An interdisciplinary consensus on the management of brain metastases in patients with renal cell carcinoma.

Brain metastases are a challenging manifestation of renal cell carcinoma. We have a limited understanding of brain metastasis tumor and immune biology, drivers of resistance to systemic treatment, and their overall poor prognosis. Current data support a multimodal treatment strategy with radiation treatment and/or surgery. Nonetheless, the optimal approach for the management of brain metastases from renal cell carcinoma remains unclear. To improve patient care, the authors sought to standardize practical management strategies. They performed an unstructured literature review and elaborated on the current management strategies through an international group of experts from different disciplines assembled via the network of the International Kidney Cancer Coalition. Experts from different disciplines were administered a survey to answer questions related to current challenges and unmet patient needs. On the basis of the integrated approach of literature review and survey study results, the authors built algorithms for the management of single and multiple brain metastases in patients with renal cell carcinoma. The literature review, consensus statements, and algorithms presented in this report can serve as a framework guiding treatment decisions for patients. CA Cancer J Clin. 2022;72:454-489.

Ensuring best practice in genomics education: A theory- and empirically informed evaluation framework.

Implementation of genomic medicine into healthcare requires a workforce educated through effective educational approaches. However, ascertaining the impact of genomics education activities or resources is limited by a lack of evaluation and inconsistent descriptions in the literature. We aim to support those developing genomics education to consider how best to capture evaluation data that demonstrate program outcomes and effectiveness within scope. Here, we present an evaluation framework that is adaptable to multiple settings for use by genomics educators with or without education or evaluation backgrounds. The framework was developed as part of a broader program supporting genomic research translation coordinated by the Australian Genomics consortium. We detail our mixed-methods approach involving an expert workshop, literature review and iterative expert input to reach consensus and synthesis of a new evaluation framework for genomics education. The resulting theory-informed and evidence-based framework encompasses evaluation across all stages of education program development, implementation and reporting, and acknowledges the critical role of stakeholders and the effects of external influences.

Ensuring best practice in genomics education: A scoping review of genomics education needs assessments and evaluations.

A health workforce capable of implementing genomic medicine requires effective genomics education. Genomics education interventions developed for health professions over the last two decades, and their impact, are variably described in the literature. To inform an evaluation framework for genomics education, we undertook an exploratory scoping review of published needs assessments for, and/or evaluations of, genomics education interventions for health professionals from 2000 to 2023. We retrieved and screened 4,659 records across the two searches with 363 being selected for full-text review and consideration by an interdisciplinary working group. 104 articles were selected for inclusion in the review-60 needs assessments, 52 genomics education evaluations, and eight describing both. Included articles spanned all years and described education interventions in over 30 countries. Target audiences included medical specialists, nurses/midwives, and/or allied health professionals. Evaluation questions, outcomes, and measures were extracted, categorized, and tabulated to iteratively compare measures across stages of genomics education evaluation: planning (pre-implementation), development and delivery (implementation), and impact (immediate, intermediate, or long-term outcomes). They are presented here along with descriptions of study designs. We document the wide variability in evaluation approaches and terminology used to define measures and note that few articles considered downstream (long-term) outcomes of genomics education interventions. Alongside the evaluation framework for genomics education, results from this scoping review form part of a toolkit to help educators to undertake rigorous genomics evaluation that is fit for purpose and can contribute to the growing evidence base of the contribution of genomics education in implementation strategies for genomic medicine.

Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning.

Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (ß coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.

The European Union Emissions Trading System might yield large co-benefits from pollution reduction.

Mitigating greenhouse gas emissions and reducing air pollution represent two pressing and interwoven environmental challenges. While international carbon markets, such as the European Union emissions trading system (EU ETS), have demonstrated their effectiveness in curbing carbon emissions (CO[Formula: see text]), their indirect impact on hazardous co-pollutants remains understudied. This study investigates how key toxic air pollutants-sulfur dioxide (SO[Formula: see text]), fine particulate matter (PM[Formula: see text]), and nitrogen oxides (NO[Formula: see text])-evolved after the introduction of the EU ETS with a comparative analysis of regulated and unregulated sectors. Leveraging the generalized synthetic control method, we offer an ex post analysis of how the EU ETS and concurrent emission standards may have jointly generated sizable pollution reductions in regulated sectors between 2005 and 2021. We provide an aggregate assessment that these pollution reductions could translate into large health co-benefits, potentially in the hundreds of billions of Euros, even when bounding the effect of emission standards. These order-of-magnitude estimates underscore key implications for policy appraisal and motivate further microlevel research around the health co-benefits of carbon abatement.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

Chromosome structure modeling tools and their evaluation in bacteria.

