Explore the vast range of titles available.

Understanding the impact of rare variants is essential to understanding human health. We analyze rare (MAF < 0.1%) variants against 4264 phenotypes in 49,960 exome-sequenced individuals from the UK Biobank and 1934 phenotypes (1821 overlapping with UK Biobank) in 21,866 members of the Healthy Nevada Project (HNP) cohort who underwent Exome + sequencing at Helix. After using our rare-variant-tailored methodology to reduce test statistic inflation, we identify 64 statistically significant gene-based associations in our meta-analysis of the two cohorts and 37 for phenotypes available in only one cohort. Singletons make significant contributions to our results, and the vast majority of the associations could not have been identified with a genotyping chip. Our results are available for interactive browsing in a webapp ( https://ukb.research.helix.com ). This comprehensive analysis illustrates the biological value of large, deeply phenotyped cohorts of unselected populations coupled with NGS data. Population-based association analyses of rare genetic variants with complex traits are limited by the availability of data from sufficiently large cohorts. Here, Cirulli et al. report gene-based collapsing analysis of exomes from 49,960 participants of the UK Biobank and 21,866 participants of the Healthy Nevada Project over a total of 4377 traits.

Newly cost-effective next-generation sequencing has led to an explosion of discoveries of novel genetic mutations that reveal the rampant “promiscuity” of genotype–phenotype relationships. Such discoveries should ultimately revolutionize clinical care. Ever since Mendel observed the varied phenotypes of peas — green or yellow, smooth or wrinkled — phenotypes have been used to systematically identify the genetic causes of disease. Similarly, genotype–phenotype relationships in humans could be dissected only if there were clearly recognizable, and relatively homogeneous, phenotypes. Since broad searches of genetic information were not technically feasible or cost-effective before the advent of next-generation sequencing (NGS), scientists studied well-characterized families to narrow the list of plausible genetic causes. However, being restricted to this set of “solvable” genetic problems led to ascertainment biases that favored highly penetrant mutations with straightforward functional . . .

While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law that possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients’ overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.

This report identifies human skeletal diseases associated with mutations in WNT1 in a family with dominantly inherited early-onset osteoporosis and in another family with recessive osteogenesis imperfecta. WNT1 is shown to be an important ligand for regulating bone mass. Osteoporosis is a common skeletal disorder characterized by low bone mineral density (BMD), impaired bone quality, and fragility fractures. 1 Although multiple genetic loci, including those for WNT ligands, have been defined on the basis of genomewide association studies in patients with osteoporosis, the known loci are generally associated with odds ratios for fracture that are below 1.1. 2 Recently, novel metabolic pathways in bone cells have been discovered in patients with osteogenesis imperfecta, a mendelian disease characterized by brittle bones. 3 The role of the WNT pathway in bone formation and maintenance has been extensively studied since the identification of mutations in . . .

Background Although the frequency and magnitude of climate change-related health hazards (CCRHHs) are likely to increase, the population vulnerabilities and corresponding health impacts are dependent on a community’s exposures , pre-existing sensitivities, and adaptive capacities in response to a hazard’s impact. To evaluate spatial variability in relative vulnerability, we: 1) identified climate change-related risk factors at the dissemination area level; 2) created actionable health vulnerability index scores to map community risks to extreme heat, flooding, wildfire smoke, and ground-level ozone; and 3) spatially evaluated vulnerability patterns and priority areas of action to address inequity. Methods A systematic literature review was conducted to identify the determinants of health hazards among populations impacted by CCRHHs. Identified determinants were then grouped into categories of exposure, sensitivity, and adaptive capacity and aligned with available data. Data were aggregated to 4188 Census dissemination areas within two health authorities in British Columbia, Canada. A two-step principal component analysis (PCA) was then used to select and weight variables for each relative vulnerability score. In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard. Scores were then categorised into quintiles and mapped. Results Two hundred eighty-one epidemiological papers met the study criteria and were used to identify 36 determinant indicators that were operationalized across all hazards. For each hazard, 3 to 5 principal components explaining 72 to 94% of the total variance were retained. Sensitivity was weighted much higher for extreme heat, wildfire smoke and ground-level ozone, and adaptive capacity was highly weighted for flooding vulnerability. There was overall varied contribution of adaptive capacity (16–49%) across all hazards. Distinct spatial patterns were observed – for example, although patterns varied by hazard, vulnerability was generally higher in more deprived and more outlying neighbourhoods of the study region. Conclusions The creation of hazard and category-specific vulnerability indices (exposure, adaptive capacity and sensitivity sub-scores) supports evidence-based approaches to prioritize public health responses to climate-related hazards and to reduce inequity by assessing relative differences in vulnerability along with absolute impacts. Future studies can build upon this methodology to further understand the spatial variation in vulnerability and to identify and prioritise actionable areas for adaptation.

