Unaccompanied Asylum-Seeking Children (UASCs) constitute one of the most vulnerable populations in the UK, often experiencing ongoing hardship. This study aimed to describe the UASCs population within the Children Looked After (CLA) dataset and to describe the association between socio-demographic characteristics and attendance at state-funded schools in England.
We used national, linked social care-hospital administrative data from the ECHILD database from 2005 to 2021. The UASC population was identified using the UASC flag which was already included in the social care dataset. We described the UASCs population and their linkage to National Pupil Dataset (NPD) by data, by gender, age group, ethnicity of placement type.
The CLA dataset included 38,8820 UASCs in the study period. The majority were male (86%,33730/38820), aged 16 and over at placement (66%,25530/38820). Only 1/10 were from a White ethnic background (10%,3870/38820). Two in five UASCs were placed in foster care (care of strangers, 41%, 16000/38820), and half in unregulated and independent accommodations (50%,10280/38820). Less than 22% of UASCs (8600/38820) in the social care dataset were linkable to NPD. Linkage rates to the NPD dataset were lower for UASCs who were aged 16 and over (6%,1480/25340), Black (17%,1890/10910), or placed in unregulated and independent accommodations (11%,1370/19270).
This paper is the first to evaluate linkage between national social care and education databases for UASCs in England. Less than a quarter of UASCs could be linked to education data. This reduces our capacity to link UASCs to other datasets included within ECHILD, such as Hospital Episode Statistics (HES).
Hardian Bimanto, Hari Basuki Notobroto, Soenarnatalina Melaniani
One of the error conditions that are required to be met is the absence of autocorrelation problems. Autocorrelation is a correlation of errors between observations, the existence of an error correlation between observations will cause deviations from the actual statistical value. One of the statistical methods for overcoming autocorrelation is the Prais Winsten method. This study aims to explain the Prais Winsten method in overcoming the problem of autocorrelation on factors that affect the life expectancy of East Java Province in 2018. This research is a secondary data analysis, data obtained from the East Java Province's health profile in 2018 with the dependent variable, namely life expectancy, and independent variables, namely prevalence of diarrhea, clean and healthy living behavior, and mean years of school. The results of this research indicated the finding of autocorrelation problems in the factors that affected the life expectancy of East Java Province in 2018. Improvements with the Prais Winsten method showed that the Durbin Watson value was at the critical point limit, Mean Square Error and coefficient of determination (R2) value was decreasing. This research concludes that the Prais Winsten method can overcome autocorrelation.
Objectives
This project combined administrative data with completed randomised controlled trials to understand whether educational interventions led to additional benefits or costs beyond those initially evaluated. The purpose of the project was to demonstrate that this can be done, the value of such work, and what needs to happen next to embed such approaches.
Methods
We reviewed the Education Endowment Foundation trials database for studies that could plausibly link to other outcomes: exclusion / suspension from school, criminal conviction, or being Not in Education, Employment or Training (NEET). After shortlisting we intended to link at least one trial to different administrative datasets containing these outcomes.
We undertook three other tasks:
• Understanding barriers to data sharing within government from a behavioural perspective and proposed solutions to those challenges.
• Reviewing theories of change for some interventions to assess whether/how there could be pathways to the outcomes we were looking at.
• Asking intervention developers and evaluators for their predictions and rationales for possible impacts on other outcomes, without them knowing the results from reanalysis.
Results
We were successful in linking more than 30 completed trials to new outcome data. In some instances we were able to recover more outcome data than the original study through the use of administrative data. We then reanalysed these trials to assess the impact of these interventions on alternative outcomes.
We also developed an approach to creating low-fidelity synthetic data to allow for more and easier data sharing by data owners, as well as proposing an asymmetric approach to linkage that can speed up results.
Conclusion
In some instances, seemingly negative and positive impacts of interventions that would otherwise not be known were observed. Beyond the results of the reanalysis, which prompted recommendations to a major funder, the project contributed to shaping processes for accessing administrative data, started conversations within government and other data owners about synthetic data, as well as providing a demonstration project for data linkage and reuse.