The three-dimensional (3D) structure of bacterial chromosomes is crucial for understanding chromosome function. With the growing availability of high-throughput chromosome conformation capture (3C/Hi-C) data, the 3D structure reconstruction algorithms have become powerful tools to study bacterial chromosome structure and function. It is highly desired to have a recommendation on the chromosome structure reconstruction tools to facilitate the prokaryotic 3D genomics. In this work, we review existing chromosome 3D structure reconstruction algorithms and classify them based on their underlying computational models into two categories: constraint-based modeling and thermodynamics-based modeling. We briefly compare these algorithms utilizing 3C/Hi-C datasets and fluorescence microscopy data obtained from Escherichia coli and Caulobacter crescentus, as well as simulated datasets. We discuss current challenges in the 3D reconstruction algorithms for bacterial chromosomes, primarily focusing on software usability. Finally, we briefly prospect future research directions for bacterial chromosome structure reconstruction algorithms.

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality.

RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

Biclustering data analysis: a comprehensive survey.

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Protein Dynamics in Complex DNA Lesions.

A single mutagen can generate multiple different types of DNA lesions. How different repair pathways cooperate in complex DNA lesions, however, remains largely unclear. Here we measured, clustered, and modeled the kinetics of recruitment and dissociation of 70 DNA repair proteins to laser-induced DNA damage sites in HeLa cells. The precise timescale of protein recruitment reveals that error-prone translesion polymerases are considerably delayed compared to error-free polymerases. We show that this is ensured by the delayed recruitment of RAD18 to double-strand break sites. The time benefit of error-free polymerases disappears when PARP inhibition significantly delays PCNA recruitment. Moreover, removal of PCNA from complex DNA damage sites correlates with RPA loading during 5'-DNA end resection. Our systematic study of the dynamics of DNA repair proteins in complex DNA lesions reveals the multifaceted coordination between the repair pathways and provides a kinetics-based resource to study genomic instability and anticancer drug impact.

Climate and health benefits of a transition from gas to electric cooking.

Household electrification is thought to be an important part of a carbon-neutral future and could also have additional benefits to adopting households such as improved air quality. However, the effectiveness of specific electrification policies in reducing total emissions and boosting household livelihoods remains a crucial open question in both developed and developing countries. We investigated a transition of more than 750,000 households from gas to electric cookstoves-one of the most popular residential electrification strategies-in Ecuador following a program that promoted induction stoves and assessed its impacts on electricity consumption, greenhouse gas emissions, and health. We estimate that the program resulted in a 5% increase in total residential electricity consumption between 2015 and 2021. By offsetting a commensurate amount of cooking gas combustion, we find that the program likely reduced national greenhouse gas emissions, thanks in part to the country's electricity grid being 80% hydropower in later parts of the time period. Increased induction stove uptake was also associated with declines in all-cause and respiratory-related hospitalizations nationwide. These findings suggest that, when the electricity grid is largely powered by renewables, gas-to-induction cooking transitions represent a promising way of amplifying the health and climate cobenefits of net-carbon-zero policies.

Social feedback biases emerge during recall but not prediction and shift across the development of social anxiety.

Memory is a reconstructive process that can result in events being recalled as more positive or negative than they actually were. While positive recall biases may contribute to well-being, negative recall biases may promote internalizing symptoms, such as social anxiety. Adolescence is characterized by increased salience of peers and peak incidence of social anxiety. Symptoms often wax and wane before becoming more intractable during adulthood. Open questions remain regarding how and when biases for social feedback are expressed and how individual differences in biases may contribute to social anxiety across development. Two studies used a social feedback and cued response task to assess biases about being liked or disliked when retrieving memories vs. making predictions. Findings revealed a robust positivity bias about memories for social feedback, regardless of whether memories were true or false. Moreover, memory bias was associated with social anxiety in a developmentally sensitive way. Among adults (study 1), more severe symptoms of social anxiety were associated with a negativity bias. During the transition from adolescence to adulthood (study 2), age strengthened the positivity bias in those with less severe symptoms and strengthened the negativity bias in those with more severe symptoms. These patterns of bias were isolated to perceived memory retrieval and did not generalize to predictions about social feedback. These results provide initial support for a model by which schemas may infiltrate perceptions of memory for past, but not predictions of future, social events, shaping susceptibility for social anxiety, particularly during the transition into adulthood.

Neural Representations of Post-Decision Accuracy and Reward Expectation in the Caudate Nucleus and Frontal Eye Field.

