Evolving symbolic density functionals

Systematic development of accurate density functionals has been a decades-long challenge for scientists. Despite the emerging application of machine learning (ML) in approximating functionals, the resulting ML functionals usually contain more than tens of thousands parameters, which makes a huge gap in the formulation with the conventional human-designed symbolic functionals. We propose a new framework, Symbolic Functional Evolutionary Search (SyFES), that automatically constructs accurate functionals in the symbolic form, which is more explainable to humans, cheaper to evaluate, and easier to integrate to existing density functional theory codes than other ML functionals. We first show that without prior knowledge, SyFES reconstructed a known functional from scratch. We then demonstrate that evolving from an existing functional $\omega$B97M-V, SyFES found a new functional, GAS22 (Google Accelerated Science 22), that performs better for the majority of molecular types in the test set of Main Group Chemistry Database (MGCDB84). Our framework opens a new direction in leveraging computing power for the systematic development of symbolic density functionals

Expression profiling of metalloproteinases and tissue inhibitors of metalloproteinases in normal and degenerate human achilles tendon

To profile the messenger RNA (mRNA) expression for the 23 known genes of matrix metalloproteinases (MMPs), 19 genes of ADAMTS, 4 genes of tissue inhibitors of metalloproteinases (TIMPs), and ADAM genes 8, 10, 12, and 17 in normal, painful, and ruptured Achilles tendons. Tendon samples were obtained from cadavers or from patients undergoing surgical procedures to treat chronic painful tendinopathy or ruptured tendon. Total RNA was extracted and mRNA expression was analyzed by quantitative real-time reverse transcription–polymerase chain reaction, normalized to 18S ribosomal RNA. In comparing expression of all genes, the normal, painful, and ruptured Achilles tendon groups each had a distinct mRNA expression signature. Three mRNA were not detected and 14 showed no significant difference in expression levels between the groups. Statistically significant (P < 0.05) differences in mRNA expression, when adjusted for age, included lower levels of MMPs 3 and 10 and TIMP-3 and higher levels of ADAM-12 and MMP-23 in painful compared with normal tendons, and lower levels of MMPs 3 and 7 and TIMPs 2, 3, and 4 and higher levels of ADAMs 8 and 12, MMPs 1, 9, 19, and 25, and TIMP-1 in ruptured compared with normal tendons. The distinct mRNA profile of each tendon group suggests differences in extracellular proteolytic activity, which would affect the production and remodeling of the tendon extracellular matrix. Some proteolytic activities are implicated in the maintenance of normal tendon, while chronically painful tendons and ruptured tendons are shown to be distinct groups. These data will provide a foundation for further study of the role and activity of many of these enzymes that underlie the pathologic processes in the tendon

Seroconversion to Pandemic (H1N1) 2009 Virus and Cross-Reactive Immunity to Other Swine Influenza Viruses

To assess herd immunity to swine influenza viruses, we determined antibodies in 28 paired serum samples from participants in a prospective serologic cohort study in Hong Kong who had seroconverted to pandemic (H1N1) 2009 virus. Results indicated that infection with pandemic (H1N1) 2009 broadens cross-reactive immunity to other recent subtype H1 swine viruses

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review

Background and Objectives When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model. Methods We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020. Results In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%). Conclusion Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review

Background Having an appropriate sample size is important when developing a clinical prediction model. We aimed to review how sample size is considered in studies developing a prediction model for a binary outcome. Methods We searched PubMed for studies published between 01/07/2020 and 30/07/2020 and reviewed the sample size calculations used to develop the prediction models. Using the available information, we calculated the minimum sample size that would be needed to estimate overall risk and minimise overfitting in each study and summarised the difference between the calculated and used sample size. Results A total of 119 studies were included, of which nine studies provided sample size justification (8%). The recommended minimum sample size could be calculated for 94 studies: 73% (95% CI: 63–82%) used sample sizes lower than required to estimate overall risk and minimise overfitting including 26% studies that used sample sizes lower than required to estimate overall risk only. A similar number of studies did not meet the ≥ 10EPV criteria (75%, 95% CI: 66–84%). The median deficit of the number of events used to develop a model was 75 [IQR: 234 lower to 7 higher]) which reduced to 63 if the total available data (before any data splitting) was used [IQR:225 lower to 7 higher]. Studies that met the minimum required sample size had a median c-statistic of 0.84 (IQR:0.80 to 0.9) and studies where the minimum sample size was not met had a median c-statistic of 0.83 (IQR: 0.75 to 0.9). Studies that met the ≥ 10 EPP criteria had a median c-statistic of 0.80 (IQR: 0.73 to 0.84). Conclusions Prediction models are often developed with no sample size calculation, as a consequence many are too small to precisely estimate the overall risk. We encourage researchers to justify, perform and report sample size calculations when developing a prediction model

