranjani
Background: Determining the relative risk of cleft lip and palate (CL[P]) on the basis of lip prints and dermatoglyphics as genetic background may be useful for genetic counseling, and the development of future preventive measures. Aims and Objectives: (1) To analyze the various pattern types of lip prints and dermatoglyphics in parents of CL(P) children and to detect if any specific type can be contemplated as a genetic marker in the transmission of CL(P). (2) To compare these patterns with that of parents of unaffected children. Materials and Methods: 31 parents of children with CL(P) as a study group, and 31 parents of unaffected children as control group were included. Lip prints and finger prints were collected from all subjects and analysis of both patterns was carried out followed by a comparison of the patterns of unaffected parents with the controls statistically. Results: Among the mothers of the study group, type O followed by type IIa lip patterns were found to be significantly higher in upper and lower lips, and in fathers type IIa followed by type O were significantly higher. In the control group, type IIb followed by type III were higher in both fathers and mothers. Dermatoglyphic analysis of palm and finger prints revealed no significant difference in the pattern types and total ridge counts, but the Atd angle asymmetry was found to be significant between study and control group. Conclusion: Types IIa and O lip patterns, asymmetry of Atd angles can be considered as genetic markers for the transmission of CL(P) deformity to offsprings.
A clinical and radiographic study of four different root canal fillings in primary molars p. 55
Alaa O Al-Ostwani, Bashier M Al-Monaqel, Mohamed K Al-Tinawi
Background: Successful treatment of infected primary teeth aims to preserve the child's health. However, the complex morphology of primary root canals and the desire for shorter therapy sessions put the necessity to search for the ideal root canal paste. Aims: To evaluate pulpectomy of nonvital primary molars using four different root canal filling pastes zinc oxide and propolis (ZOP) as a new paste, endoflas-chlorophenol-free as a new paste free of chlorophenol, metapex paste, and zinc oxide and eugenol (ZOE) paste as a control paste. Materials and Methods: Pulpectomy of 64 nonvital primary molars were accomplished in 39 children aged 3-9 years. Teeth were randomly divided into four equal groups of 16 molars according to the type of root canal filling. Pulpectomy was performed in one stage using 5.25% sodium hypochlorite irrigation and stainless-steel crown for final restoration. Clinical and radiographic results were evaluated double-blindly for two periods of 6, 12 months. Data were analyzed using Chi-square/Fisher's exact test, and P-value was set as 0.05. Results: The four pastes achieved convergent clinical and radiographic success within the two observation periods (P > 0.05). ZOE paste was the slowest in its resorption. Conclusions: ZOP is a promising paste with its natural antibacterial component (propolis). ZOE paste had convergent efficacy to the other pastes.
Effect of the video output of the dental operating microscope on anxiety levels in a pediatric population during restorative procedures p. 60
Abrar Sayed, Vinisha Ranna, Dimple Padawe, Vilas Takate
Background: Adapting a child to the alien settings of a dental operatory is a major challenge to the dentist. Fear of the unknown and preconceived notions of dental pain causes anxiety in the pediatric patient. This often leads to disruptive and uncooperative behavior in the dental operatory. Many methods of behavior management have been described, of which the Tell-Show-Do (TSD) is an established and time-tested technique of behavior management. Aim: To determine if a live visual output of the dental operating microscope (DOM) could be used as an adjunct to the TSD technique, to involve the child more completely in the procedure and reduce the fear of the unknown. Materials and Methods: The study was a randomized, controlled, crossover, and cross-sectional clinical trial. Data were obtained from two visits. 90 children having carious lesions on both lower first molars, in the 7-9 years age group were selected and divided randomly into two groups. Restorative procedures were performed on one tooth per visit, with visits 1 week apart. Live display of the procedure was shown to the patient using video output of the DOM displayed on a 72 inch LCD monitor, angled for best visibility of the child. Anxiety levels were evaluated using Venhams picture selection test and pulse oximetry. Statistical Analysis: Student's t-test was used to compare the anxiety scores obtained from the two groups. Results: The results showed there was a decrease in the anxiety from the first visit to the second visit. (P = 0.05 for Group A and P = 0.003 for Group B). The patients preferred the visit in which the DOM was used. The operator reported an increased patient compliance and reduced patient movement in the visits in which the DOM was used. Conclusion: There is a reduction in anxiety from the first visit to the second visit for restorative treatment when the DOM is used.
Anti-microbial efficacy of green tea and chlorhexidine mouth rinses against Streptococcus mutans, Lactobacilli spp. and Candida albicans in children with severe early childhood caries: A randomized clinical study p. 65
Ann Thomas, Sneha R Thakur, Sowmya B Shetty
Introduction: Green tea is a beverage which is consumed worldwide and is reported to have anti-cariogenic effect. So, if it was as effective as chlorhexidine (CHX) mouth rinse against cariogenic microbes it could be considered a natural, economical alternative. The purpose of this study was to evaluate and compare the anti-microbial efficacy of 0.5% green tea and 0.2% CHX mouth rinses against Streptococcus Mutans, Lactobacilli spp. and Candida Albicans. Materials and Methods: 30 children aged 4-6 years with S-ECC (based ondefs score) were selected. Children were divided randomly into 2 equal groups and were asked to rinse with the prescribed mouth rinse once daily for 2 weeks after breakfast under supervision. A base-line and post rinsing non-stimulated whole salivary sample (2 ml) was collected and tested for the number of colony forming units. The data was statistically analyzed using SPSS v16.0 software with one-way ANOVA and Tukey'sPOSTHOC test. Results: A statistically significant fall in colony count was found with both the mouth rinses in Streptococcus Mutans (P < 0.001, P < 0.001) and lactobacilli (P < 0.001, P < 0.001) but not against Candida albicans (P = 0.264, P = 0.264). Against Streptococcus Mutans, green tea mouth rinse was found to be significantly better than CHX mouth rinse (P = 0.005). Against lactobacilli spp, CHX mouth rinse was significantly better than green tea mouth rinse (P < 0.001). Conclusion: Green tea mouth rinse can be considered safe, economical and used without much concern. However, further studies are recommended.
Comparison of linear and zero-inflated negative binomial regression models for appraisal of risk factors associated with dental caries p. 71
Manu Batra, Aasim Farooq Shah, Prashant Rajput, Ishrat Aasim Shah
Context: Dental caries among children has been described as a pandemic disease with a multifactorial nature. Various sociodemographic factors and oral hygiene practices are commonly tested for their influence on dental caries. In recent years, a recent statistical model that allows for covariate adjustment has been developed and is commonly referred zero-inflated negative binomial (ZINB) models. Aim: To compare the fit of the two models, the conventional linear regression (LR) model and ZINB model to assess the risk factors associated with dental caries. Materials and Methods: A cross-sectional survey was conducted on 1138 12-year-old school children in Moradabad Town, Uttar Pradesh during months of February-August 2014. Selected participants were interviewed using a questionnaire. Dental caries was assessed by recording decayed, missing, or filled teeth (DMFT) index. Statistical Analysis Used: To assess the risk factor associated with dental caries in children, two approaches have been applied - LR model and ZINB model. Results: The prevalence of caries-free subjects was 24.1%, and mean DMFT was 3.4 ± 1.8. In LR model, all the variables were statistically significant. Whereas in ZINB model, negative binomial part showed place of residence, father's education level, tooth brushing frequency, and dental visit statistically significant implying that the degree of being caries-free (DMFT = 0) increases for group of children who are living in urban, whose father is university pass out, who brushes twice a day and if have ever visited a dentist. Conclusion: The current study report that the LR model is a poorly fitted model and may lead to spurious conclusions whereas ZINB model has shown better goodness of fit (Akaike information criterion values - LR: 3.94; ZINB: 2.39) and can be preferred if high variance and number of an excess of zeroes are present.
Dental health in children with congenital bleeding disorders in and around Davangere: A case-control study p. 76
NB Nagaveni, Shruthi Arekal, P Poornima, Suresh Hanagawady, Sneha Yadav
Aim: The present study was carried out to investigate the dental and some other aspects of oral health status of young patients with congenital bleeding disorders (CBDs) and compared with controls. Materials and Methods: Decayed, missed, filled tooth surfaces (DMFS-dmfs) in permanent and primary teeth scores, simplified oral hygiene index, occlusion, occurrence of hypoplasia, fluorosis other hard tissue and soft tissue findings of 50 CBD patients at the age range of 4-15 years and 50 of other children as control were compared. Data were analyzed by Chi-square and Student's unpaired t-test. Results: Patients were significantly more caries-free with less decayed teeth in primary-permanent dentition (P < 0.05) and with lower scores for overall hygiene. Conclusion: By this, it can be concluded that children with CBD have a significantly lower prevalence of dental caries and better oral hygiene compared with matched, healthy controls.
Self healing hemophilic pseudotumor of the mandible in a 5-year-old boy, an interesting and rare finding: Case report and review p. 82
Ruchika Keshaw Prasad, B Siva, Jaisika Rajpal, Ankur Singh
Hemophilic pseudotumor (PT) is a very rare complication of hemophilia consisting of a chronic, encapsulated, hemorrhagic fluid collection occurring both in the soft tissues and/or bone. Radiological features of osseous hemophilic PT are nonspecific and mimic several other benign or malignant bone tumors or infectious processes. Although the diagnosis is usually made on the location of the lesion and by the knowledge of the underlying disease, the radiologist should be aware of the imaging characteristics, in order to avoid misinterpretation as a malignant tumor, as biopsy of these lesions is contraindicated. [1]
Central hemangioma: A case report and review of literature p. 87
Sandeep Jain, Sasidhar Singaraju, Medhini Singaraju
Hemangioma is a benign self-involuting tumor of endothelial cells. They are the most common benign congenital lesions in humans and are characterized by the proliferation of blood vessels. They are often present at birth or appear soon after, and grow rapidly by endothelial proliferation. This article presents a rare case of central hemangioma occurring in the mandible of a 13-year-old boy, and a brief review on clinical, radiological, and histopathological characteristics of central hemangiomas.
Fetal hydantoin syndrome: A case report p. 92
A Singh, HP Bhatia, A Mohan, N Sharma
Fetal hydantoin syndrome (FHS) is a spectrum of defects caused to the developing fetus by exposure to the teratogenic effects of antiepileptic drug (AED) phenytoin during pregnancy. Its clinical manifestations include limb abnormalities, ocular defects, central nervous system anomalies, intrauterine growth restriction, and hand and phalangeal anomalies. This case report presents an 8-year-old child born to an epileptic mother with a history of AED therapy, with features suggestive of FHS.
Dental root agenesis following radiation and antineoplastic therapy: A Case Report p. 96
Abdul Hafiz, Abdulla Mufeed, Punit Bharadwaj, Dinesh Rao
The survival rates of patients suffering from various childhood neoplasms have improved dramatically with the advent of chemo-radiation therapy. The harmful effects of chemo-radiation therapy in the oro-facial region such as root agenesis, short roots, impaired amelogenesis, dentinogenesis, radiation caries, and other soft tissue pathologies are well recognized. In spite of these documented risks, the antineoplastic treatment modalities continue to serve the patient for overall improvement in survival and quality of life. However, a thorough understanding of the growth and development process and its relation with the complex antineoplastic treatment is very important for all clinicians. Such awareness could significantly improve the status of patients in the posttreatment period with the implementation of proper preventive and interceptive measures. This article intends to document a case of root agenesis that developed secondary to chemo-radiation therapy in a 12-year-old girl.
A tribute p. 100
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Posted 8 November, 2017
Wednesday TV Ratings Wrap: Seven Records Lowest-ever Audience For Melbourne Cup
By B&T Magazine
Rekindling brought heartache to many punters who put their money on the favourites at the Melbourne Cup, and the race that stops the nation would've had Seven's executives smiling only a little.
Almost 1.8 million viewers tuned in to Seven's coverage of the Melbourne Cup yesterday, according to OzTAM's five-city metro figures.
Unsurprisingly, Melbourne had the most viewers with 849,000, followed by Sydney (377,000), Brisbane (255,000), Perth (202,000) and Adelaide (114,000).
The overall figure excludes regional viewers, as well as those who watched Rekindling seal victory from their office or at the local watering hole.
The 1.8 million also doesn't factor in the streamers, with Seven clocking 350,000 concurrent streams on 7Live – up 22 per cent on 2016, and making it the biggest streaming event of the year so far.
However, Seven's streaming success seemingly had an impact on TV broadcast figures, with this year's Melbourne Cup recording the lowest-ever audience.
Promos for The Good Doctor during the Melbourne Cup coverage helped the Seven show win the primetime slot with 1.19 million viewers, and finish streaks ahead of Nine's The Big Bang Theory and the ABC's Keeping Australia Safe.
Seven's new show, Instant Hotel, also tasted success, winning its slot with just 695,000 viewers against Nine's Family Food Fight, the ABC's 7.30 and Jamie's Quick and Easy Food on Ten.
Melbourne Cup OzTam Seven TV Ratings

