Protein quality assessment with a loss function designed for high-quality decoys.

Motivation: The prediction of a protein 3D structure is essential for understanding protein function, drug discovery, and disease mechanisms; with the advent of methods like AlphaFold that are capable of producing very high-quality decoys, ensuring the quality of those decoys can provide further confidence in the accuracy of their predictions. Results: In this work, we describe Qϵ, a graph convolutional network (GCN) that utilizes a minimal set of atom and residue features as inputs to predict the global distance test total score (GDTTS) and local distance difference test (lDDT) score of a decoy. To improve the model's performance, we introduce a novel loss function based on the ϵ-insensitive loss function used for SVM regression. This loss function is specifically designed for evaluating the characteristics of the quality assessment problem and provides predictions with improved accuracy over standard loss functions used for this task. Despite using only a minimal set of features, it matches the performance of recent state-of-the-art methods like DeepUMQA. Availability: The code for Qϵ is available at https://github.com/soumyadip1997/qepsilon.

Nanohybrids of atomically precise metal nanoclusters.

Atomically precise metal nanoclusters (NCs) with molecule-like structures are emerging nanomaterials with fascinating chemical and physical properties. Photoluminescence (PL), catalysis, sensing, etc., are some of the most intriguing and promising properties of NCs, making the metal NCs potentially beneficial in different applications. However, long-term instability under ambient conditions is often considered the primary barrier to translational research in the relevant application fields. Creating nanohybrids between such atomically precise NCs and other stable nanomaterials (0, 1, 2, or 3D) can help expand their applicability. Many such recently reported nanohybrids have gained promising attention as a new class of materials in the application field, exhibiting better stability and exciting properties of interest. This perspective highlights such nanohybrids and briefly explains their exciting properties. These hybrids are categorized based on the interactions between the NCs and other materials, such as metal-ligand covalent interactions, hydrogen-bonding, host-guest, hydrophobic, and electrostatic interactions during the formation of nanohybrids. This perspective will also capture some of the new possibilities with such nanohybrids.

On the choice of negative examples for prediction of host-pathogen protein interactions.

As practitioners of machine learning in the area of bioinformatics we know that the quality of the results crucially depends on the quality of our labeled data. While there is a tendency to focus on the quality of positive examples, the negative examples are equally as important. In this opinion paper we revisit the problem of choosing negative examples for the task of predicting protein-protein interactions, either among proteins of a given species or for host-pathogen interactions and describe important issues that are prevalent in the current literature. The challenge in creating datasets for this task is the noisy nature of the experimentally derived interactions and the lack of information on non-interacting proteins. A standard approach is to choose random pairs of non-interacting proteins as negative examples. Since the interactomes of all species are only partially known, this leads to a very small percentage of false negatives. This is especially true for host-pathogen interactions. To address this perceived issue, some researchers have chosen to select negative examples as pairs of proteins whose sequence similarity to the positive examples is sufficiently low. This clearly reduces the chance for false negatives, but also makes the problem much easier than it really is, leading to over-optimistic accuracy estimates. We demonstrate the effect of this form of bias using a selection of recent protein interaction prediction methods of varying complexity, and urge researchers to pay attention to the details of generating their datasets for potential biases like this.

Active leisure, passive leisure and health.

Leisure consumption has been increasing in the United States since the 1960s. Over the same period, inactive lifestyles have contributed to adverse health outcomes. We propose a new way of categorizing leisure into groups based on the amount of physical exercise needed. Our results show that physically active leisure is a normal good whose demand rises with education and health, while physically passive leisure is an inferior good whose demand rises with lower education and poorer health. These patterns allow us to propose a taxonomy that categorizes various leisure activities into 'Active' and 'Passive' groups.

Family-Related Factors Associated with Caries Prevalence in the Primary Dentition of 5-6-year-old Children in Urban and Rural Areas of Jabalpur City.

AIM: The purpose of this study was to examine the prevalence of dental caries in primary dentition of 5-6-year-old children in urban and rural areas of Jabalpur city. MATERIALS AND METHODS: The present cross-sectional study was conducted in the rural and urban areas of Jabalpur city, India. A power analysis was carried out to select a representative sample of 5-6-year-old children (n = 408), 204 from government schools and 204 from private schools. Parents were interviewed using a self-structured questionnaire to collect data with regard to variables under evaluation. STATISTICAL ANALYSIS: Collected data were subjected to descriptive analysis using the SPSS 12.0 version. Risk factor association with dental caries was investigated using a stepwise logistic regression analysis with P < 0.05 considered significant. RESULTS: This shows significantly higher decayed missing filled teeth among rural children than urban children. It was seen that 46.5% of children whose mothers were illiterate were affected with dental caries. In urban area, 91.5% of children whereas 77% of children in rural area have parental control on sugar consumption. CONCLUSION: It is important to focus on parents' education level when planning preventive programs for young children. Assessing family-related risk factors is essential when instituting preventive/treatment programs for young children.