<b>Background</b>: The role of race within tribal communities is a contentious topic, and some of this acrimony emerged from 19th-century Indian policies rooted in scientific racism. There has been relatively little written on the role of intermarriage within indigenous communities. <b>Methods</b>: We link household data from the Eastern Band of Cherokee Indians in North Carolina at the turn of the 20th century to individual two-generational family trees located in legal documents to investigate the link between personal property and whether a household head had white ancestry. <b>Results</b>: We find that the racial gap in property does not follow simple racial hierarchies but rather depends on the gender of the household head. However, once selection into intermarriage is accounted for, the racial gap in property from intermarriage is eliminated. In fact, households containing a male head with close white ancestors held less property than households containing a male head without white ancestry. <b>Contribution</b>: Understanding who chose to intermarry and how intermarriages impacted the economic status of both families and their children as adults can provide key insights into understanding racial inequality today.
The coronavirus pandemic has influenced cities’ shape and future. Telecommuting, teleconferencing, telelearning and e-commerce are creating a new environment of human life. These new factors should be added to the existing problems of urbanisation that are urban sprawl and depopulation. The authors of the paper decided to examine the intensity and spatial distribution of residential construction activity and residential real-estate trade in one of the biggest Polish cities – Łódź – and neighbouring municipalities to answer the question of whether the pandemic has affected housing decisions taken by developers and buyers. Based on statistical data, attempts were made to observe new signs confirming the intensification or weakening of past trends. The research was based on desk research and comparative analysis.
Ana María Chávez Galindo, José Marcos Pinto Da Cunha, Jorge Barquero
et al.
En este artículo se estudia la hipótesis de la “desconcentración concentrada”, que plantea que la pérdida de gravitación demográfica y económica de las áreas metropolitanas se debe a movimientos migratorios de corta distancia, lo que puede ampliar su radio de influencia y sus relaciones funcionales, manteniendo o aun reforzando su importancia dentro del sistema urbano nacional. Para verificar esta hipótesis, se ofrece una propuesta metodológica que operativiza el concepto de desconcentración concentrada, utilizando dos dimensiones analíticas: i) peso relativo del área metropolitana en la población total y en la población urbana del país, y ii) movimientos de migración interna reciente entre el área metropolitana y sus entornos cercanos y lejanos. Esta propuesta se aplica a cinco áreas metropolitanas de América Latina. Los resultados sugieren que el cambio en la distribución territorial de la población es diverso, ya que en algunos casos no se observa pérdida del peso demográfico de la gran ciudad, mientras que en otras esa pérdida es reducida, o en otras más existen signos de desconcentración concentrada.
Social Sciences, Demography. Population. Vital events
Objectives To determine whether spontaneous reporting rates of myocarditis and pericarditis differed in immunocompromised patients compared with the whole population overall, and in terms of demographics, vaccine dose and time-to-onset. Design Systematic review of spontaneously reported data from the European Union/European Economic Area (EU/EEA), the USA and the UK. Data sources EudraVigilance (EU/EEA), Vaccine Adverse Event Reporting System (VAERS; USA) and the Medicines and Healthcare products Regulatory Agency (UK) spontaneous reporting databases were searched from date of vaccine launch to 1 December 2021. Eligibility criteria Publicly available spontaneous reporting data for ‘myocarditis’ and ‘pericarditis’ from EU/EEA and USA following COVID-19 messenger RNA vaccines. Reports with comorbidities or concurrent medication indicative of transplantation, HIV infection or cancer (‘immunocompromised’ population) were compared with each overall database population. Data extraction and synthesis Two researchers extracted data. Spontaneously reported events of myocarditis and pericarditis were presented for immunocompromised populations for each data source, stratified by age, sex, dose and time-to-onset (where available). Seriousness of each event was determined according to the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) Guideline E2A definition. Proportional reporting ratio (PRR) was calculated. Results There were 178 reports of myocarditis and pericarditis among immunocompromised individuals overall. Seriousness was comparable between the immunocompromised and overall populations in both databases. No trends in age or sex were observed among immunocompromised individuals. Most reports followed a second vaccine dose and occurred within 14 days. The frequency of reporting was similar to the wider population (PRR=1.36 (95% CI=0.89 to 1.82) for VAERS population). Conclusions Myocarditis and pericarditis following COVID-19 vaccination are very rare, and benefits of COVID-19 vaccination continue to outweigh any perceived risks. Reporting rates of myocarditis and pericarditis were similar in immunocompromised individuals, however defining characteristics differed compared with the whole population; therefore, continued monitoring of adverse events following vaccination remains vital to understand differences between population subgroups.