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: How to parse JSON in jQuery I am using the following code
<script>
$(document).ready(function(){
    $('.v_modalLink').click(function(){
        var journalId=$(this).attr('data-id');
        $.ajax({
            type:"POST",
            url:"<?php echo base_url();?>index.php/pranasInventory/view_invoice/1",
            data:{journalId:journalId},
            success:function(data)
            {
                var json_obj=$.parseJSON(data);
                for(var i in json_obj)
                {
                    voucherId=json_obj[i].invoice_id;

                }

            }
        });
    })
});
</script>

I want to assign the each responce value in each variable Ex:clientName="dxx";cleitPhone="eerere"
I have the following console response:
{
    "invoice": [{
        "invoice_id": "1",
        "client_id": "1",
        "client_name": "makemytrip",
        "client_phone": "1234567891",
        "client_email": "info@makemytrip.com",
        "client_address": "Banglore.",
        "service_name": "Mobile   Apps",
        "service_description": "asdasd asasd  asdads asdasdads.",
        "created_date": "2016-08-10",
        "gross_amount": "1000000",
        "tax_amount": "145000",
        "net_amount": "1145000",
        "tax_identifier_name": "Service Tax 14.5%"
    }],
    "myarray": [{
        "taxname": "Service tax @ 14%",
        "taxamount": 140000
    }, {
        "taxname": "swachh bharat cess @  0.5%",
        "taxamount": 5000
    }],
    "rowcount": 2
}

How can I parse the above data in jQuery? Thanks.

A: You just need to change your success response to
success:function(data)
{
    var json_obj = JSON.parse(data);

    var voucherId = json_obj['invoice'][0].invoice_id;
    var clientName = json_obj['invoice'][0].client_name;
    var clientPhone = json_obj['invoice'][0].client_phone;

    //myarray

    var taxName1 = json_obj['myarray'][0].taxname;
    var taxAmount1 = json_obj['myarray'][0].taxamount;
    var taxName2 = json_obj['myarray'][1].taxname;
    var taxAmount2 = json_obj['myarray'][1].taxamount;

    //rowcount

    var rowCount = json_obj.rowcount;
}

A: Set response to variable and use the variable in your jQuery method.

redpajama_set_name: RedPajamaStackExchange


Dame is the same thing as a Knight is for a man, except when a woman is knighted in the United Kingdom, she is addressed as Dame whatever, just as a knight would be addressed as Sir Alexander, or... There comes a time in every young female's life when she must put away her dolls and hair ribbons and become the woman she was born to be. With the proper grooming and etiquette, a girl can easily find her footing in this new world of womanhood.
7/07/2007 · Become "British" over the summer. Tell people you went to Great Britain over the summer and you've grown accustomed to their ways. Tell people you went to Great Britain over the summer and you've grown accustomed to their ways.... 3/04/2017 · I'm British by birth but arrived in the Philippines after living in the US for twenty years. I came with just $2000 so all you see has been achieved with little in terms of dollars.
Dame is the same thing as a Knight is for a man, except when a woman is knighted in the United Kingdom, she is addressed as Dame whatever, just as a knight would be addressed as Sir Alexander, or how to create a game environment Lady Diana Spencer, who famously married Prince Charles to become the Princess of Wales, was the daughter of Earl Spencer, head of one of the oldest, wealthiest aristocratic families.
usage: In the meanings "refined, polite woman" and "woman of high social position" the noun lady is the parallel of gentleman. As forms of address, both nouns are used in the plural (Ladies and gentlemen, thank you for your cooperation), but only lady occurs in the singular.
W hen two children, a six-year-old girl and a slightly older boy, visited her flight deck last week, British Airways pilot Aoife Duggan asked if they would like to fly planes too.
Lady Diana Spencer, who famously married Prince Charles to become the Princess of Wales, was the daughter of Earl Spencer, head of one of the oldest, wealthiest aristocratic families.


HMA Ships Stuart and Arunta, usually based on opposite sides of Australia, met when they were both in Busan, South Korea.
Since departing Fleet Base West in late August, Arunta has had a busy program exercising and conducting visits to India, Malaysia, Philippines and Japan. Stuart's crew had similarly travelled from Yokosuka, Japan having participated in the Japanese International Fleet Review.
The review showcased the latest assets of the Republic of Korea Navy fleet, and culminated in a simulated attack on a submarine from maritime aircraft which included the launching of missiles and torpedos.
The two ships will now team up to form an Australian Task Group and conduct the rest of their North East Asian deployment in company, with replenishment ship HMAS Sirius to join up in several weeks time as well.
Stuart and Arunta are both Anzac class frigates capable of air defence, surface and undersea warfare, surveillance, reconnaissance and interdiction. They are currently undertaking a North East Asia deployment to Japan, South Korea and China.
Posted on October 29, 2015 with tags Australian Navy, HMAS Arunta, HMAS Sirius, ROK Navy.


