Correlations, means, standard deviations, minimums and maximums of the measured variables (N = 253).

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow...

Kenneth R. Mount is an author. They have 2 books in our database.

Summary of true attachment kernels used in the Monte Carlo simulation. There is a total of 16 different kernels.Summary of true attachment kernels used in the Monte Carlo simulation.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Replication pack, FSE2018 submission #164:
------------------------------------------

**Working title:** Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: 
A Case Study of the PyPI Ecosystem

**Note:** link to data artifacts is already included in the paper. 
Link to the code will be included in the Camera Ready version as well.


Content description
===================

- **ghd-0.1.0.zip** - the code archive. This code produces the dataset files 
 described below
- **settings.py** - settings template for the code archive.
- **dataset_minimal_Jan_2018.zip** - the minimally sufficient version of the dataset.
 This dataset only includes stats aggregated by the ecosystem (PyPI)
- **dataset_full_Jan_2018.tgz** - full version of the dataset, including project-level
 statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages
 themselves, which take around 2TB.
- **build_model.r, helpers.r** - R files to process the survival data 
  (`survival_data.csv` in **dataset_minimal_Jan_2018.zip**, 
  `common.cache/survival_data.pypi_2008_2017-12_6.csv` in 
  **dataset_full_Jan_2018.tgz**)
- **Interview protocol.pdf** - approximate protocol used for semistructured interviews.
- LICENSE - text of GPL v3, under which this dataset is published
- INSTALL.md - replication guide (~2 pages)

Replication guide
=================

Step 0 - prerequisites
----------------------

- Unix-compatible OS (Linux or OS X)
- Python interpreter (2.7 was used; Python 3 compatibility is highly likely)
- R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible)

Depending on detalization level (see Step 2 for more details):
- up to 2Tb of disk space (see Step 2 detalization levels)
- at least 16Gb of RAM (64 preferable)
- few hours to few month of processing time

Step 1 - software
----------------

- unpack **ghd-0.1.0.zip**, or clone from gitlab:

   git clone https://gitlab.com/user2589/ghd.git
   git checkout 0.1.0
 
 `cd` into the extracted folder. 
 All commands below assume it as a current directory.
  
- copy `settings.py` into the extracted folder. Edit the file:
  * set `DATASET_PATH` to some newly created folder path
  * add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` 
- install docker. For Ubuntu Linux, the command is 
  `sudo apt-get install docker-compose`
- install libarchive and headers: `sudo apt-get install libarchive-dev`
- (optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools`
 Without this dependency, you might get an error on the next step, 
 but it's safe to ignore.
- install Python libraries: `pip install --user -r requirements.txt` . 
- disable all APIs except GitHub (Bitbucket and Gitlab support were
 not yet implemented when this study was in progress): edit
 `scraper/init.py`, comment out everything except GitHub support
 in `PROVIDERS`.

Step 2 - obtaining the dataset
-----------------------------

The ultimate goal of this step is to get output of the Python function 
`common.utils.survival_data()` and save it into a CSV file:

  # copy and paste into a Python console
  from common import utils
  survival_data = utils.survival_data('pypi', '2008', smoothing=6)
  survival_data.to_csv('survival_data.csv')

Since full replication will take several months, here are some ways to speedup
the process:

####Option 2.a, difficulty level: easiest

Just use the precomputed data. Step 1 is not necessary under this scenario.

- extract **dataset_minimal_Jan_2018.zip**
- get `survival_data.csv`, go to the next step

####Option 2.b, difficulty level: easy

Use precomputed longitudinal feature values to build the final table.
The whole process will take 15..30 minutes.

- create a folder `

Dataset and R script that accompanies the paper "Low potential for evolutionary rescue from climate change in a tropical fish"
fulldataset.csv : contains raw data for all fish across all generations F0-F6 of the selection experiment
reaction_norm.csv: contains raw data from experiment in F6 generation when all lines were acclimated to three acclimation temperatures (24, 28 and 32 degrees celsius)
The other attached datafiles contain summary datasets (e.g. means of the selected lines at each generation) and are references in the R script attached.

Towns in Time is a compilation of time series data for Victoria's towns\r covering the years 1981 to 2011. The data is based on Census data collected by\r the Australian Bureau of Statistics. Towns in Time presents 2011 data for the\r 2011 definition of each town, together with data under the 2006 definition for\r 2006 and earlier years. A map showing the difference in the town's boundaries\r between 2006 and 2011 is attached to each data sheet. It is recommended the\r user assess this concordance when using time series data.\r \r

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the phase space...