First birth is one of the most significant events in woman’s life which signifies her taking on the roles and responsibilities of a mother. This article tries to figure out the factors that determine the age at first birth of women aged 15 to 24 years. Utilizing the Nepal Demographic and Health Survey 2016 data, this study examines the socio economic and demographic determinants of age at first birth among the women aged 15-24 years with the 4,849 married women. The analysis has made using the mean distribution and two tailed Pearson’s correlation has been used for statistical test. The result shows that women’s education, occupation, wealth index and age at marriage has positive correlation with the age at first birth. It is expected that higher the use of contraceptive method has lower the fertility. Though, use of contraceptive methods seems no any association with the age of women at first birth. Study reveals that unmet need for family planning is high of that age group. That’s why, supply of family planning methods of target population group can play vital role to postpone age at first birth.
Introduction
Prior research into the prevalence of SARS-CoV-2 infection amongst people experiencing homelessness (PEH) largely relates to people in communal forms of temporary accommodation in contexts where this type of accommodation remained a major part of the response to homelessness during the COVID-19 pandemic. Little is known about the prevalence of SARS-CoV-2 amongst PEH more broadly, and in a policy and practice context that favoured self-contained accommodation, such as Wales, UK.
Objective
Describe the prevalence of SARS-CoV-2 amongst PEH in Wales, UK, using routinely collected administrative data from the Secure Anonymised Information Linkage Databank.
Methods
Routinely collected data were used to identify PEH in Wales between 1st March 2020 and 1st March 2021. Using SARS-CoV-2 pathology testing data, prevalence rates were generated for PEH and three comparator groups: (1) the not-homeless population; (2) a cohort `exact matched' for age, sex, local authority and area deprivation; and (3) a matched comparison group created using these same variables and Propensity Score Matching (PSM). Three logistic regressions were run on samples containing each of the comparator groups to explore the effect of experiencing homelessness on testing positive for SARS-CoV-2.
Results
The prevalence of SARS-CoV-2 infection amongst PEH was 5.0%, compared to the not-homeless population at 5.6%. For the exact matched and PSM match comparator groups, prevalence was 6.9% and 6.7%, respectively. Logistic regression found that SARS-CoV-2 infection was 0.9 times less likely amongst PEH compared to people not experiencing homelessness from the general population. The odds of SARS-CoV-2 infection for PEH was 0.75 and 0.73 where the `not-homeless' comparators were from the exact match and PSM samples, respectively.
Conclusion
Our analysis revealed that a year into the COVID-19 pandemic, the prevalence of SARS-CoV-2 amongst PEH in Wales was lower than the general population. A policy response to homelessness that moved away from communal accommodation may be partly responsible for the reduced SAR-CoV-2 infection amongst PEH.
The article discusses the issues of improving physical culture and health activities as important strategic objectives of social policy in solving demographic problems and based on the joint activities of sports organizations, educational organizations, health care systems, families and other social institutions. Social policy activities in the field of solving demographic problems are usually aimed at studying and regulation of processes of population reproduction, maintaining or changing the trend of population dynamics, resettlement of internally displaced persons, improving the quality and standard of living, availability of high quality medical care, providing of social, cultural, educational and other vital services. Social policy is viewed as an organic part of state policy, specific in its goals, objectives and methods of activity: it is social support that becomes today the most significant and popular mechanism for the state's influence on the development of the demographic state in the field of population. The fundamental characteristic of social policy in the regulation of demographic processes is not a direct impact on the dynamics of population reproduction, but an indirect impact through the ideological potential of human capital resources, the implementation of rational social programs (Cross of Nations running event, the Russian Ski Track, the TRP), use of the capabilities of various social institutions, socio-economic potential of the state, etc. In the article, the authors prove the value of physical culture and sports for the population, its lifestyle in improving the demographic state of the country as a whole. The strategy of social policy in the field of physical culture and sports is aimed at timely prevention of diseases, maintaining a high working capacity of the population, preventing offenses, overcoming any bad habits, especially among the younger generation. The listed aspects together contribute to the improvement of demographic processes in the country.