Performance monitoring that supports ongoing behavioral adjustments is often examined in the context of either choice confidence for perceptual decisions (i.e., "did I get it right?") or reward expectation for reward-based decisions (i.e., "what reward will I receive?"). However, our understanding of how the brain encodes these distinct evaluative signals remains limited because they are easily conflated, particularly in commonly used two-alternative tasks with symmetric rewards for correct choices. Previously we used a motion-discrimination task with asymmetric rewards to identify neural substrates of forming reward-biased perceptual decisions in the caudate nucleus (part of the striatum in the basal ganglia) and the frontal eye field (FEF, in prefrontal cortex). Here we leveraged this task design to partially decouple estimates of accuracy and reward expectation and examine their impacts on subsequent decisions and their representations in those two brain areas. We identified distinguishable representations of these two evaluative signals in individual caudate and FEF neurons, with regional differences in their distribution patterns and time courses. We observed that well-trained monkeys (both sexes) used both evaluative signals, infrequently but consistently, to adjust their subsequent decisions. We found further that these behavioral adjustments had reliable relationships with the neural representations of both evaluative signals in caudate, but not FEF. These results suggest that the cortico-striatal decision network may use diverse evaluative signals to monitor and adjust decision-making behaviors, adding to our understanding of the different roles that the FEF and caudate nucleus play in a diversity of decision-related computations.

A cross-species approach using an in vivo evaluation platform in mice demonstrates that sequence variation in human RABEP2 modulates ischemic stroke outcomes.

Ischemic stroke, caused by vessel blockage, results in cerebral infarction, the death of brain tissue. Previously, quantitative trait locus (QTL) mapping of cerebral infarct volume and collateral vessel number identified a single, strong genetic locus regulating both phenotypes. Additional studies identified RAB GTPase-binding effector protein 2 (Rabep2) as the casual gene. However, there is yet no evidence that variation in the human ortholog of this gene plays any role in ischemic stroke outcomes. We established an in vivo evaluation platform in mice by using adeno-associated virus (AAV) gene replacement and verified that both mouse and human RABEP2 rescue the mouse Rabep2 knockout ischemic stroke volume and collateral vessel phenotypes. Importantly, this cross-species complementation enabled us to experimentally investigate the functional effects of coding sequence variation in human RABEP2. We chose four coding variants from the human population that are predicted by multiple in silico algorithms to be damaging to RABEP2 function. In vitro and in vivo analyses verify that all four led to decreased collateral vessel connections and increased infarct volume. Thus, there are naturally occurring loss-of-function alleles. This cross-species approach will expand the number of targets for therapeutics development for ischemic stroke.

Assessing protein model quality based on deep graph coupled networks using protein language model.

Model quality evaluation is a crucial part of protein structural biology. How to distinguish high-quality models from low-quality models, and to assess which high-quality models have relatively incorrect regions for improvement, are remain a challenge. More importantly, the quality assessment of multimer models is a hot topic for structure prediction. In this study, we propose GraphCPLMQA, a novel approach for evaluating residue-level model quality that combines graph coupled networks and embeddings from protein language models. The GraphCPLMQA consists of a graph encoding module and a transform-based convolutional decoding module. In encoding module, the underlying relational representations of sequence and high-dimensional geometry structure are extracted by protein language models with Evolutionary Scale Modeling. In decoding module, the mapping connection between structure and quality is inferred by the representations and low-dimensional features. Specifically, the triangular location and residue level contact order features are designed to enhance the association between the local structure and the overall topology. Experimental results demonstrate that GraphCPLMQA using single-sequence embedding achieves the best performance compared with the CASP15 residue-level interface evaluation methods among 9108 models in the local residue interface test set of CASP15 multimers. In CAMEO blind test (20 May 2022 to 13 August 2022), GraphCPLMQA ranked first compared with other servers (https://www.cameo3d.org/quality-estimation). GraphCPLMQA also outperforms state-of-the-art methods on 19, 035 models in CASP13 and CASP14 monomer test set.

A review of methods for predicting DNA N6-methyladenine sites.

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

Evaluation of sequence-based predictors for phase-separating protein.

Liquid-liquid phase separation (LLPS) of proteins and nucleic acids underlies the formation of biomolecular condensates in cell. Dysregulation of protein LLPS is closely implicated in a range of intractable diseases. A variety of tools for predicting phase-separating proteins (PSPs) have been developed with the increasing experimental data accumulated and several related databases released. Comparing their performance directly can be challenging due to they were built on different algorithms and datasets. In this study, we evaluate eleven available PSPs predictors using negative testing datasets, including folded proteins, the human proteome, and non-PSPs under near physiological conditions, based on our recently updated LLPSDB v2.0 database. Our results show that the new generation predictors FuzDrop, DeePhase and PSPredictor perform better on folded proteins as a negative test set, while LLPhyScore outperforms other tools on the human proteome. However, none of the predictors could accurately identify experimentally verified non-PSPs. Furthermore, the correlation between predicted scores and experimentally measured saturation concentrations of protein A1-LCD and its mutants suggests that, these predictors could not consistently predict the protein LLPS propensity rationally. Further investigation with more diverse sequences for training, as well as considering features such as refined sequence pattern characterization that comprehensively reflects molecular physiochemical interactions, may improve the performance of PSPs prediction.

Label-free proteome quantification and evaluation.

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.