AE1918 was a cruise of opportunity on which two oceanographic sampling activities were conducted: a CTD cast and a MOCNESS net tow. These are the net data from the MOCNESS tow. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Standard MOCNESS procedure. It was observed filtered volumes to be too high (and speeds too high), so flowmeter was "recalibrated"\u00a0using deployments and files reanalyzed. It is likely that, since the flowmeter was too new, after several recent deployments has finally "broken-in" and now goes faster. Flow calibration was done by running the LVpki software for several profiles looking at average CF values.

Refer to the cruise report for more information. See also: the xmlcon and hdr files under Supplemental Files. awards_0_award_nid=764113 awards_0_award_number=OCE-1829318 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1829318 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 awards_1_award_nid=764119 awards_1_award_number=OCE-1829378 awards_1_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1829378 awards_1_funder_name=NSF Division of Ocean Sciences awards_1_funding_acronym=NSF OCE awards_1_funding_source_nid=355 awards_1_program_manager=David L. Garrison awards_1_program_manager_nid=50534 cdm_data_type=Other comment=MOCNESS Net Data from R/V Atlantic Explorer cruise AE1918 PI: Amy Maas (BIOS) Co-PIs: Leocadio Blanco-Bercial (BIOS) & Ann Tarrant (WHOI) Version date: 13-Nov-2019 Noted after the cruise: original volumes (VOL) were wrong.
New column (NewVol) was added with real values after changing the calibration to 2.7.
The incorrect VOL column has been removed. Please notice that, contrary to regular mocness net numbering (0 to 8) the net count starts at 1 (1 to 9). Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.781508.1 infoUrl=https://www.bco-dmo.org/dataset/781508 institution=BCO-DMO instruments_0_acronym=MOC1 instruments_0_dataset_instrument_description=Seabird 9/11 unit attached to a 1 m MOCNESS, 150 micron mesh nets instruments_0_dataset_instrument_nid=781517 instruments_0_description=The Multiple Opening/Closing Net and Environmental Sensing System or MOCNESS is a family of net systems based on the Tucker Trawl principle. The MOCNESS-1 carries nine 1-m2 nets usually of 335 micrometer mesh and is intended for use with the macrozooplankton. All nets are black to reduce contrast with the background. A motor/toggle release assembly is mounted on the top portion of the frame and stainless steel cables with swaged fittings are used to attach the net bar to the toggle release. A stepping motor in a pressure compensated case filled with oil turns the escapement crankshaft of the toggle release which sequentially releases the nets to an open then closed position on command from the surface. -- from the MOCNESS Operations Manual (1999 + 2003). instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L22/current/NETT0097/ instruments_0_instrument_name=MOCNESS1 instruments_0_instrument_nid=437 instruments_0_supplied_name=MOCNESS keywords_vocabulary=GCMD Science Keywords metadata_source=https://www.bco-dmo.org/api/dataset/781508 param_mapping={'781508': {'ISO_DateTime_UTC_start': 'flag - time'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/781508/parameters people_0_affiliation=Bermuda Institute of Ocean Sciences people_0_affiliation_acronym=BIOS people_0_person_name=Amy Maas people_0_person_nid=51589 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Bermuda Institute of Ocean Sciences people_1_affiliation_acronym=BIOS people_1_person_name=Leocadio Blanco-Bercial people_1_person_nid=51108 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI people_2_person_name=Ann M. Tarrant people_2_person_nid=51430 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Zooplankton Diel Rhythm projects_0_acronym=Zooplankton Diel Rhythm projects_0_description=NSF Award Abstract: The daily vertical migration (DMV) of zooplankton and fish across hundreds of meters between shallow and deep waters is a predominant pattern in pelagic ecosystems. This migration has consequences for biogeochemical cycling as it moves a substantial portion of fixed carbon and nitrogen (an estimated 15 to 40 % of the total global organic export) from the surface directly to depth where it feeds the midwater food chain and sequesters nutrients away from atmospheric mixing. Estimates and predictions of these fluxes are, however, poorly understood at present. New observations have shown that one source of uncertainty is due to the assumption that metabolic rates and processes do not vary over the course of the day, except based on changes in temperature and oxygen availability. Rates are, however, also driven by differences in feeding, swimming behavior, and underlying circadian cycles. The objective of this project is to improve the ability of scientists to understand and predict zooplankton contributions to the movement of carbon and nitrogen in the ocean by detailing daily changes in physiological processes of these organisms. By producing a set of respiration and excretion measurements over a daily time series, paired with simultaneously collected gene and protein expression patterns for an abundant vertically migratory species, the investigators will provide unprecedented and predictive insight into how changes in the environment affect the contribution of zooplankton to biogeochemical fluxes. The sampling design of the project will advance discovery and understanding by providing hands-on training opportunities to at least two undergraduate researchers. The project will broaden dissemination of the research via development of an educational module, focusing on rhythms in the ocean. The module will initially be piloted with the Bermuda Institute of Ocean Sciences (BIOS) summer camp students and then disseminated through the BIOS Explorer program, the Teacher Resources Page on the BIOS website, and published in a peer-reviewed educational journal. This project will characterize the metabolic consequences of daily physiological rhythms and DVM for a model zooplankton species, the abundant subtropical copepod Pleuromamma xiphias. Flux processes (oxygen consumption, carbon dioxide production, production of ammonium and fecal pellet production) will be interrogated using directed experiments testing the effects of temperature, feeding and circadian cycle. Circadian cycling will further be examined using transcriptomic and proteomic profiling. These experiments will be related to field samples taken at 6-h intervals over the course of the diel migration using an integrated suite of molecular and organismal metrics. Combined organismal, transcriptomic and proteomic profiles will provide an understanding of which metabolic pathways and associated flux products vary in relation to particular environmental variables (food, light cycle, temperature). Diel variation in metabolic rates will also be assessed across seasons and species using other important migratory groups (pteropod, euphausiid, and another copepod). The metabolic data will then be contextualized with abundance estimates from archived depth-stratified tows to allow scaling to community-level patterns and will be used to improve calculations of zooplankton contribution to particulate organic carbon, nitrogen and respiratory active flux. The results of this study will both improve our flux estimates and provide predictive insight into how various environmental variables influence the underlying physiological pathways generating carbon and nitrogen flux. Cruise reports are available from the completed cruises:SD031019AE1910AE1918 projects_0_end_date=2021-09 projects_0_geolocation=Bermuda projects_0_name=Collaborative Research: Diel physiological rhythms in a tropical oceanic copepod projects_0_project_nid=764114 projects_0_start_date=2018-10 sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=tow,date_start,time_start,time,cruise,Date time_coverage_end=2019-07-25T14:20:08Z time_coverage_start=2019-07-25T14:20:08Z version=1 xml_source=osprey2erddap.update_xml() v1.3