Accurate inference of demographic histories for temperate tree species can aid our understanding of current climate change as a driver of evolution. Microsatellites are more suitable for inferring recent historical events due to their high mutation rates. However, most programs analyzing microsatellite data assume a strict stepwise mutation model (SMM), which could cause false detection of population shrinkage when microsatellite mutation does not follow SMM. This study aims to reconstruct the recent demographic histories of five cool-temperate tree species in Eastern Asia, Quercus mongolica, Q. liaotungensis, Juglans cathayensis, J. mandshurica and J. ailantifolia, by using 19 microsatellite markers with two methods considering generalized stepwise mutation model (GSM) (MIGRAINE and VarEff). Both programs revealed that all the five species experienced expansions after the Last Glacial Maximum (LGM). Within butternuts, J. cathayensis experienced a more serious bottleneck than the other species, and within oaks, Q. mongolica showed a moderate increase in population size and remained stable after the expansion. In addition, the point estimates of the multistep mutation proportion in the GSM model (pGSM) for all five species were between 0.50 and 0.65, indicating that when inferring population demographic history of the cool-temperate forest species using microsatellite markers, it is better to assume a GSM rather than a SMM. This study provides the first direct evidence that five cool-temperate tree species in East Asia have experienced expansions after the LGM with microsatellite data. Considering the mutation model of microsatellite has a vital influence on demographic inference, combining multiple programs such as MIGRAINE and VarEff can effectively reduce errors caused by inappropriate model selection and prior setting.
Objectives To assess the efficacy of ivermectin in addition to standard treatment compared to standard treatment alone in reducing hospitalizations in the COVID-19 patient population. Trial design IVERCOR-COVID19 will be a single-center, prospective, randomized, double-blind, parallel group (1:1 ratio), placebo-controlled study. Participants Patients who meet the following criteria will be invited to participate: Inclusion criteria: (1) Over 18 years of age who reside in the province of Corrientes at the time of diagnosis. (2) Confirmed diagnosis of COVID-19 by polymerase chain reaction (PCR) test for detection of SARS-CoV2 in the last 48 h. (3) In the case of women of childbearing age, they must be using a contraceptive method of proven efficacy and safety (barrier, hormonal, or permanent contraceptives) for at least 3 months prior to inclusion in the present study and for the entire period of time for the duration of the study and until at least 30 days after the end of this study. A woman will be considered to have no reproductive capacity if she is postmenopausal (at least 2 years without her menstrual cycles) or if she has undergone surgical sterilization (at least 1 month before the time of inviting her to participate in this study). (4) Weight at the time of inclusion greater than 48 kg. (5) That they sign the informed consent for participation in the study. Exclusion criteria: (1) pregnant or breastfeeding women; (2) known allergy to ivermectin or some of the components of ivermectin tablets or placebo; (3) current use of home oxygen; (4) require hospitalization due to COVID-19 at the time of diagnosis or history of hospitalization for COVID-19; (5) presence of mal-absorptive syndrome; (6) presence of any other concomitant acute infectious disease; (7) known history of severe liver disease, for example liver cirrhosis; (8) need or use of antiviral drugs at the time of admission for another viral pathology other than COVID-19; (9) need or use of hydroxychloroquine or chloroquine; (10) use of ivermectin up to 7 days prior to randomization; (11) patients on dialysis or who have required it in the last 2 months or who plan to do it in the next 2 months; and (12) current participation or in the last 30 days in a research study that has included the administration of a drug (Table 1 ). Table 1 Ivermectin/placebo dose according to patient weight Patient weight Ivermectin/placebo dose Total dose (mg) Equal to or greater than 48 kg and less than 80 kg 2 tablets of 6 mg each at the time of inclusion and 2 tablets 24 h after the first intake 24 Equal or greater than 80 kg and less than 110 kg 3 tablets of 6 mg each at the time of inclusion and 3 tablets 24 h after the first intake 36 Equal or greater than 110 kg 4 tablets of 6 mg each at the time of inclusion and 4 tablets 24 h after the first intake 48 The study will be carried out by the Ministry