Poor handling of continuous predictors in clinical prediction models using logistic regression:a systematic review

Background and Objectives When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model. Methods We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020. Results In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%). Conclusion Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model

The impact of contact tracing in clustered populations

The tracing of potentially infectious contacts has become an important part of the control strategy for many infectious diseases, from early cases of novel infections to endemic sexually transmitted infections. Here, we make use of mathematical models to consider the case of partner notification for sexually transmitted infection, however these models are sufficiently simple to allow more general conclusions to be drawn. We show that, when contact network structure is considered in addition to contact tracing, standard “mass action” models are generally inadequate. To consider the impact of mutual contacts (specifically clustering) we develop an improvement to existing pairwise network models, which we use to demonstrate that ceteris paribus, clustering improves the efficacy of contact tracing for a large region of parameter space. This result is sometimes reversed, however, for the case of highly effective contact tracing. We also develop stochastic simulations for comparison, using simple re-wiring methods that allow the generation of appropriate comparator networks. In this way we contribute to the general theory of network-based interventions against infectious disease

Impact of natural organic matter on particle behavior and phototoxicity of titanium dioxide nanoparticles

Due to their inherent phototoxicity and inevitable environmental release, titanium dioxide nanoparticles (nano- TiO2) are increasingly studied in the field of aquatic toxicology. One of the particular interests is the interactions between nano-TiO2 and natural organic matter (NOM). In this study, a series of experiments was conducted to study the impacts of Suwannee River natural organic matter (SRNOM) on phototoxicity and particle behaviors of nano-TiO2. For Daphnia magna, after the addition of 5 mg/L SRNOM, LC50 value decreased significantly from 1.03 (0.89–1.20) mg/L to 0.26 (0.22–0.31) mg/L. For zebrafish larvae, phototoxic LC50 values were 39.9 (95% CI, 25.9–61.2) mg/L and 26.3 (95% CI, 18.3–37.8) mg/L, with or without the presence of 5 mg/L SRNOM, respectively. There was no statistically significant change of these LC50 values. The impact of SRNOM on phototoxicity of nano- TiO2 was highly dependent on test species, with D. magna being the more sensitive species. The impact on particle behavior was both qualitatively and quantitatively examined. A global predictive model for particle behavior was developed with a three-way interaction of SRNOM, TiO2 concentration, and time and an additive effect of ionic strength. Based on power analyses, 96-h exposure in bioassayswas recommended for nanoparticle–NOM interaction studies. The importance of reactive oxygen species (ROS) quenching of SRNOMwas also systematically studied using a novel exposure system that isolates the effects of environmental factors. These experiments were conducted with minimal impacts of other important interaction mechanisms (NOM particle stabilization, NOM UV attenuation, and NOM photosensitization). This study highlighted both the particle stabilization and ROS quenching effects of NOM on nano-TiO2 in an aquatic system. There is an urgent need for representative test materials, together with key environmental factors, for future risk assessment and regulations of nanomaterials

Antibodies to the Mr 64,000 (64K) protein in islet cell antibody positive non-diabetic individuals indicate high risk for impaired Beta-cell function

A prospective study of a normal childhood population identified 44 islet cell antibody positive individuals. These subjects were typed for HLA DR and DQ alleles and investigated for the presence of antibodies to the Mr 64,000 (64K) islet cell antigen, complement-fixing islet cell antibodies and radiobinding insulin autoantibodies to determine their potency in detecting subjects with impaired Beta-cell function. At initial testing 64K antibodies were found in six of 44 islet cell antibody positive subjects (13.6%). The same sera were also positive for complement-fixing islet cell antibodies and five of them had insulin autoantibodies. During the follow-up at 18 months, islet cell antibodies remained detectable in 50% of the subjects studied. In all six cases who were originally positive, 64K antibodies were persistently detectable, whereas complement-fixing islet cell antibodies became negative in two of six and insulin autoantibodies in one of five individuals. HLA DR4 (p < 0.005) and absence of asparic acid (Asp) at position 57 of the HLA DQ chain (p < 0.05) were significantly increased in subjects with 64K antibodies compared with control subjects. Of 40 individuals tested in the intravenous glucose tolerance test, three had a first phase insulin response below the first percentile of normal control subjects. Two children developed Type 1 (insulin-dependent) diabetes mellitus after 18 and 26 months, respectively. Each of these subjects was non-Asp homozygous and had persistent islet cell and 64K antibodies. We conclude that 64K antibodies, complement-fixing islet cell antibodies and insulin autoantibodies represent sensitive serological markers in assessing high risk for a progression to Type 1 diabetes in islet cell antibody positive non-diabetic individuals