This data contains general government sector operating expenses, sourced from the Australian Bureau of Statistics historical data and the Department of Treasury and Finance, categorised by ‘government purpose classification’ (GPC) and ‘classification of the functions of government’ (COFOG).\r \r The Australian system of Government Finance Statistics (GFS) was revised by the Australian Bureau of Statistics, with the release of the Australian System of Government Finance Statistics: Concepts, Sources and Methods 2015 Cat. No. 5514.0.\r \r Implementation of the updated GFS manual has resulted in the COFOG framework replacing the former GPC framework, with effect from the 2018-19 financial year for financial reporting under AASB 1049.\r \r The underlying data from 1961-62 to 1997-98 represents a conversion from the original cash series to an accruals basis by estimating depreciation and superannuation expenses based on statistical modelling.\r \r Although the conversion provides a basis for comparison with total expenses in the current series of accrual GFS information from 1998 (in the attached table), the estimated accrued expense items have not been apportioned to individual purpose classifications.\r \r The absence of these splits between functional classifications in the attached table data therefore represents a break in the series and it is not possible to compare individual purpose categories with those in other tables.\r \r Similarly, the transition from GPC to COFOG represents an additional break in the series and comparability between the two frameworks will not be possible.\r \r The key reporting changes from GPC to COFOG are as follows:\r \r - the number of categories has reduced from 12 under GPC to 10 under COFOG; \r - the fuel and energy, agriculture, forestry, fishing and hunting categories have been abolished and are now part of the new economic affairs category. The majority of the outputs in other economic affairs are also included in this new category;\r - public debt transactions have moved from the other purposes category (i.e. primarily interest expense on borrowings) to general public services category;\r - a new environmental protection category was created to include functions such as waste management, water waste management, pollution and production of biodiversity and landscape, which were previously classified under housing and community amenities category, as well as national and state parks functions from the recreation and culture category; and\r - housing functions such as housing assistance and housing concessions are now part of the social protection category

The data collection was carried out in a nationally representative online sample by a Hungarian company (Social Research Ltd.). 16,000 members of the company’s research community were invited to participate voluntarily in the research via email. The sample was stratified according to gender, age, education, and settlement type. Following the stratification, the respondents were randomly selected in order to obtain a final, nationally representative sample. There were five age groups (18-29, 30-39, 40-49, and 50-59 years of age, and 60 years of age or older), and three categories of education level (primary and vocational school, secondary, and higher education). The stratification of the sample was completed by adjusting the true proportions of settlement type (capital, cities/towns, and villages) and regions (Central, Eastern, and Western Hungary).