In response to yesterday's post, Reader Kevin asked me to share the Excel spreadsheet that I use for tracking adjusted cost base (ACB) and capital gains. Since I mostly make buy transactions and rarely do a sell transaction these days, my set up is rather simple. I simply input the date, type of transaction (buy, sell or ROC), no. of shares, price / share and commission and compute the ACB and share balance. Each stock goes into a separate sheet and US stocks have an additional column for the exchange rate.
If you haven't calculated ACB before, it is quite simple. When you make a buy transaction, you should add the total value of the current transaction to the previous ACB. For instance, let's say you own 300 shares of XYZ Corp. at an ACB of $3,000. If you buy another 100 shares of XYZ Corp. for a total of $1,500, you'll own 400 shares of XYZ Corp. at an ACB of $4,500.
When you make a sell transaction, your ACB decreases by the number of shares sold times the average ACB per share. For instance, if you had instead sold 100 shares of XYZ Corp., you'll now own 200 shares at an ACB of $2,000. You'll also have a capital gains of $500.
Return of Capital simply reduces the ACB by the number of shares times ROC per share. For instance, if XYZ Corp. had a return of capital of $1 per share, your ACB would be $2,700 ($3,000 – 300 shares * $1). If you need more detailed explanation of ACB, check out Gummy Stuff's Adjusted Cost Base for Canadians. DRIP Primer also has a handy explanation of ACBs for those who reinvest their dividends.
To make Kevin happy, I cleaned up my Excel spreadsheet and added some code to automatically handle buy, sell and Return Of Capital transactions. All you need to do to track your ACB, is copy the cells into a new sheet and enter the Date, Transaction, # Shares, Price / Share, Exchange rate (if applicable) and Commission columns. The spreadsheet should calculate ACB, Share Balance and Capital Gains columns. Just copy and paste the previous row to create a new transaction. You can download the spreadsheet here. Comments are welcome.
There is no limit to how complicated these stock transaction spreadsheets can become. For example, I used to have to track stock options and deferred stock option gains. The critical thing is to learn enough to be able to create your own spreadsheet. Start with CC's spreadsheet, but be prepared to know how to modify it to suit your own situation.
@Michael: I agree that this is just a starting point. It can become a lot fancier depending on how fancy one wants to make it. I've been meaning to clean up my spreadsheet for a long time because it is better to get it done right once instead of doing it manually and increasing the chances of errors.
@CC: I agree that automation is best, but it is hard to anticipate all possible events. There are stock splits, reverse splits, share distributions, returns of capital, spinoff of shares of other stocks (such as when BCE gave Nortel shares to its shareholders), and other even more exciting possible events. The latest update to stock option gain deferral rules forced me to add a new column to my spreadsheet. So, while I do my best to automate, I find it necessary to update my spreadsheet periodically.
The one I like is that the issuer doesn't tell you how much of your distribution is Return of Capital (ROC) until tax time… So, you don't actually know the full tax implications of your trading until April 30 or whatever.
Like you, I try to make my life simple by making sure that I never sell the investments (mostly REITs) whose distributions contain a lot of ROC, unless they're inside my RSP or whatever.
DRIPers can use CC's spreadsheet, by simply entering a reinvested dividend as a 'buy'.
Thats not strictly correct is it?
When I go to sell, you don't claim capital gains on the average ACB, but base it on the original purchase price for what I'm selling. So if I decide a year later to sell 100 shares of XYZ, I can elect to dispose of the 100 at $1500, or 100 at $1000 (or even a different mix if you are like pain :-). Depending on how much extra gain/loss you want to claim, the remaining 300 shares ACB will then be either $3000 or $3500.
So don't you have to treat the sells exactly like buys, only with negative quantity with original cost of your *chosen* units, and base you gain/loss from that?
@highlander: That's not correct. When you sell, CRA requires you to claim capital gains or losses based on the ACB. There is no leeway here. You can't choose FIFO, LIFO or whichever you find convenient. If you think that's not how CRA treats capital gains and losses, can you point me to the relevant source?
I was wondering if this tool could be enhanced to support journaling from an exchange to another and still keep tracking the cost back to the original currency the securities were purchased. Journaling complicates the tracking of the ACB and also the performance of a holding. Also, it is my understaing that if a security is interlisted, then driving currency is the one the headoffice is based on, e.g. TD/TSX fluctuates based on sell/buy activity; TD/US fluctuates based on sell/buy activity AND currency exchange; alternatively, lets say GM (when coming back to public), since the head office is in the US, will behave in reverse.
@CC: Thanks for the spreadsheet!
I looked at the US Stock tab, it seems it makes an assumption that the Comission is charged in CAD. If I recall correctly some brokers charge the Comission in USD in case of US stock buys/sells. I suppose I should adjust the calculation in my case?
OOps. my comment is only regarding line #2. Starting from line #3 the formula does account for Comission charged in USD.
@G: Good catch. Thanks for pointing out the error. I've fixed it now and uploaded the edited version.
How would you best show a stock split, with this format of your spreadsheet, and thanks for this workmanship, it has sure helped me out good job.
@Canadian Capitalist: Ericj is right, if you are investing with ETFs, you need to include the re-invested distributions in the calculation of ACB.
Investing in ETF's and Mutual funds require more complexity that I would love to see added to the spreadsheet.
The spreadsheet calculates Capital Gains only when the stock is sold, however with ETFs or Mutual Funds these Capital Gains occur often within the fund as the fund manager buys and sells stocks. An investor will receive T3/T5s indicating gains while he is holding the fund, and he must track this to determine his ACB. When this occurs, the taxes have to be paid in the year the gains occur but they also result in an increase in the ACB by that amount, unless there was also some Return of Capital in which case the amount of ROC decreases the ACB. Similarly, if the stock/ETF/fund declares a dividend that is re-invested, then that amount also increases the ACB — but again, you pay tax in the year it occurs even though you didn't receive it. Even interest gained within the fund (say for money the fund manager has sitting while he waits to purchase stocks) must be taxed in the year they occur but will result in an increase in the ACB as long as it is not.
It's really important to track these gains as they occur within a fund, and updating your ACB, or else an investor will end up understating the value of the funds and will end up paying unnecessary capital gains when he sells (i.e. paying twice for the capital gains that occurred within the fund).
I found the answer. I edit the cell to increase the decimal places.
I trade actively (more than a thousand transactions per year). Rather than track the ACB on each position (often held a week or two, not longer), can I not measure the gain on the overall portfolio during the year, and treat the gain as a capital gain (as if the portfolio were one stock)?
If you spend $4000 and re-invest $1000, the ACB increases by $1000 to $101,000. Am I right so far?
But what if you spend $10,000? The portfolio made $5000, you paid tax and spent the entire $5,000. No change to the ACB.
But you also spent another $5,000. This is all return of capital; none of this money is taxable, right?
Is the ACB also reduced by $5000 (to $95,000)?
You buy 100,000 shares of XYZ at $1.00 each. Your ACB on 100,000 shares is $1.
XYZ goes up to $1.05. You sell 100,000 shares at $1.05 each. Your proceeds are $105,000.
You spend $4,000 of your proceeds. You buy 1,000 shares of RST at $101.
Now, you sell 100 shares of RST at $100. You have a loss of $100. ACB of RST remains at $101. You have $10,000 that you spend. You have 900 shares of RST left.
I started a TRP drip in my name. After a year I also started a drip joined account (with my husband) using one share from the drip in my name. Now I have 2 TRP drips (one in my name and one with my husband) and I am wondering how to calculate ACB. Your help is much appreciated.
Thanks for all the great information and comments.
My question is, if you have bought and sold a stock multiple times in the year and have kept track of the ACB using the spreadsheet, do you aggregate all sales for that stock into one disposition and ACB when you are filling out your taxes using software such as SimpleTax or do you need to do a separate disposition for each sale of that stock individually?
Now how would you calculate this scenario in this sequence?
Thanks you so very much! This little spreadsheet app was just the ticket for my needs!


text:
``defined``
===========

``defined`` checks if a variable is defined in the current context. This is very
useful if you use the ``strict_variables`` option:

.. code-block:: jinja

    {# defined works with variable names #}
    {% if foo is defined %}
        ...
    {% endif %}

    {# and attributes on variables names #}
    {% if foo.bar is defined %}
        ...
    {% endif %}

    {% if foo['bar'] is defined %}
        ...
    {% endif %}

When using the ``defined`` test on an expression that uses variables in some
method calls, be sure that they are all defined first:

.. code-block:: jinja

    {% if var is defined and foo.method(var) is defined %}
        ...
    {% endif %}

redpajama_set_name: RedPajamaGithub


text:
Now Viewing : Tackling Covid-19 Disparities with Collaboration
Tackling Covid-19 Disparities with Collaboration
June 12, 2020 • Aspen Ideas Now & Aspen Global Innovators Group
The coronavirus pandemic is exposing deep health inequities across the US, with the highest rates of transmission among Native American, Black, Latinx, and economically vulnerable populations. Kirsten Bibbins-Domingo and Sam Hawgood of UC San Francisco share how one of the nation's largest health systems is taking discrete steps to support the country's most overlooked communities. What can cities and health systems learn from one of the country's top medical schools? Presented by Aspen Global Innovators Group
Kirsten Bibbins-Domingo, PhD, MD, MAS is the Professor and Chair of the Department of Epidemiology and Biostatistics, and the Lee Goldman, MD Endowed Chair and Professor of Medicine at the University of California, San Francisco (UCSF). She is the inaugural Vice Dean for Population Health and Health Equity in the UCSF School of Medicine. She co-founded the UCSF Center for Vulnerable Populations at Zuckerberg San Francisco General Hospital that focuses on actionable research to increase health equity and reduce health disparities in at risk communities. She is one of the Principle Investigators for the UCSF Clinical and Translational Sciences Institute, and she leads the newly launched UCSF COVID Community Public Health Initiative.
Sam Hawgood, MBBS, is the Chancellor and the Arthur and Toni Rembe Rock Distinguished Professor at the University of California, San Francisco (UCSF). Hawgood was dean of the UCSF School of Medicine and vice chancellor for medical affairs from 2009-2014, after assuming the role of interim dean in December of 2007. His distinguished career at UCSF includes serving as chair of the Department of Pediatrics and associate director of the Cardiovascular Research Institute. Hawgood joined UCSF as a research fellow in 1982 and was active in research until 2015. Hawgood maintained an active presence in clinical medicine for almost thirty years including serving first as division chief of the Division of Neonatology, then as chair of Pediatrics and physician-in-chief of the UCSF Benioff Children's Hospital prior to becoming dean. He was also president of the UCSF Medical Group, the faculty association that represents more than 1,800 physicians. Under his leadership, the school become the top medical school in the nation in research funding from the National Institutes of Health ($439.6 million in 2013), with many of its departments also leading the nation in their fields, reflecting the caliber of scientific research on campus. As chancellor, Hawgood oversees the entire $6.7 billion UCSF enterprise, which also includes top-ranking schools of dentistry, nursing, and pharmacy, as well as a graduate division and affiliated hospitals.
Aspen Global Innovators Group ,	Aspen Ideas Now
coronavirus ,	COVID-19 ,	public health
What's next in the fight against Covid-19?
July 9, 2020 Aspen Ideas Now
Latinos Are Not Causing the Covid-19 Resurgence
June 29, 2020 Lara Kinne
The 2019 Aspen Ideas Incubator
Health For All: Nurse Innovators Leading the Way
16 Books Up for the Aspen Words Literary Prize
ERISA Safe Harbor for States
Reinventing Restaurant Work: An Overdue…


text:
America�??s Obamanomic transformation to part-time work continues
Eyebrows are vanishing into hairlines from coast to coast over a story in the Orlando Sentinel, in which a restaurant chain experiments with moving to a part-time workforce�?� a move explicitly intended to help the company escape from the horrors of ObamaCare:
In an experiment apparently aimed at keeping down the cost of health-care reform, Orlando-based Darden Restaurants has stopped offering full-time schedules to many hourly workers in at least a few Olive Gardens, Red Lobsters and LongHorn Steakhouses.
Darden said the test is taking place in "a select number" of restaurants in four markets, including Central Florida, but would not give details. The company said there has been no decision made about expanding it.
In an emailed statement, Darden said staffing changes are "just one of the many things we are evaluating to help us address the cost implications health care reform will have on our business. There are still many unanswered questions regarding the health care regulations and we simply do not have enough information to make any decisions at this time."
Darden might be able to solve this problem by relocating into Nancy Pelosi�??s congressional district, where ObamaCare waivers are easy to come by, but maybe they like being based in Florida. It�??s not just this one company transitioning to part time, either:
Analysts say many other companies, including the White Castle hamburger chain, are considering employing fewer full-timers because of key features of the Affordable Care Act scheduled to go into effect in 2014. Under that law, large companies must provide affordable health insurance to employees working an average of at least 30 hours per week.
If they do not, the companies can face fines of up to $3,000 for each employee who then turns to an exchange �?? an online marketplace �?? for insurance.
"I think a lot of those employers, especially restaurants, are just going to ensure nobody gets scheduled more than 30 hours a week," said Matthew Snook, partner with human-resources consulting company Mercer.
Darden�??s previous health insurance plan is banned under ObamaCare�??s mandates. But�?� but�?� Barack Obama said that if you like your plan, you can keep your plan! In fact, he�??s still saying it, even as his rhetoric sours from political spin into bitter farce.
Once again, the childish fantasies of socialists have run into hard, cold economic reality. Incentives produce responses. Increasing the cost of anything, including full-time labor, reduces demand. Behavior tailored to avoid cumbersome mandates and tax increases is less beneficial for all concerned, but at a certain point, such behavior becomes inevitable. It�??s great to be able to offer full-time positions and extra hours to your best workers, but if the government makes that unaffordable, there are alternative strategies.
What�??s the Obamanomics solution to the disastrous changes his policies have been wreaking in the American workforce? Nationalize the restaurant industry? Pass a law requiring a certain percentage of every staff be given full-time positions�?� followed by a bailout of bankrupt restaurants, or maybe some sneering editorials telling Americans to stop complaining about dropping fifty bucks to eat at White Castle?
Do you want to improve the employment situation in America? There is absolutely no doubt about what Step One needs to be: repeal ObamaCare.
Carter and Hollywood relish in supporting leftist-strongman Chavez
Cheney, Ben Romney top bill of pre-Veep debate party
America's Obamanomic transformation to part-time work continues
Eyebrows are vanishing into hairlines from coast to coast over a story in the Orlando Sentinel, in which a restaurant chain experiments with moving to a part-time workforce… a move explicitly intended to help the company escape from the horrors of ObamaCare:
Darden might be able to solve this problem by relocating into Nancy Pelosi's congressional district, where ObamaCare waivers are easy to come by, but maybe they like being based in Florida. It's not just this one company transitioning to part time, either:
If they do not, the companies can face fines of up to $3,000 for each employee who then turns to an exchange — an online marketplace — for insurance.
Darden's previous health insurance plan is banned under ObamaCare's mandates. But… but… Barack Obama said that if you like your plan, you can keep your plan! In fact, he's still saying it, even as his rhetoric sours from political spin into bitter farce.
Once again, the childish fantasies of socialists have run into hard, cold economic reality. Incentives produce responses. Increasing the cost of anything, including full-time labor, reduces demand. Behavior tailored to avoid cumbersome mandates and tax increases is less beneficial for all concerned, but at a certain point, such behavior becomes inevitable. It's great to be able to offer full-time positions and extra hours to your best workers, but if the government makes that unaffordable, there are alternative strategies.
What's the Obamanomics solution to the disastrous changes his policies have been wreaking in the American workforce? Nationalize the restaurant industry? Pass a law requiring a certain percentage of every staff be given full-time positions… followed by a bailout of bankrupt restaurants, or maybe some sneering editorials telling Americans to stop complaining about dropping fifty bucks to eat at White Castle?


text:
\section{Introduction}

RNA molecules accomplish a plethora of functional roles in the cell, their function being dictated not only by their sequence and structure but also, to a large extent, by their dynamical behavior \cite{al2008rna,mustoe2014hierarchy}.
Molecular dynamics (MD) simulations have nowadays reached the status of a standard tool to explore the dynamics of biomolecular structure at the atomistic level \cite{dror2012biomolecular,sponer2018rna}.
Nevertheless, inaccuracies in the force fields and limitations in terms of the accessible timescales often make their capability to reproduce and to predict experimental results limited \cite{sponer2018rna}. The combination of MD simulations with experimental data is thus emerging as a robust asset to characterize the conformational dynamics of relevant biomolecules \cite{pitera2012use,bonomi2016metainference,bonomi2017principles,bottaro2018biophysical,cesari2018using}
including RNAs \cite{borkar2016structure,borkar2017simultaneous,kooshapur2018structural,paissoni2019martini,bottaro2020integrating,reisser2020conformational}.
Here, MD simulations can be seen as a powerful tool that complements experimental data making it possible to add dynamical information to experiments that report ensemble averages. This can be even more important when using low-resolution experiments such as small-angle X-ray scattering (SAXS) \cite{chen2016saxs,hub2018interpreting}.
Here, the synergy between MD and experiment allows faithful structural ensembles at atomistic resolutions to be generated \cite{kofinger2013atomic,chen2015structural,kooshapur2018structural,hermann2019saxs,paissoni2019martini,paissoni2020determination,jussupow2020dynamics,ivanovic2020small}.
SAXS experiments are particularly valuable in capturing the structural impact of changes in the ionic conditions,
that are highly relevant for RNA but poorly described by force fields \cite{sponer2018rna}.
Importantly, in the small-angle regime, most of the contribution to the overall SAXS is expected to originate from the solute.
Thus, from a computational standpoint, it is common practice to compute SAXS spectra using \emph{forward models}, \emph{i.e.}, equations to back-calculate the experiment from the simulated structures, based on the solute atomic coordinates only and including corrections to implicitly account for the solvent \cite{svergun1995crysol,schneidman2010foxs,nguyen2014accurate}. This choice reflects a compromise with the intensive effort that is typically involved to include the solvation explicitly in the computation. In recent years, methods have been devised that allow computing SAXS spectra including the solvent contribution through relatively efficient implementations, aiming at predicting SAXS spectra as accurately as possible
\cite{grishaev2010improved,kofinger2013atomic,knight2015waxsis,marchi2016first,hermann2019saxs,park2009simulated}.
This can be particularly critical when dealing with highly charged biomolecules, such as RNA, whose effect on the surrounding solvent and on the ionic cloud can be sizable up to a distance of several nanometers \cite{chen2009molecular,ivanovic2018quantifying}.
A possible route to combine MD and experimental data is to enforce the reference experimental data during the MD simulations. However, in the context of SAXS data, this option is hindered by practical limitations in the forward models. Indeed, whereas the on-the-fly estimate of the SAXS spectra is affordable for the pure solute, explicit solvent estimators have been used sparsely in this context \cite{hermann2019saxs}.

