Model Chemistry Recommendations for Scaled Harmonic Frequency Calculations: A Benchmark Study.

Despite the widespread popularity of scaled harmonic frequency calculations to predict experimental fundamental frequencies in chemistry, sparse benchmarking is available to guide users on the appropriate level of theory and basis set choices (model chemistry) or deep understanding of expected errors. An updated assessment of the best approach for scaling to minimize errors is also overdue. Here, we assess the performance of over 600 popular, contemporary, and robust model chemistries in the calculation of scaled harmonic frequencies, evaluating different scaling factor types and their implications in the scaled harmonic frequencies and model chemistry performance. We can summarize our results into three main findings: (1) Using model-chemistry-specific scaling factors optimized for three different frequency regions (low (<1,000 cm-1), mid (1,000-2,000 cm-1), and high (>2,000 cm-1)) results in substantial improvements in the agreement between the scaled harmonic and experimental frequencies compared to other choices. (2) Larger basis sets and more robust levels of theory generally lead to superior performance; however, the particular model chemistry choice matters and poor choices lead to significantly reduced accuracies. (3) Outliers are expected in routine calculations regardless of the model chemistry choice. Our benchmarking results here do not consider the intensity of vibrational transitions; however, we draw upon previous benchmarking results for dipole moments that highlight the importance of diffuse functions (i.e., augmented basis sets) in high-quality intensity predictions. In terms of specific recommendations, overall, the highest accuracy model chemistries are double-hybrid density functional approximations with a non-Pople augmented triple-ζ basis set, which can produce median frequency errors down to 7.6 cm-1 (DSD-PBEP86/def2-TZVPD), which is very close to the error in the harmonic approximation, i.e., the anharmonicity error. Double-ζ basis sets should not be used with double-hybrid functionals as there is no improvement compared to hybrid functional results (unlike for double-hybrid triple-ζ model chemistries). Note that 6-311G* and 6-311+G* basis sets perform like a double-ζ basis set for vibrational frequencies. After scaling, all studied hybrid functionals with non-Pople triple-ζ basis sets will produce median errors of less than 15 cm-1, with the best result of 9.9 cm-1 with B97-1/def2-TZVPD. Appropriate matching of double-ζ basis sets with hybrid functionals can produce high-quality results, but the precise choice of functional and basis set is more important. The B97-1, TPSS0-D3(BJ), or ωB97X-D hybrid density functionals with 6-31G*, pc-1, or pcseg-1 are recommended for fast routine calculations, all delivering median errors of 11-12 cm-1. Note that dispersion corrections are not easily available for B97-1; given its strong performance here, we recommend these be added to major programs in coming updates.

VIBFREQ1295: A New Database for Vibrational Frequency Calculations.

High-throughput approaches for producing approximate vibrational spectral data for molecules of astrochemistry interest rely on harmonic frequency calculations using computational quantum chemistry. However, model chemistry recommendations (i.e., a level of theory and basis set pair) for these calculations are not yet available and, thus, thorough benchmarking against comprehensive benchmark databases is needed. Here, we present a new database for vibrational frequency calculations (VIBFREQ1295) storing 1295 experimental fundamental frequencies and CCSD(T)(F12*)/cc-pVDZ-F12 ab initio harmonic frequencies from 141 molecules. VIBFREQ1295's experimental data was complied through a comprehensive review of contemporary experimental data, while the ab initio data was computed here. The chemical space spanned by the molecules chosen is considered in-depth and is shown to have good representation of common organic functional groups and vibrational modes. Scaling factors are routinely used to approximate the effect of anharmonicity and convert computed harmonic frequencies to predicted fundamental frequencies. With our experimental and high-level ab initio data, we find that a single global uniform scaling factor of 0.9617(3) results in median differences of 15.9(5) cm-1. A far superior performance with a median difference of 7.5(5) cm-1 can be obtained, however, by using separate scaling factors (SFs) for three regions: frequencies less than 1000 cm-1 (SF = 0.987(1)), between 1000 and 2000 cm-1 (SF = 0.9727(6)), and above 2000 cm-1 (SF = 0.9564(4)). This sets a lower bound for the performance that could be reliably obtained using scaling of harmonic frequency calculations to predict experimental fundamental frequencies. VIBFREQ1295's most important purpose is to provide a robust database for benchmarking the performance of any vibrational frequency calculations. VIBFREQ1295 data could also be used to train machine-learning models for the prediction of vibrational spectra and as a reference and data starting point for more detailed spectroscopic modeling of particular molecules. The database can be found as part of the Supporting Information for this paper or in the Harvard DataVerse at https://doi.org/10.7910/DVN/VLVNU7.

Machine learning model from a Spanish cohort for prediction of SARS-COV-2 mortality risk and critical patients.

