Protective Policy Index (PPI) global dataset of origins and stringency of COVID 19 mitigation policies

This the final version. Available on open access from Nature Research via the DOI in this recordData Records: We have created a Github repository (https://github.com/COVID-policy-response-lab/PPI-data) to store the datasets with the Public Health Protective Policy Index and its components. A copy of the included datafiles, as described below, was deposited with openICPSR15. It presently requires creating an account with the depository. Data access is free. Data location is at https://www.openicpsr.org/openicpsr/project/123401. The datasets are stored as csv files with five types of layouts. “PPI_country_m1.csv” is a file with country-level aggregates of region-level PPIs, computed using method 1, and their components. Each row corresponds to a country-date. The rows are identified using the country name (cname), numeric and 2-letter ISO 3166-1 codes (isocode and isoabbr respectively), as well as a date variable. The names of the policy variables contain four components: the name of the broader category, the name of the category, the level of issuing government (“nat” refers to the national policies, “reg” refers to the subnational policies, and “tot” refers to the combination of national and subnational policies), as well as suffix “ave”. For example, the average Total PPI is denoted as “ppi.all.tot.ave”, and the average stringency of the closures of air borders by the national government is denoted as “borders.air_bord.nat.ave”. See the codebook for the complete list of variables. “PPI_country_m2.csv” is a file with country-level aggregates of region-level PPIs, computed using method 2, and their components. The identifying variables and the naming convention for the policy variables is the same as in “PPI_country_m1.csv”, with the addition of suffix “0.2” at the end of the policy variable names. “PPI_regions_XX_m1.csv” (replace XX with the 2-letter ISO 3166-1 country codes) are country-specific files with region-specific PPIs, computed using method 1, and their components. The identifying variables include the numeric and 2-letter ISO 3166-1 codes of the country (isocode and isoabbr respectively), the name of the region (state_province), its ISO 3166-2 code (iso_state), as well as a date variable. The names of the policy variables contain three components: the name of the broader category, the name of the category, and the level of issuing government (“nat” refers to the national policies, “reg” refers to the subnational policies, and “tot” refers to the combination of national and subnational policies). For example, the average Total PPI is denoted as “ppi.all.tot”, and the stringency of the closures of air borders by the national government is denoted as “borders.air_bord.nat”. “PPI_regions_XX_m2.csv” (replace XX with the 2-letter ISO 3166-1 country codes are country-specific files with region-specific PPIs, computed using method 2, and their components. The identifying variables and the naming convention for the policy variables is the same as in “PPI_regions_XX_m1.csv”, with the addition of the suffix “0.2” at the end of the policy variable names. “changes_regions_m1.csv” is an auxiliary file that describes the changes in the policy states, as recorded in the “PPI_regions_XX_m1.csv” files. Each row in this file corresponds to a change in a value of a policy state variable in a region and of a specific government level. The case identifying variables include the name of the country (cname), the numeric and 2-letter ISO 3166-1 code of the country (isocode and isoabbr, respectively), the name of the region (state_province) and its ISO 3166-2 code, date, policy dimension, and a marker of policies issued by a regional government (subnational). Among others, the attributes included in this file include the branch of the government (branch) and the date when the change was announced (report_date).Code availability; The code used to produce our calculations is available at https://github.com/COVID-policy-response-lab/PPI-dataWe have developed and made accessible for multidisciplinary audience a unique global dataset of the behavior of political actors during the COVID-19 pandemic as measured by their policy-making efforts to protect their publics. The dataset presents consistently coded cross-national data at subnational and national levels on the daily level of stringency of public health policies by level of government overall and within specific policy categories, and reports branches of government that adopted these policies. The data on these public mandates of protective behaviors is collected from media announcements and government publications. The dataset allows comparisons of governments’ policy efforts and timing across the world and can serve as a source of information on policy determinants of pandemic outcomes–both societal and possibly medical

Optimization of xylanase production by filamentous fungi in solid state fermentation and scale-up to horizontal tube bioreactor

Five microorganisms, namely Aspergillus niger CECT 2700, A. niger CECT 2915, A. niger CECT 2088, Aspergillus terreus CECT 2808, and Rhizopus stolonifer CECT 2344, were grown on corncob to produce cell wall polysaccharide-degrading enzymes, mainly xylanases, by solid-state fermentation (SSF). A. niger CECT 2700 produced the highest amount of xylanases of 504±7 U/g dry corncob (dcc) after 3 days of fermentation. The optimization of the culture broth (5.0 g/L NaNO3, 1.3 g/L (NH4)2SO4, 4.5 g/L KH2PO4, and 3 g/L yeast extract) and operational conditions (5 g of bed loading, using an initial substrate to moistening medium of 1:3.6 (w/v)) allowed increasing the predicted maximal xylanase activity up to 2,452.7 U/g dcc. However, different pretreatments of materials, including destarching, autoclaving, microwave, and alkaline treatments, were detrimental. Finally, the process was successfully established in a laboratory-scale horizontal tube biore- actor, achieving the highest xylanase activity (2,926 U/g dcc) at a flow rate of 0.2 L/min. The result showed an overall 5.8-fold increase in xylanase activity after optimization of culture media, operational conditions, and scale-up.We are grateful to the Spanish Ministry of Science and Innovation for the financial support of this work (project CTQ2011-28967), which has partial financial support from the FEDER funds of the European Union; to the Leonardo da Vinci Programme for founding the stay of Felisbela Oliveira in Vigo University; to MAEC-AECID (Spanish Government) for the financial support for Perez-Bibbins, B. and to Spanish Ministry of Education, Culture and Sports for Perez-Rodriguez's FPU; and to Solla E. and Mendez J. (CACTI-University of Vigo) for their excellent technical assistance in microscopy