In this work, we introduce a protocol exploiting pure-solute forward models \cite{paissoni2019martini} and enhanced sampling \cite{mlynsky2018exploring} during MD simulations, to favor sampling of an heterogenous ensemble, without explicitly using the experimental data on-the-fly. The ensemble is then reweighted \cite{cesari2018using} to match experimental data using accurate explicit-solvent forward models \cite{kofinger2013atomic}.
We apply this framework to the GTPase-associated center (GAC), a 57-nucleotides-long RNA molecule of the 23S ribosomal subunit that is involved in protein translation \cite{moazed1988interaction,gao2009structure,weixlbaumer2008insights}. Recently, SAXS experiments reported on GAC structural flexibility in response to different ionic conditions in the buffer solution, noticing that Mg$^{2+}$ can stabilize the folded state, while K$^+$ favored less compact and more extended conformations \cite{welty2018divalent}. Through our protocol, we notice that explicit-solvent SAXS spectra are necessary to correctly reconstruct the ion-dependent structural ensembles and to obtain radii of gyration through Guinier fit that are compatible with the experiments (Figure \ref{figure_1}). In particular, in the case of K$^+$, we observed that a mixture of compact and extended structures is necessary to generate a structural ensemble that is in agreement with the experimental SAXS spectra.

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{figure_1.png}
\end{center}
\caption{Schematic pipeline of the protocol introduced in this work. Enhanced-sampling MD simulations with explicit solvent are initially employed
	to sample RNA conformations of different structural compactness. At this stage, pure-solute forward models for SAXS are used as collective variables in a metadynamics protocol to guide the sampling,
	with no information from experiments. We then compute \emph{a posteriori} the full SAXS spectra for the sampled structures, to be used as input in
	the subsequent reweighting procedure. Notably, the SAXS spectra can either be computed from the pure solute only (top orange arrow) or including
	the explicit solvent (bottom sky-blue arrow).
	Experimental data are finally enforced through the maximum entropy principle to reweight the original ensemble (prior, blue rounded rectangle) and thus identify the least-modified ensemble
	(posterior, green rounded rectangle) that is in agreement with the experimental reference.
	Remarkably, we observe that the inclusion of the solvent in the computation of the SAXS spectra is a critical factor to achieve a successful
	reweighting (bottom green arrow).
	}
\label{figure_1}
\end{figure*}

\section{MATERIALS AND METHODS}

\subsection{Simulation details}
All systems were prepared from the available crystal structure of GAC in its folded state (PDB ID: 1HC8 \cite{conn2002compact}, see Figure \ref{figure_2})
after removing the bound protein. We notice that a recent RNA-only crystal structure is virtually identical \cite{welty2020ribosomal}.
The system was described using the AMBER force field for nucleic acids \cite{cornell1995second,perez2007refinement,zgarbova2011refinement}, the 4-point optimal-point-charge (OPC) model for water \cite{izadi2014building}, and compatible ion parameters \cite{Joung2008determination,allner2012magnesium}.
4-point water models have been reported to improve the accuracy of simulated hydration effects in molecular systems.
This is particularly critical for biomolecules exploring heterogeneous ensembles including more extended structures, where an accurate representation of solute-water interactions is crucial to avoid overly compact conformations
\cite{best2014balanced,palazzesi2015accuracy,shabane2019general,hermann2019saxs}.
The OPC model has been shown to improve the agreement of simulation and experiment for unstructured RNA tetramers
\cite{bergonzo2015improved,bottaro2018conformational}.
All simulations were performed using GROMACS 2018.4 \cite{abraham2015gromacs}.
Four ionic conditions were considered with an increasing number of Mg$^{2+}$ ions, namely: 1. none, 2. crystallographic Mg$^{2+}$, 3.
crystallographic Mg$^{2+}$ plus half the amount needed to neutralize the system and 4. crystallographic Mg$^{2+}$ plus the amount needed to
neutralize the system (a representative sketch of the 4 systems is provide in Figure \ref{figure_3}a). In all cases, the crystal K$^+$ was retained, a 100 mM concentration of KCl was set for the buffer, and neutralization, where needed, was achieved by adding K$^+$ accordingly.
The systems were initially minimized applying soft position restraints on RNA and water heavy atoms and on the crystal ions.
A multi-step equilibration was then conducted: 3 short simulations lasting 200 ps at 100, 200 and 300 K in the NVT ensemble using the velocity-rescaling thermostat \cite{bussi2007canonical}, with soft position restraints on RNA heavy atoms and on crystal ions; in further 900 ps in NPT ensemble using the Parrinello-Rahman barostat \cite{parrinello1981polymorphic}, these restraints were gradually removed to relax the system. Production runs were performed in the same ensemble.
Short unbiased MD simulations (10 ns) were run using a large rhombic dodecahedron simulation box containing approximately 170000 atoms, with edges distant 3 nm from all RNA atoms, using soft position restraints on solute heavy atoms to exclude solute dynamics effects. Longer unbiased MD simulations (1 $\mu$s) were performed using a smaller rhombic dodecahedron simulation box containing approximately 100000 atoms,
with edges distant 2 nm from all RNA atoms, with no position restraints, for the ionic conditions 1 and 4 (Figure \ref{figure_3}a) described above.

\begin{figure}
\begin{center}
\includegraphics[width=\columnwidth]{figure_2.png}
\end{center}
\caption{GAC RNA structure. The stem regions are consistently color coded in the secondary (left panel) and tertiary (right panel) structure
	representation. In the tertiary structure (PDB ID: 1HC8), the RNA backbone is represented as tube, stem heavy atoms are shown in licorice and the
	overall shape of the molecule in depicted in shaded gray.
	}
\label{figure_2}
\end{figure}

\subsection{Enhanced sampling}
Enhanced sampling MD simulations \cite{mlynsky2018exploring} were conducted on the system with no Mg$^{2+}$ (Figure \ref{figure_3}a) using the larger rhombic dodecahedron simulation box (approximately 170000 atoms). The Hamiltonian replica exchange (HREX) method~\cite{sugita2000replica} with scaling applied to selected residues \cite{bussi2014hamiltonian} (non-stem residues, Figure \ref{figure_2}, left panel in grey) was used to relax the contacts of regions outside of the stems. The lambda scaling factors were in the range 0.7--1.0 and distributed on 16 replicas according to a geometric progression. To ensure charge neutrality at each replica, the missing charge was spread over the dummy atom of all water molecules. Exchanges between neighboring replicas were attempted every 400 MD steps. HREX was combined with metadynamics \cite{laio2002escaping,camilloni2008exploring,bussi2020using}, where two
collective variables (CVs) were biased: the Ratio between peak ($q=0.1$ \AA$^{-1}$) and shoulder ($q=0.2$ \AA$^{-1}$) of the pure-solute SAXS spectrum in the Kratky form computed using the MARTINI model \cite{paissoni2019martini} and an additional variable (Diff) purposely designed to estimate the degree of formation of tertiary contacts in the RNA molecule.
Diff is defined as the root mean square of the G-vectors introduced in \cite{bottaro2014role} and can be computed
in practice taking the root-square difference of the eRMSD \cite{bottaro2014role,bottaro2016free} with respect to an arbitrary structure with no contacts formed,
computed using either the whole sequence or only the stem regions (see Figure S1).
Gaussians with height 2.09 kJ mol$^{-1}$ and $\sigma$ of 0.035 and 0.05 were deposited every 400 steps with a bias factor of 10 \cite{barducci2008well}.
In order to decrease the computational overhead of computing collective variables, the bias was applied every second step \cite{ferrarotti2015accurate}.
Stem regions were restrained setting an upper wall on the eRMSD with respect to the initial state at 0.7 using a force constant of 41.84 kJ mol$^{-1}$.  Upper (at 25 \AA) and lower (at 16 \AA) wall restraints were also applied to the $R_g$ with force constant 41.84 kJ mol$^{-1}$ nm$^{-2}$. Finally, an upper wall on the Ratio CV was placed at a value of 2.7 with a force constant of 41.84 kJ mol$^{-1}$. The whole setup was achieved through the open-source, community-developed PLUMED library \cite{tribello2014plumed,bonomi2019promoting} version 2.5. The simulation was conducted in the NVT ensemble for 180 ns/replica. Weights were computed a posteriori using the final bias \cite{branduardi2012metadynamics}. Representative sampled structures were selected taking advantage of the Quality Threshold (QT) clustering algorithm \cite{gonzalez2019quality}.