Patients affected by SARS-COV-2 have collapsed healthcare systems around the world. Consequently, different challenges arise regarding the prediction of hospital needs, optimization of resources, diagnostic triage tools and patient evolution, as well as tools that allow us to analyze which are the factors that determine the severity of patients. Currently, it is widely accepted that one of the problems since the pandemic appeared was to detect (i) who patients were about to need Intensive Care Unit (ICU) and (ii) who ones were about not overcome the disease. These critical patients collapsed Hospitals to the point that many surgeries around the world had to be cancelled. Therefore, the aim of this paper is to provide a Machine Learning (ML) model that helps us to prevent when a patient is about to be critical. Although we are in the era of data, regarding the SARS-COV-2 patients, there are currently few tools and solutions that help medical professionals to predict the evolution of patients in order to improve their treatment and the needs of critical resources at hospitals. Moreover, most of these tools have been created from small populations and/or Chinese populations, which carries a high risk of bias. In this paper, we present a model, based on ML techniques, based on 5378 Spanish patients' data from which a quality cohort of 1201 was extracted to train the model. Our model is capable of predicting the probability of death of patients with SARS-COV-2 based on age, sex and comorbidities of the patient. It also allows what-if analysis, with the inclusion of comorbidities that the patient may develop during the SARS-COV-2 infection. For the training of the model, we have followed an agnostic approach. We explored all the active comorbidities during the SARS-COV-2 infection of the patients with the objective that the model weights the effect of each comorbidity on the patient's evolution according to the data available. The model has been validated by using stratified cross-validation with k = 5 to prevent class imbalance. We obtained robust results, presenting a high hit rate, with 84.16% accuracy, 83.33% sensitivity, and an Area Under the Curve (AUC) of 0.871. The main advantage of our model, in addition to its high success rate, is that it can be used with medical records in order to predict their diagnosis, allowing the critical population to be identified in advance. Furthermore, it uses the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD 9-CM) standard. In this sense, we should also emphasize that those hospitals using other encodings can add an intermediate layer business to business (B2B) with the aim of making transformations to the same international format.

Modelling local patterns of child mortality risk: a Bayesian Spatio-temporal analysis.

BACKGROUND: Globally, child mortality rate has remained high over the years, but the figure can be reduced through proper implementation of spatially-targeted public health policies. Due to its alarming rate in comparison to North American standards, child mortality is particularly a health concern in Mexico. Despite this fact, there remains a dearth of studies that address its spatio-temporal identification in the country. The aims of this study are i) to model the evolution of child mortality risk at the municipality level in Greater Mexico City, (ii) to identify municipalities with high, medium, and low risk over time, and (iii) using municipality trends, to ascertain potential high-risk municipalities. METHODS: In order to control for the space-time patterns of data, the study performs a Bayesian spatio-temporal analysis. This methodology permits the modelling of the geographical variation of child mortality risk across municipalities, within the studied time span. RESULTS: The analysis shows that most of the high-risk municipalities were in the east, along with a few in the north and west areas of Greater Mexico City. In some of them, it is possible to distinguish an increasing trend in child mortality risk. The outcomes highlight municipalities currently presenting a medium risk but liable to become high risk, given their trend, after the studied period. Finally, the likelihood of child mortality risk illustrates an overall decreasing tendency throughout the 7-year studied period. CONCLUSIONS: The identification of high-risk municipalities and risk trends may provide a useful input for policymakers seeking to reduce the incidence of child mortality. The results provide evidence that supports the use of geographical targeting in policy interventions.

Waste disposal and households' heterogeneity. Identifying factors shaping attitudes towards source-separated recycling in Bogotá, Colombia.

Solid waste management in many cities of developing countries is not environmentally sustainable. People traditionally dispose of their solid waste in unsuitable urban areas like sidewalks and satellite dumpsites. This situation nowadays has become a serious public health problem in big Latin American conurbations. Among these densely-populated urban spaces, the Colombia's capital and main city stands out as a special case. In this study, we aim to identify the factors that shape the attitudes towards source-separated recycling among households in Bogotá. Using data from the Colombian Department of Statistics and Bogotá's multi-purpose survey, we estimated a multivariate Probit model. In general, our results show that the higher the household's socioeconomic class, the greater its effort for separating solid wastes. Likewise, our findings also allowed us to characterize household profiles regarding solid waste separation and considering each socioeconomic class. Among these profiles, we found that at lower socioeconomic classes, the attitudes towards solid waste separation are influenced by the use of Internet, the membership to an environmentalist organization, the level of education of the head of household and the homeownership. Hence, increasing the education levels within the poorest segment of the population, promoting affordable housing policies and facilitating Internet access for the vulnerable population could reinforce households' attitudes towards a greater source-separated recycling effort.