of Public Health of the Province of Corrientes (Argentina) in coordination with the Institute of Cardiology of Corrientes in the Province of Corrientes, Argentina. Intervention and comparator Intervention group: patients who are randomized to ivermectin will receive the dose according to their weight (patients up to 80 kg will receive 2 tablets of 6 mg ivermectin; patients with more than 80 kg and up to 110 kg will receive 3 tablets of 6 mg of ivermectin; patients weighing more than 110 kg will receive 4 tablets of 6 mg ivermectin) the day they enter the study and the same dose 24 h after the first dose. Control group: patients who are randomized to placebo will receive the dose according to their weight (patients up to 80 kg will receive 2 tablets of 6 mg placebo; patients with more than 80 kg and up to 110 kg will receive 3 tablets of 6 mg of placebo; patients weighing more than 110 kg will receive 4 tablets of 6 mg placebo) on the day they enter the study and the same dose 24 h after the first dose (Table 2 ). Table 2 Inclusion and exclusion criteria Inclusion criteria Exclusion criteria 1. Over 18 years of age who reside in the province of Corrientes at the time of diagnosis 1. Pregnant or breastfeeding women 2.Confirmed diagnosis of COVID-19 by polymerase chain reaction test for detection of SARS-CoV2 in the last 48 h 2. Known allergy to ivermectin or some of the components of ivermectin tablets or placebo 3. In case of being women of childbearing age, they must be using a contraceptive method of proven efficacy and safety (barrier, hormonal, or permanent contraceptives) for at least 3 months prior to inclusion in the present study, during the entire period of time for the duration of the study, and until at least 30 days after the end of this study. A woman will be considered to have no reproductive capacity if she is postmenopausal (at least 2 years without her menstrual cycles) or if she has undergone surgical sterilization (at least 1 month before the time of inviting her to participate in this study) 3. Current use of home oxygen 4. Weight at the time of inclusion equal to or greater than 48 kg 4. That require hospitalization due to COVID-19 at the time of diagnosis or history of hospitalization for COVID-19 5. That they sign the informed consent for participation in the study 5. Presence of mal-absorptive syndrome 6. Presence of any other concomitant acute infectious disease 7. Known history of severe liver disease, for example liver cirrhosis 8. Need or use of antiviral drugs at the time of admission for another viral pathology other than COVID-19 9. Need or use of hydroxychloroquine or chloroquine 10. Use of ivermectin up to 7 days prior to randomization 11. Patients on dialysis or who have required it in the last 2 months or who plan to do it in the next 2 months 12. Current participation or in the last 30 days in a research study that has included the administration of a drug Main outcomes Primary outcome will be the percentage of hospitalizations in patients with COVID-19 in the intervention and control groups. Secondary outcomes: time to hospitalization in each of the arms of the study: number of days elapsed from the inclusion in the study until the hospitalization of the patient; percentage of use of invasive mechanical ventilation in each of the study arms: every patient who is connected to invasive mechanical ventilation after signing the informed consent and before the final study visit; time to invasive mechanical ventilation in each of the arms of the study: number of days elapsed from inclusion in the study to connection to invasive mechanical ventilation of the patient; percentage of patients requiring dialysis in each of the study arms: all patients who require renal replacement therapy of any kind, temporary or permanent, and which begins after signing the informed consent and before the final visit; mortality from all causes in each of the two trial groups: death of the patient, from any cause. Negative PCR swab at 3 ± 1 and 12 ± 2 days after entering the study. Ivermectin safety: it will be analyzed according to the incidence of adverse events that patients present in the intervention and control groups. The end of study (EOS) is recorded as the day the patient is discharged or death. Discharge will be granted according to the current recommendations of the Ministry of Public Health of the Province of Corrientes. A follow-up visit (EOF) will be made by phone 30 days after the EOS when vital status will be verified. Randomization Randomization will be done through a web system with randomly permuted blocks. Randomization will be carried out by one of the investigators who will not participate in the inclusion of patients or in the