\subsection{Backcalculation of SAXS spectra}
The SAXS spectra for all the structures sampled during the MD simulation were computed in the $q$ range 0--0.5 \AA$^{-1}$ with a pace of 0.01 \AA$^{-1}$. Pure solute spectra for the short (10 ns), long (1 $\mu$s), and enhanced sampling simulations were computed on the all-atom structures with PLUMED, relying on a MARTINI bead representation of the system as recently introduced \cite{paissoni2019martini}. Notably, the method has been shown to produce coincident results in the small angle regime
that is relevant here (0$<q<$0.3 \AA$^{-1}$)
when compared with the all-(solute)-atom calculation (see also Figure S2).
Crysol spectra were computed for the short simulations using a maximum order of harmonics of 20 and default parameters, with the software version 2.8.4 \cite{svergun1995crysol}. Waxsis spectra were computed for the short simulations with an envelope constructed at a distance of 7 \AA{} from the RNA molecule and using default parameters, as implemented in the modified version of GROMACS 4.6.2 \cite{knight2015waxsis}. To compute the SAXS spectra with Waxsis, 1000 frames from the 10 ns simulations were used, along with 1000 frames from an independent pure-solvent simulation with the same salt concentrations and lasting 5 ns. Capriqorn spectra were computed for the short (10 ns), long (1 $\mu$s), and enhanced sampling simulations through the method introduced by Köfinger and Hummer \cite{kofinger2013atomic} implemented in the Capriqorn software (https://github.com/bio-phys/capriqorn) version 1.0.0. The method first computes separately the radial distribution function from a pure-solvent simulation with the same salt concentrations, for which purpose we used the same 5 ns pure-solvent simulations as for Waxsis. For the short and long simulations, a sphere geometry with radius 40 \AA{} was used along with a shell for solvent matching of width 7 \AA; for the enhanced sampling simulations, a sphere radius of 54 \AA{} and shell width of 10 \AA{} was employed (see Figure S3 for a schematic depiction).
$R_g^*$ was computed from the SAXS spectra through the standard Guinier fit procedure. Specifically, the linear fit was conducted in the $q$ range 0.02--0.06 \AA$^{-1}$ of the SAXS spectra in the $\ln I$ vs $q^2$ form, using scipy \cite{virtanen2020scipy}; $R_g^*$ is then computed as $\sqrt{-3m}$, where $m$ is the slope of the fitted line.

\subsection{Ensemble reweighting}
We then used ensemble reweighting to enforce the experimental spectrum for four chosen values of $q$.
Two values ($q=$0.03 and $q=$0.05 \AA$^{-1}$) were chosen inside the range used to compute $R_g^*$ through Guinier fit, while other two values ($q=$0.1 and $q=$0.2 \AA$^{-1}$) correspond to peak and shoulder of the SAXS spectrum in the Kratky form, respectively, which are more sensitive to variations in the structural compactness of the RNA solute.
The standard maximum-entropy reweighting procedure \cite{pitera2012use,cesari2018using} consists in determining a set of Lagrangian multipliers
$\lambda_i$ so that averages computed along the entire trajectory with weights proportional to $w_t=e^{-\sum_i \lambda_i s_i(t)+\frac{V_b(q(t))}{k_BT}}$
are equal to the reference values $s_i^{exp}$.  Here $s_i(t)$ is the value of the $i$-th observable in the $t$-th frame,
$V_b(q(t))$ is the metadynamics bias at the final time recomputed for the coordinates of the $t$-th frame, and $k_BT$ the thermal energy.
However, when used to directly enforce SAXS intensities, this procedure requires
an arbitrary scaling factor to be fixed \emph{a priori} or estimated during the analysis \cite{paissoni2020determination}.
To overcome this issue, we here enforce the weighted average of the quantity
\begin{equation}
s(q,t)=I(q,t)-\frac{I^{exp}(q)\sum_{q'} I(q')}{\sum_{q'} I^{exp}(q')}
\end{equation}
to be equal to zero,
where $I(q,t)$ is the SAXS intensity at wavevector $q$ for the $t$-th frame.
By straightforward manipulation it can be seen that $\langle s(q,t) \rangle=0$ implies
$\langle I(q,t) \rangle \propto I^{exp}(q)$.
Since by construction $\sum_i s(q,t)=0$, the resulting ensemble is invariant with respect to a uniform shift of the
four multipliers $\lambda_i$. It is then possible to arbitrarily set their sum to zero,
\emph{i.e.} $\sum_i\lambda_i=0$.
The reweighting was performed using a custom python script taking advantage of scipy minimization algorithms \cite{virtanen2020scipy}.
Two sets of experimental datapoints reported in Ref.~\cite{welty2018divalent} were used to reweight our samples:
one set was obtained using 100 KCl concentration and another using an additional 1mM MgKCl concentration. Both experiments also included 10mM MOPSO as a buffer.

Since weights can be uneven, only the structures with the highest weights effectively contribute to the ensemble.
An approximate estimate of the number of structures with a significant weight can be obtained by computing the
Kish effective sample size, defined as $K=\left(\sum_t w_t\right)^2/\left(\sum_t w_t^2\right)$ \cite{gray1969survey}.

\section{RESULTS}

\subsection{Spectra from MD simulations of crystal structure}
Short unbiased MD simulations, lasting 10 ns, were performed with varying ionic conditions (see Figure \ref{figure_3}a) on GAC RNA starting from the crystal structure, where GAC is in its folded state. Specifically, in the four considered setups, the concentration of Mg$^{2+}$ ions was gradually increased. Soft position restraints were placed on the RNA heavy atoms to exclude relevant solute dynamics while guaranteeing adequate sampling for the solvent. SAXS spectra were then computed from the MD sampled structure through different available software, namely PLUMED \cite{tribello2014plumed,paissoni2019martini}, Crysol \cite{svergun1995crysol}, WAXSiS \cite{knight2015waxsis} and Capriqorn \cite{kofinger2013atomic}. A substantial difference between these methods is how they include the contribution of solvent to the overall SAXS spectra (see Figure S4 for a schematic depiction). In particular, while PLUMED purely relies on the solute coordinates and CRYSOL models the solvent implicitly, both WAXSiS and Capriqorn include explicitly the solvent species in the computation of the spectra. Consistently, a comparison of the predicted SAXS spectra (Figure  \ref{figure_3}b) for a same simulation displays differences between the methods, which are more marked in the region at $q>$ 0.1 \AA$^{-1}$ where the contribution of the solvent becomes more significant. Most notably, WAXSiS and Capriqorn, both including explicitly the solvent contribution, provide compatible results. Additionally, no remarkable dependence on the different ionic conditions explored was observed when comparing results from the different setups (see Figure S5).

To investigate the role of RNA dynamics and potentially observe extended conformations, we performed long unbiased MD simulations of 1 $\mu$s with no restraints (setups 1 and 4 in Figure \ref{figure_3}a). Interestingly, no remarkable structural changes were observed on this time scale (see Figure \ref{figure_3}c), although the simulation with a pure K$^+$ buffer had a slightly larger displacement from the crystal structure and a slightly larger
gyration radius than the simulation including Mg$^{2+}$  (see Figure S6).
Average SAXS spectra from the 1 $\mu$s simulations were then computed using the pure solute (Figure \ref{figure_3}d) or including the solvent contribution (Figure \ref{figure_3}e). It is worth noticing that, when using a method that explicitly takes into account the solvent contribution (Figure \ref{figure_3}e), the frame-by-frame spectra exhibit significant variations from the spectrum obtained by averaging over the whole trajectory. On the contrary, when a pure-solute method is employed (Figure \ref{figure_3}d), the frame-by-frame spectra are closer to their average. Consistently, the fluctuations in the corresponding $R_g^*$, as computed through the Guinier fit procedure (see Materials and Methods) in the low-$q$ values of the SAXS spectra, are wider when the solvent is included explicitly. Standard deviation of $R_g^*$ was 0.1 \AA{} and 1.3 \AA{} for the pure solute and explicit solvent calculations, respectively. Most notably, for the same simulation, a greater value of $R_g^*$ as computed from the full simulation is observed when including the solvent, namely 16.8 \AA{} from SAXS with solvent vs 16.2 \AA{} from pure-solute SAXS, with statistical errors $<0.1$ \AA{}.