The total sample size was N = 993, but the number of respondents to the ECR-R-HU questions was N=958, as the questionnaire was not offered to participants who stated that they had never been in a romantic relationship. After the first, main wave of data collection in December 2018, a second wave was carried out in order to assess the stability of the ECR-R-HU scale. After a 4-month interval in March 2019, the questionnaire was administered again to a smaller subsample (N = 98) of the original sample (Wave 2 data). In addition to socio-demographic questions and the ECR-R-HU, participants were given the Hungarian version of the following four questionnaires in order to test the convergent validity of the ECR-R-HU: 1. WHO Well-being Questionnaire (WBI-5), 2. Perceived Stress Scale – 4 (PSS-4), 3. Depression Scale Questionnaire (DS1K), 4. Family Assessment Device (FAD). ECR-R-HU item distributions were checked by Kolmogorov-Smirnov tests. To test the original two-factor model of the ECR-R, a series of Confirmatory Factor Analyses (CFAs) were conducted on AMOS 21.0. Maximum likelihood estimations were used and four model fit indices were examined: the Chi Square Test of Model Fit (χ2/df ratio, which indicates a good model fit below 3), the Steiger-Lind Root Mean Square Error of Approximation (RMSEA which indicates a good model fit below .05), the Tucker-Lewis Index (TLI which signifies a good fit above .95), and the Bentler Comparative Fit Index (CFI which indicates a good fit above .95). RMSEA between .05 and .10, CFI and TLI between .90 and .95 mean a moderate fit. Exploratory Factor Analyses (EFAs) were conducted using Principal Axis Factoring (PAF) method with varimax rotation to examine the orthogonal structure of the latent factors in our study. We also used Hierarchical Cluster Analysis (HCA) to confirm the latent structure of the items based on their similarities. Between-groups linkage method was used, and the items’ distances/similarities were examined with Pearson correlations. The internal consistency of the subscales was measured by calculating Cronbach’s alphas. Descriptive statistics showed that the distribution of the ECR-R-HU subscale scores did not follow a normal distribution, so we used non-parametric statistical tests (Spearman correlations, Mann-Whitney Test, Kruskal-Wallis Test) for further analyses. The research was approved by the Research Ethics Committee of Semmelweis University Budapest, Hungary. The license number: RKEB: 197/2018.

The Australian Charities and Not-for-profits Commission (ACNC) is the national regulator of charities in Australia. \r \r Since 3 December 2012, charities wanting to access Commonwealth charity tax concessions (and other benefits), need to register with the ACNC. Although many charities choose to register, registration with the ACNC is voluntary. Each financial year, registered charities are required to lodge an Annual Information Statement (AIS) with the ACNC. Generally, charities are required to submit their AIS within six months of the end of their reporting period – for example, by 31 December for a charity with a 30 June financial year end. This dataset provides a record of the 2015 AISs submitted by charities, that is, the statements submitted for a charity’s 2015 reporting year. For most charities that will be for the financial year 1 July 2014 – 30 June 2015, for others it will be the 2015 calendar year. There are also a small number of charities that have alternative particular reporting periods. \r \r Registered charities can apply to the ACNC to have some or all of the information they provide withheld from the ACNC Register. If a charity has applied to have their data withheld, the AIS data relating to that charity has been excluded from this dataset. There are only limited circumstances when the ACNC can agree to withhold information, including because the information: \r \r • is commercially sensitive and it could cause harm \r \r • is false, confusing or misleading \r \r • is offensive \r \r • could endanger public safety \r \r • falls within the circumstances allowed by the regulations (such as if the information identifies a private donor in relation to a private ancillary fund). \r The AIS information for individual charities can be viewed at www.acnc.gov.au/findacharity. \r \r The 2015 AIS dataset on data.gov.au can be used to find the AIS information lodged by more than one charity, for example for research purposes. It can also be used to filter and sort by different variables across all the AIS information that the ACNC has received for the 2015 reporting period. \r \r Some charities report to the ACNC as part of a reporting group. The 2015 AIS information for these charities is attached as a separate dataset on this page. Any analysis of AIS data should include those charities as well. \r \r The 2015 AIS collects information about charity finances. Financial information provides a basis for understanding the charity and its activities in greater detail. However, it is easy to misunderstand a charity's financial position or performance by judging it solely on its financial information. When comparing financial information to other charities, it is important to consider each charity's unique situation. This is especially the case for small charities, that do not provide financial reports which often contain more details about a charity's financial position and activities. For more information on interpreting financial information, please see the ACNC website. \r \r The ACNC maintains a network of researchers who are interested in the sector, and hold regular teleconferences. If you are interested in becoming involved in the Research network, please email us. The ACNC also publishes other datasets on data.gov.au, as part of our commitment to open data and transparent regulation. Please click here to view these. \r \r NOTE: It is possible that some information in this dataset might be subject to a future request by the charity to have their information withheld, but will still appear in the dataset until the next update. Please consider this risk when using this dataset. \r \r Please use the explanatory notes attached to help with analysis of this dataset.