delivery of medication (Table 3 ). Table 3 EOS end of study, EOF end of follow-up Visit Basal and randomization, day 0 Day 3 ± 1 Day 12 ± 2 V#1 V#2 V#3 EOS EOF Informed consent X – – – – Inclusion/exclusion criteria X – – – – Demographic data and medical history X – – – – Concomitant medication X – – – – Vital signs* X X – – – Anthropometric data ^ X – – – – Basal laboratory X – – – – PCR swab – X X – – Assessment of adverse events – X X X – Final objective evaluation – X X X X Randomization X – – – – Adherence to treatment X X – – – *Includes heart rate, temperature, and oxygen saturation by a digital saturometer ^Includes weight and height Blinding (masking) The participants, investigators, care providers, and outcome assessors will be blinded. Numbers to be randomized (sample size) We will include a total of 500 patients (250 patients in each group). Trial status This is version 1.0, 17 August 2020. The recruitment started on 19 August 2020, and we anticipate the trial will finish recruitment on 31 December 2020. Trial registration ClinicalTrials.gov NCT04529525 . Registered on 26 August 2020 Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1 ). In the interest of expediting the dissemination of this material, the familiar formatting has been eliminated; this letter serves as a summary of the key elements of the full protocol.
Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors represent a powerful class of technology for digital, wireless measurements of mechano-acoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. Here, we introduce an effort that integrates such an MA sensor, a cloud data infrastructure and data analytics approaches based on digital filtering and convolutional neural networks for comprehensive monitoring of COVID-19 infections in sick and healthy individuals in a population, both in the hospital and the home. This hardware/software system extracts diverse signatures of health status in an automated fashion from a single device and time series data stream. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate direct correlations between the time and intensity of coughing, speaking and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a comprehensive collection of other biometrics, with recording times for individuals of more than a month after disease diagnosis. These pilot studies include 3,111 hours of data spanning 363 days from 37 COVID-19 patients (20 females, 17 males) with 27,651 coughs detected in total along with continuous measurements of heart rate, respiratory rate, physical activity, and skin temperature. Manual labeling of randomly sampled 10,258 vocal events from 11 COVID-19 patients (6 females, 5 males) suggests a sensitivity of 85% and a specificity of 96% in cough detection using automated algorithms. The collective results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology also opens opportunities to study patterns in biometrics across individuals and among different demographic groups.
В 2016 г. в Москве, по данным полиции, в дорожно-транспортных происшествиях (далее ДТП) погиб 561 человек, тогда как согласно данным органов статистики (если опираться на подход Всемирной организации здравоохранения (далее ВОЗ) к агрегации кодов МКБ-10 для определения числа умерших в ДТП, используемый в базе данных «Здоровье для всех») – 790 человек. Для понимания тенденций дорожно-транспортной смертности и для разработки обоснованных и эффективных мер по ее предотвращению необходимы надежные данные, поэтому важно понять причины расхождения чисел погибших в ДТП в Москве по разным официальным источникам информации. Цель исследования – выявить причины, обусловливающие несовпадение чисел погибших по данным полиции и государственной статистики смертности, с помощью связывания записей о погибших в ДТП в Москве в 2016 г. на индивидуальном уровне. В исследовании использованы данные органов статистики о 1 891 016 умерших от всех причин смерти и данные полиции о 20 302 погибших в ДТП в России в 2016 г. Оба массива информации представляли собой индивидуальные неперсонифицированные записи о погибших. В результате их сопоставления было получено 944 записи с упоминанием Москвы хотя бы в одном из двух источников данных, из которых 699 записей можно рассматривать как связанные, 245 как несвязанные. В 6% связанных записей код причины смерти, не относится к той группе кодов, которые ВОЗ использует для определения числа умерших в ДТП в базе данных «Здоровье для всех», а в 35% связанных записей регион регистрации ДТП отличался от региона регистрации смерти. Из 561 записи полиции о погибших 84% было связано с данными государственной статистики смертности, а из 790 умерших по данным органов статистики 80% записей связано с данными полиции.