\begin{figure}
\begin{center}
\includegraphics[width=0.8\columnwidth]{figure_3.png}
\end{center}
	\caption{Simulated buffers and SAXS spectra from unbiased MD. a) Schematics representing the four tested setups.
                 All setups were used in short (10 ns) simulations with restrained solute. Setups 1 and 4 were also used in longer (1$\mu$s)
                 unrestrained simulations. Setup 1 was used in enhanced sampling simulations.
  b) Comparison of SAXS spectra computed using different available methods on a 10 ns simulation (K$^+$ only, no Mg$^{2+}$)
	with soft restraints on RNA
	heavy atom positions. The spectra are displayed in the Kratky form as $Iq^2$ vs $q$ and normalized by their peak ($q=0.1$ \AA$^{-1}$) values.
	c) Probability density function for the geometric gyration radius $R_g$ and
	RMSD from native for a 1 $\mu$s long unbiased MD simulation, with
	explicit solvent, started from the crystal structure (PDB ID: 1HC8). d) SAXS spectrum for the 1 $\mu$s unbiased MD run computed from the pure solute.
	The frame-by-frame spectra are shown as a light orange shade and display a root-mean-square deviation from the resulting average, in orange, of 0.01 in the displayed units. The colored dashed line indicates
	the Guinier fit performed at low-$q$ values of the average spectrum to compute the effective gyration radius$R_g^*$. In the top right corner of the plot, the small sketch indicates
	that the spectra are computed from the pure solute (RNA, in gray) only. e) SAXS spectrum from the 1 $\mu$s unbiased MD run computed including the explicit solvent.
	The frame-by-frame spectra are shown as light sky-blue shade and display a root-mean-square deviation from the resulting average, in sky-blue, of 0.03 in the displayed units. In the top right corner, the small
	sketch indicates that the spectra are computed from the whole system (RNA, in gray, water, in sky-blue, and ions, in red).
Spectra in panels d) and e) are normalized by the peak of the average spectrum.
	}
\label{figure_3}
\end{figure}

\subsection{Spectra from enhanced-sampling MD simulations}

In order to reproduce SAXS experimental spectra associated with diverse ionic conditions, enhanced sampling methods \cite{mlynsky2018exploring} were used to generate conformations with a broad range of compactness. Representative sampled structures are given in the supplementary material. Figure \ref{figure_4} reports the free-energy surfaces (FES) reconstructed from enhanced sampling simulations started from the crystal conformation. The system was biased with metadynamics through one variable that favored disruption of tertiary contacts and another that guided the sampling towards more extended structures and relaxation was enhanced by a replica exchange protocol (see Materials and Methods). We stress here that no information to guide towards the experiment was used in the sampling procedure and that only the no-Mg$^{2+}$ setup (setup 1 in Figure \ref{figure_3}a) was simulated. The resulting FES displayed a unique global minimum in correspondence of the crystal conformation (star label in the leftmost panel of Figure \ref{figure_4}). Correspondingly, more extended conformations were located in higher free-energy regions, up to about 25 kcal mol$^{-1}$ from the minimum. SAXS spectra computed \emph{a posteriori} using sets of frames with different compactness are also reported, both from the pure solute and the explicit solvent calculation (Figure \ref{figure_4}, panels with SAXS spectra).
For FES regions where GAC structure was significantly expanded compared to the initial state of the simulation, the shape of the SAXS spectra in the Kratky form changed remarkably. In all cases, a significant difference can be observed between the SAXS spectra computed from the pure solute and including the solvent explicitly (Figure \ref{figure_4}, orange and sky-blue lines in the SAXS spectra).

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{figure_4.png}
\end{center}
\caption{Free-energy surface (FES) and SAXS spectra from the enhanced-sampling MD simulation. The FES is shown as a function of the biased collective variables, namely the ratio
	between peak ($q=0.1$ \AA$^{-1}$) and shoulder ($q=0.2$ \AA$^{-1}$) of the pure-solute SAXS spectrum in the Kratky form, and a variable (Diff) estimating the degree of formation of
	tertiary contacts in the RNA molecule (see the methods section for its definition). The star indicates the crystal structure, from
	which the simulation was initiated. For representative regions of the FES (dashed rectangles), the corresponding SAXS spectra are shown (panels indicated by the dashed arrows).
	Specifically, both the average spectrum computed from the pure solute (RNA only) and from the whole system (RNA and solvent) are displayed in orange and
	sky-blue, respectively. The corresponding frame-by-frame spectra are also reported as light orange and light sky-blue shades. From left to right panels, their root-mean-square deviations from the corresponding averages are 0.01, 0.07, 0.14 for the pure solute and 0.03, 0.06, 0.12 for the whole system, in the displayed units. For each region, a representative structure of GAC is shown, color
	coded consistently with Figure 2.
	}
\label{figure_4}
\end{figure*}

\subsection{Enforcing experimental spectra}
We conducted a reweighting procedure to possibly identify GAC structural ensembles underlying reference experimental SAXS spectra among those collected in the enhanced sampling simulation. As a first step, the SAXS spectra were computed for each of the obtained structures. Importantly, the spectra were computed for both the pure solute and for the whole system, \emph{i.e.}, with the explicit inclusion of the solvent (Figure \ref{figure_4}, orange and sky-blue lines). Subsequently, a reweighting procedure was conducted using the maximum entropy principle \cite{pitera2012use,cesari2018using}. In particular, the ensemble generated in the K$^+$-only simulation was reweighted so as to match experimental SAXS spectra obtained in presence of Mg$^{2+}$ or only K$^+$  (Figure S7). To this end, the agreement with the experiment was enforced in four $q$ points of the spectrum that included the Guinier fit region and the peak and shoulder points of the Kratky plot. In this way, both the $R_g^*$ and the shape of the Kratky plot were taken into account. Interestingly, while employing as prior observables the spectra computed with the contribution from the solvent allowed to identify posterior ensembles consistent with the experimental ones (Figure S8), this was not the case when performing the procedure using the pure-solute spectra (Figure S9). This can be explained by the different distributions of the $R_g^*$ in the prior ensemble (Figure \ref{figure_5}, left panels). While $R_g^*$ values were confined in the range 15-24 \AA{} in the pure-solute case, the range explored was broader when the spectra included the solvent contribution.
In other words, despite they were applied to the same pool of structures, the two different ways of computing the spectra (pure-solute vs solute + solvent) produced different effects in terms of the resulting $R_g^*$. In particular, $R_g^*$ was greater than the corresponding $R_g$ computed from the only solute coordinates, with the effect being more marked on compact structures and becoming milder for more extended ones (Figure S10).

The refined distribution obtained when enforcing the Mg$^{2+}$ experimental data revealed a prevalence of compact structure complemented by a small population (about 1\%) of extended structures.
The population of extended structures was significantly larger (about 42\%) when the refinement was done using K$^+$ experimental data
(Figure \ref{figure_5}, upper and lower left panels, respectively).
It is important to notice that this reweighting stage resulted in very low Kish sizes \cite{gray1969survey,rangan2018determination} (15.9 and 2.9 for Mg$^{2+}$ and K$^+$, respectively, to be compared with 36000 structures used in the analysis), indicating that only a very limited number of structures contributed to the final spectrum. This is reflected in a high statistical error for the final spectrum, as it can be appreciated in Fig.~\ref{figure_5},
especially when using K$^+$ experimental data. This effect can be ascribed to a limited sampling of more extended GAC conformations in the enhanced sampling simulations.
The corresponding reweighted SAXS spectra were compatible with the experimental reference for Mg$^{2+}$ and K$^+$ (Figure \ref{figure_5}, upper and lower right panels, respectively). As expected based on the low Kish size, the agreement was poorer for the case of K$^+$. Nevertheless, the predicted $R_g^*$ values were in agreement with the experimental ones (compare green and gray values in the right panels of Figure \ref{figure_5}).

We also tested if adding a rigid shift to the experimental data could increase the Kish size or result in a Guinier radius in better agreement with experiment \cite{schneidman2013accurate}. The analysis suggests that no shift is necessary (see Figure S11).

\begin{figure}
\begin{center}
\includegraphics[width=\columnwidth]{figure_5.png}
\end{center}
	\caption{Probability distribution of $R_g^*$ and SAXS spectra from the
        ensembles reweighted to match Mg$^{2+}$ experimental data (top panels) and K$^+$ experimental data (bottom panels).
        In the left panels, $R_g^*$
	is computed through a Guinier fit on the SAXS spectra of the original (prior) ensemble (dashed lines) and of the reweighted (posterior) ensemble
	(solid lines). Results for both the pure solute (orange lines) and the whole system (green lines) are shown.
	Note that both solid lines are based on the same reweighted ensemble, \emph{i.e.} the one matching experiments and SAXS spectra computed including the solvent. In the
	right panels, the SAXS spectra from the reweighted ensembles are shown in the Kratky form and also reported are the corresponding $R_g^*$ and the associated statistical error
	(in green). The statistical errors for selected $q$ values of the spectra are displayed as error bars. The reference experimental value for $R_g^*$, which was
	computed from the experimental SAXS spectra (in light gray and violet for Mg$^{2+}$ and K$^+$, respectively), is also reported in gray for comparison.
	}
\label{figure_5}
\end{figure}

\section{DISCUSSION}

In this work we use atomistic molecular dynamics simulations, coupled with enhanced sampling methods, to generate a conformational ensemble for GAC RNA. The resulting ensemble is then reweighted so as to enforce agreement with recently published SAXS data \cite{welty2018divalent}. SAXS spectra are back-calculated comparing approaches that include solvent effects to a different extent. Preprocessed data and a notebook that can be used to generate all the figures can be downloaded at \url{https://github.com/bussilab/saxs-md-gac}. Full trajectories are available at \url{https://doi.org/10.5281/zenodo.4646262}.

The introduced approach combines two crucial ingredients, namely conformational dynamics and solvent effects. The former is enhanced by using replica exchange and metadynamics acting on a proxy of the SAXS intensities, so as to encourage the exploration of extended structures. The latter is achieved by using an accurate \textit{a posteriori} calculation of the spectra, where the presence of the solvent is explicitly modeled \cite{kofinger2013atomic}, in conjunction with the maximum entropy principle. The approach is summarized in Fig.~\ref{figure_1}. We note here that all the steps involved in the procedure are conducted through freely available software.
When enforcing experimental data obtained in presence of Mg$^{2+}$, our procedure suggests a low population of extended structures to contribute to the spectrum. Albeit low, this contribution is necessary to explain the difference observed between the experimental SAXS data and the spectra back-calculated using the explicit solvent approach on the native structure. When enforcing experimental data obtained in absence of Mg$^{2+}$, instead, the fraction of extended structures is significantly higher.

We first addressed the role of solvent contribution to the SAXS intensities. We thus ran long MD simulations starting from the crystal structure and analyzed them using a range of methods that neglect solvent contribution or include it at different levels of approximation and compare the back calculated SAXS spectra with the available experimental data. From our results it emerges that (a) an explicit inclusion of the solvent has a measurable effect on the apparent radius of gyration of the molecule as obtained with the Guinier fit and (b) the overall shape of the spectrum is affected also at relatively small scattering vectors ($q<0.3$\AA$^{-1}$). Although none of the employed methods results in a SAXS spectrum in agreement with the available experimental data, irrespective of the ionic condition in which the experiments were performed and the simulations were run, the explicit-solvent methods lead to values of the observed gyration radius that were closer to the experimental ones. This suggests that the role of solvent contribution in the estimation of SAXS spectra for nucleic acids should not be neglected. We hypothesize this to be related to the high charges that make the solvation shell structured and of critical importance.

We then addressed the role of RNA dynamics. Unbiased MD simulations in the $\mu$s timescale stably maintained the compact, crystal conformation of GAC and no alteration of the structure towards more extended states was observed, irrespective of the simulated ion conditions. This suggested that the disruption of the initial conformation would require remarkably extensive sampling. Indeed, the crystal structure comprises a complex network of tertiary contacts between the four separate stem regions of GAC, which overall results in a significant structural stability.
We thus employed a combination of two enhanced sampling methods, namely metadynamics and replica exchange. The former was used to improve the sampling of extended conformation by biasing dedicated CVs. Specifically, a first CV was designed to quantify the amount of tertiary contacts in the structure, so as to facilitate its rupture and to explore different sets of contacts, while a second CV was designed to reflect salient features of the SAXS spectra (peak and shoulder of the Kratky form), so as to encourage the exploration of structures with heterogeneous spectra and thus of varying structural compactness. At the same time, the replica exchange method was exploited to soften energetic barriers, thus accelerating local reconformations.

It is important to underline that no experimental data were used while performing MD simulations, and that SAXS intensities used for the metadynamics simulations were estimated using a pure-solute coarse-grain approach, that can be effectively used on-the-fly. This was possible here since the estimated intensities were only used to enhance sampling, and not to reproduce the experimental data. We notice that also in Ref.~\cite{kimanius2015saxs} SAXS intensities calculated in a pure-solute approximation were used to enhance sampling. Interestingly, also in the extended structures we found discrepancies between the pure-solute calculation of the SAXS intensities and their explicit-solvent counterparts. The effect of the solvent is not trivial and cannot be reduced to an increased effective gyration radius. In fact, the estimated Guinier radius for extended structure appeared not to be dependent on the inclusion of the solvent.

Although enhanced sampling simulations were capable to generate extended structures with SAXS spectra compatible with experiment, the  weight of these structures in the Boltzmann ensemble was very low. This happened even if our simulations were performed in absence of Mg$^{2+}$, and thus in conditions that are expected to favor a significant population of extended structures. This can be explained to be a consequence of (a) the choice of initializing our simulations from a compact structure and (b) a possible bias in the employed force field towards compact structures. Only by using a reweighting procedure based on the maximum entropy principle we were able to reproduce the experimental spectra.
The standard maximum-entropy-based reweighting was here extended so as to allow fitting experimental data that are known up to an a priori undetermined pre-factor.
Notably, the employed maximum-entropy reweighting only succeeded when spectra were calculated including the solvent, confirming that both solvent contribution and dynamics are crucial in this system. We observe that it is possible to extend the maximum entropy principle so as to model experimental errors by including a regularization term \cite{cesari2018using}. Error models are also explicitly included in the metainference method \cite{bonomi2016metainference}. For simplicity, we didn't include any regularization or error model here, since the statistical error on the intensities due to the limited sampling dominates on the experimental error. When attempting a fit based on the pure-solute spectra, instead, adding a regularization term was required for the minimization to converge but then lead to spectra in remarkable disagreement with experiment.

The decision to avoid an explicit usage of the available SAXS data during the simulation was here made so as to generate a generic ensemble that spans a wide range of conformations and can be then, \emph{a posteriori}, reweighted in order to match experimental data obtained with different ionic conditions. In this specific case, it allowed for obtaining reference ensembles for two different datasets from the same simulation, thus at half of the computational cost. Using the same simulation for fitting two separate experiments has also the advantage that statistical errors and biases towards the initial conditions will be identical in the two cases, thus making the comparison of the resulting ensembles more robust. In general, this can be of critical importance when intensive MD simulations are involved. In fact, in case of a variation in the reference experimental data, such as the availability of additional data, the procedure can be easily renewed using the same prior ensemble, with no necessity of repeating the MD simulations \cite{orioli2020learn}.
The usage of approximate back-calculated SAXS intensities in the enhanced sampling stage, however, helped us not to incur in the difficult situation where the initial ensemble is so poor that it cannot match experimental data when reweighted \cite{cesari2018using,rangan2018determination}.
Our comparison of the back-calculated SAXS intensities obtained from the simulation of crystal structures
using different ion concentrations suggests that, for a fixed RNA structure,
the effect of the ions present in the solution on the spectrum is limited.
This provides a rationale for our choice to reweight our KCl simulation using both KCl and MgCl data and, indirectly,
justifies the neglection of the 10mM MOPSO present in the original experiment.
We also notice that the reweighted ensembles are relatively poor and show a very limited Kish size. Longer simulations might allow for better converged ensembles. However, the reported calculation is at the limit of what is currently feasible in terms of system size and statistical sampling. In particular, the investigated system is significantly larger (57 nucleotides) than other RNA systems where
maximum-entropy or related methods have been used so far to integrate solution experiments with atomistic simulations \cite{borkar2016structure,borkar2017simultaneous,kooshapur2018structural,paissoni2019martini,bottaro2020integrating,reisser2020conformational} (29 nucleotides at most) and contains more heterogeneous structural motifs.

To the best of our knowledge, this is the first report on a procedure to reconstruct the conformational ensemble of a functional RNA molecule that combines experimental SAXS spectra and MD simulations while fully accounting for the solvent contribution, which is included explicitly in the computation, and RNA conformational dynamics. Previous works have mostly addressed separately the two issues. For instance, a common practice used when computing SAXS spectra with explicit solvent methods is to apply soft position restraints to the backbone atoms of the solute biomolecule \cite{kofinger2013atomic,knight2015waxsis}, although other studies were conducted where unrestrained solutes were examined \cite{hermann2019saxs,chen2015structural}.
If used to analyze relatively short plain MD simulations where, by necessity, the solute dynamics is limited, these methods would mostly report the solvent fluctuations.
An opposite scenario is offered by the pure solute restraints implemented in PLUMED \cite{paissoni2019martini}, that
can be naturally combined with efficient enhanced sampling methods so as to properly characterize solute dynamics
\cite{bonomi2016metadynamic}. In this case, however, solvent contributions are neglected by construction.
In this respect, our protocol takes the best from both worlds to reconstruct conformational ensembles that are compatible with reference experimental SAXS data, by (a) enforcing approximate solute-only spectra during the simulation and (b) reweighting the resulting ensemble using the more accurate explicit solvent models.

\section{ACKNOWLEDGEMENTS}

Sandro Bottaro and Carlo Camilloni are acknowledged for reading the manuscript
and providing useful suggestions.
\bibliographystyle{ieeetr}

redpajama_set_name: RedPajamaArXiv


Majolica Majorca's eyeshadows are just so pigmented that when you swatch them a bit strongly than usual they're already as buttery as Western eyeshadows! Today's dupe is between Urban Decay's baked, a coppery gold color from the Book of Shadows vol. IV redux, and the coppery one on the Majolica Majorca Jeweling Eyes eyeshadow quad.
In the previous photo, which was taken with flash, you could kind of see this really tiny difference between the two, but if you look at the eyeshadows in the pan and if you look at it with normal lighting (as represented by the photo below without flash), you could barely see the difference.
I love these two actually, because I love coppery shades more than golden ones. I also love the color payoff of both eyeshadows despite having differences in formulation. Baked is a strong shimmery metallic shade, while the one in the MM quad is a metallic glittery shade. The only reason why it doesn't seem so apparent is because Japanese microglitters are really small, that they're too large to be considered shimmer but too small if you're standard is the Western definition of microglitters.
Tune in again for next week's #MDM! What else would you like to see in this section? Please do tell me in the comments box!