Shohat-type spondyloepimetaphyseal dysplasia (SEMD) is a skeletal dysplasia that affects cartilage development. Similar skeletal disorders, such as spondyloepiphyseal dysplasias, are linked to mutations in type II collagen (COL2A1), but the causative gene in SEMD is not known. Here, we have performed whole-exome sequencing to identify a recurrent homozygous c.408+1G>A donor splice site loss-of-function mutation in DDRGK domain containing 1 (DDRGK1) in 4 families affected by SEMD. In zebrafish, ddrgk1 deficiency disrupted craniofacial cartilage development and led to decreased levels of the chondrogenic master transcription factor sox9 and its downstream target, col2a1. Overexpression of sox9 rescued the zebrafish chondrogenic and craniofacial phenotype generated by ddrgk1 knockdown, thus identifying DDRGK1 as a regulator of SOX9. Consistent with these results, Ddrgk1-/- mice displayed delayed limb bud chondrogenic condensation, decreased SOX9 protein expression and Col2a1 transcript levels, and increased apoptosis. Furthermore, we determined that DDRGK1 can directly bind to SOX9 to inhibit its ubiquitination and proteasomal degradation. Taken together, these data indicate that loss of DDRGK1 decreases SOX9 expression and causes a human skeletal dysplasia, identifying a mechanism that regulates chondrogenesis via modulation of SOX9 ubiquitination.

The focus behind this book on wafer bonding is the fast paced changes in the research and development in three-dimensional (3D) integration, temporary bonding and micro-electro-mechanical systems (MEMS) with new functional layers.

Although there is increased interest in utilizing machine learning (ML) to support drug development, technical hurdles associated with complex algorithms have limited widespread adoption. In response, we have developed Pharm‐AutoML, an open‐source Python package that enables users to automate the construction of ML models and predict clinical outcomes, especially in the context of pharmacological interventions. In particular, our approach streamlines tedious steps within the ML workflow, including data preprocessing, model tuning, model selection, results analysis, and model interpretation. Moreover, our open‐source package helps to identify the most predictive ML pipeline among defined search spaces by selecting the best data preprocessing strategy and tuning the ML model hyperparameters. The package currently supports multiclass classification tasks, and additional functions are being developed. Using a set of five publicly available biomedical datasets, we show that our Pharm‐AutoML outperforms other ML frameworks, including H2O with default settings, by demonstrating improved predictive accuracy of classification. We further illustrate how model interpretation methods can be utilized to help explain the fine‐tuned ML pipeline to end users. Pharm‐AutoML provides both novice and expert users improved clinical predictions and scientific insights.

The increase in the availability of real‐world data (RWD), in combination with advances in machine learning (ML) methods, provides a unique opportunity for the integration of the two to explore complex clinical pharmacology questions. Here we present a recently developed RWD/ML framework that utilizes ML algorithms to understand the influence and importance of various covariates on the use of a given dose and schedule for drugs that have multiple approved dosing regimens. To demonstrate the application of this framework, we present atezolizumab as a use case on account of its three approved alternative intravenous (IV) dosing regimens. As expected, the real‐world use of atezolizumab has generally been increasing since 2016 for the 1200 mg every 3 weeks regimen and since 2019 for the 1680 mg every 4 weeks regimen. Out of the ML algorithms evaluated, XGBoost performed the best, as measured by the area under the precision–recall curve, with an emphasis on the under‐sampled class given the imbalance in the data. The importance of features was measured by Shapley Additive exPlanations (SHAP) values and showed metastatic breast cancer and use of protein‐bound paclitaxel as the most correlated with the use of 840 mg every 2 weeks. Although patient usage data for alternative IV dosing regimens are still maturing, these analyses provide initial insights on the use of atezolizumab and set up a framework for the re‐analysis of atezolizumab (at a future data cut) as well as application to other molecules with approved alternative dosing regimens.

The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments. The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. This Perspective discusses the development and use of ontologies that are designed to add semantic information to computational models and simulations.