An impact assessment of the Child Growth, Development and Care Program in the Caribbean Region of Colombia / Evaluación del Programa de Crecimiento y Desarrollo y el Cuidado del Niño en Colombia: sus efectos en la región Caribe / Avaliação do Programa de Crescimento, Desenvolvimento e Cuidado Infantil na Colômbia: seus efeitos na Região Caribe

Abstract This article aims to assess the impact of the Child Growth, Development and Care Program in the Caribbean region of Colombia by analyzing variables such as maternal childcare practices and indicators of the nutritional status and health of children under the age of five. To this end, the authors used the quasi-experimental technique Propensity Score Matching. Positive impacts included a decrease in acute diarrheal disease, and an increase in immunization and seeking treatment for acute respiratory infection or fever symptoms. However, the program had little influence on chronic and acute malnutrition in the region.

Resumen Este artículo tiene como objetivo evaluar el impacto del Programa de Crecimiento y Desarrollo y el Cuidado del Niño en la región Caribe de Colombia. La evaluación se efectúa sobre variables como el estado nutricional, indicadores antropométricos y salud de los niños menores de cinco años de edad y las prácticas maternales de cuidado infantil. Para este propósito se emplean técnicas cuasi-experimentales de emparejamiento tipo Propensity Score Matching. Entre los resultados de la evaluación se encuentran una disminución de la enfermedad diarreica aguda, una inmunización más alta y la búsqueda de tratamiento para atender síntomas de infección respiratoria aguda o fiebre. Sin embargo, el programa tuvo poca influencia sobre la desnutrición crónica y desnutrición aguda en la región.

Resumo Este artigo tem como objetivo a avaliação do impacto do Programa de Crescimento, Desenvolvimento e Cuidado Infantil na região do Caribe colombiano. A avaliação é realizada em variáveis como status nutricional, indicadores antropométricos, saúde das crianças menores de cinco anos de idade e as práticas de cuidados infantis maternos. Para isso, são utilizadas técnicas quase experimentais do tipo Propensity Score Matching. Entre os resultados da avaliação, verificou-se uma diminuição da doença diarreica aguda, uma imunização mais alta e a busca pelo tratamento de sintomas de infecção respiratória aguda ou febre. Porém, o programa teve pouca influência sobre a desnutrição crônica e a desnutrição aguda na região.

Attainments and limitations of an early childhood programme in Colombia.

The Growth and Development Monitoring Programme is a longstanding early childhood social intervention in Colombia. The programme's goal is the prevention and early identification of problems affecting children's health and nutrition. To achieve this aim, the programme's basic strategy is to educate parents about the overall health care of infants. The objective of this study is to measure the impact of this programme on children's nutrition and health status and maternal child-care practices. To address potential selection bias, we employ quasi-experimental techniques. This article uses data from the Demographic Health Survey of 2010. The evidence suggests that the programme improved immunization status and the likelihood of health care for acute respiratory infection or fever. As expected, the programme has a greater impact on children from among the poorest people in the country. In the most advanced regions and for the beneficiaries of private health care, the effects of the programme have tended to be negligible. In this sense, our central policy recommendation is to optimize the programme for the poorest households in the country.

An impact assessment of the Child Growth, Development and Care Program in the Caribbean Region of Colombia.

This article aims to assess the impact of the Child Growth, Development and Care Program in the Caribbean region of Colombia by analyzing variables such as maternal childcare practices and indicators of the nutritional status and health of children under the age of five. To this end, the authors used the quasi-experimental technique Propensity Score Matching. Positive impacts included a decrease in acute diarrheal disease, and an increase in immunization and seeking treatment for acute respiratory infection or fever symptoms. However, the program had little influence on chronic and acute malnutrition in the region.

Relationship between professional antenatal care and facility delivery: an assessment of Colombia.

The determinants of maternal and child health have been the recurrent topics of study in developing countries. Using the Demographic and Health Survey (2010) of Colombia, this study aimed to identify the determinants for professional antenatal care and institutional delivery, taking into account the interdependence of these two decisions, which we consider using a bivariate probit model. This study found that when certain factors affecting both the decision to seek prenatal care and giving birth in a hospital are neglected, the results of the estimates are inefficient. Estimates show that the effects of education, parity, regional location and economic status on institutional delivery tend to be underestimated in a univariate probit model. The results indicate that economic status, level of education, parity and medical-insurance affiliation influenced the joint likelihood of accessing professional antenatal care and delivering in a health facility. An important finding is that mothers with a higher level of education are 9 percentage points more likely to access these two health services compared with mothers who are illiterate. Another observed finding is the regional disparities. The evidence indicates that mothers in the Pacific Region, the poorest region of Colombia, are 6 percentage points less likely to access such services. Thus, the results indicate that the Colombian health policy should emphasize increasing the level of schooling of mothers and establish health facilities in the poorest regions of the country to ensure that women in need are provided with social health insurance.