We used R for all data analyses. We have attached R Markdown files, with code, adjoining the datasets used for analyses related to our objectives. R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Bacteria Counts CTD Bottle Measurements HRS2204

P-values were calculated by Fisher’s exact test based on the annotations of all significant partners for each protein. All these predictions are marked with “likely” in S2 Table. For more discussion on the prediction of GO and KEGG annotations, please refer to Appendix B of S1 File.Predicted GO and KEGG annotations that are worth further validation.

Background: The novel coronavirus 2019 (COVID-19) has caused severe panic among people worldwide. In Italy, a nationwide state of alert was declared on January 31st, leading to the confinement of the entire population from March 11 to May 18, 2020. Isolation and quarantine measures cause psychological problems, especially for individuals who are recognized as being vulnerable. Parental bonding and attachment styles play a role in the programming of the stress response system. Here, we hypothesize that the response to restricted social contact and mobility due to the pandemic has detrimental effects on mental-psychological health and that this relationship is, at least in part, modulated by parental bonding and attachment relationships that are experienced at an early age.Methods: A sample of 68 volunteer University students was screened for psychopathological symptoms (SCL-90-R and STAI-Y), stress perception (PSS), attachment style (RQ), and parental care and overcontrol (PBI) 6 months before the confinement. In the same subjects, psychopathological symptoms and stress perception were measured again during confinement.Results: Overall, psychological health and stress management deteriorated across the entire sample during confinement. Specifically, a significant increase in phobic anxiety, depression, psychological distress, and perceived stress was observed. Notably, parental bonding and attachment styles modulated the psychological status during the lockdown. Individuals with secure attachment and high levels of parental care (high care) showed increased levels of state anxiety and perceived stress in phase 2, compared with phase 1. In contrast, individuals with insecure attachment and low levels of parental care (low care) already showed a high rate of state anxiety and perceived stress in phase 1 that did not increase further during phase 2.Conclusion: The general deterioration of psychological health in the entire sample demonstrates the pervasiveness of this stressor, a decline that is partially modulated by attachment style and parental bonding. These results implicated disparate sensitivities to environmental changes in the high- and low care groups during the lockdown, the former of which shows the greatest flexibility in the response to environment, suggesting adequate and functional response to stress in high care individuals, which is not observable in the low care group.

Discoveries of RNA roles in cellular physiology and pathology are increasing the need for new tools that modulate the structure and function of these biomolecules, and small molecules are proving useful. In 2017, we curated the RNA-targeted BIoactive ligaNd Database (R-BIND) and discovered distinguishing physicochemical properties of RNA-targeting ligands, leading us to propose the existence of an “RNA-privileged” chemical space. Biennial updates of the database and the establishment of a website platform (rbind.chem.duke.edu) have provided new insights and tools to design small molecules based on the analyzed physicochemical and spatial properties. In this report and R-BIND 2.0 update, we refined the curation approach and ligand classification system as well as conducted analyses of RNA structure elements for the first time to identify new targeting strategies. Specifically, we curated and analyzed RNA target structural motifs to determine the properties of small molecules that may confer selectivity for distinct RNA secondary and tertiary structures. Additionally, we collected sequences of target structures and incorporated an RNA structure search algorithm into the website that outputs small molecules targeting similar motifs without a priori secondary structure knowledge. Cheminformatic analyses revealed that, despite the 50% increase in small molecule library size, the distinguishing properties of R-BIND ligands remained significantly different from that of proteins and are therefore still relevant to RNA-targeted probe discovery. Combined, we expect these novel insights and website features to enable the rational design of RNA-targeted ligands and to serve as a resource and inspiration for a variety of scientists interested in RNA targeting.

Pearson correlations (r) between study variables (N = 155).

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp