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Ischemic heart disease remains the leading cause of death worldwide. Ischemic pre-, post-, and remote conditionings trigger endogenous cardioprotection that renders the heart resistant to ischemic-reperfusion injury (IRI). Mimicking endogenous cardioprotection by modulating genes involved in cardioprotective signal transduction provides an opportunity to reproduce endogenous cardioprotection with better possibilities of translation into the clinical setting. Genes and signaling pathways by which conditioning maneuvers exert their effects on the heart are partially understood. This is due to the targeted approach that allowed identifying one or a few genes associated with IRI and cardioprotection. Genes critical for signaling pathways in cardioprotection include protectomiRs (e.g., microRNA 125b*), ZAC1 transcription factor, pro-inflammatory genes such as cycloxygenase (COX)-2 and inducible nitric oxide synthase (iNOS), antioxidant enzymes such as hemoxygenase (HO)-1, extracellular and manganese superoxidase dismutases (ec-SOD and Mg-SOD), heat shock proteins (HSPs), growth factors such as insulin like growth factor (IGF)-1 and hepatocyte growth factor (HGF), antiapoptotic proteins such as Bcl-2 and Bcl-xL, pro-apoptotic proteins such as FasL, Bcl-2, Bax, caspase-3 and p53, and proangiogenic genes such as TGFbeta, sphingosine kinase 1 (SPK1), and PI3K-Akt. By identifying the gene expression profiles of IRI and ischemic conditioning, one may reveal potential gene targets responsible for cardioprotection. In this manuscript, we review the current state of the art of gene therapy in cardioprotection and propose that gene expression analysis facilitates the identification of individual genes associated with cardioprotection. We discuss signaling pathways associated with cardioprotection that can be targeted by gene therapy to achieve cardioprotection. [ABSTRACT FROM AUTHOR]

Abstract: Recent changes in the European legislation of chemical-related substances have forced the scientific community to speed up the search for alternative methods that could partly or fully replace animal experimentation. The Sixth Framework Program project carcinoGENOMICS was specifically raised to develop omics-based in vitro screens for testing the carcinogenic potential of chemical compounds in a pan-European context. This paper provides an in-depth analysis of the complexity of choosing suitable reference compounds used for creating and fine-tuning the in vitro carcinogenicity assays. First, a number of solid criteria for the selection of the model compounds are defined. Secondly, the strategy followed, including resources consulted, is described and the selected compounds are briefly illustrated. Finally, limitations and problems encountered during the selection procedure are discussed. Since selecting an appropriate set of chemicals is a frequent impediment in the early stages of similar research projects, the information provided in this paper might be extremely valuable. [Copyright &y& Elsevier]

Patient factors affect the risk of radiotherapy toxicity, but many are poorly defined. Studies have shown that race affects cancer incidence, survival, drug response, molecular pathways and epigenetics. Effects on radiosensitivity and radiotherapy toxicity are not well studied. The aim of the present study was to identify the effects of race and ethnicity on the risk of radiotherapy toxicity. A systematic review was carried out of PubMed, Ovid Medline and Ovid Embase with no year limit. PRISMA 2020 guidelines were followed. Two independent assessors reviewed papers. Of 607 papers screened, 46 fulfilled the inclusion criteria. Papers were published between 1996 and 2021 and involved 30–28,354 individuals (median 433). Most involved patients with prostate (33%), breast (26%) and lung (9%) cancer. Both early and late toxicities were studied. Some studies reported a higher risk of toxicity in White men with prostate cancer compared with other races and ethnicities. For breast cancer patients, some reported an increased risk of toxicity in White women compared with other race and ethnic groups. In general, it was difficult to draw conclusions due to insufficient reporting and analysis of race and ethnicity in published literature. Reporting of race and ethnicity in radiotherapy studies must be harmonised and improved and frameworks are needed to improve the quality of reporting. Further research is needed to understand how ancestral heritage might affect radiosensitivity and risk of radiotherapy toxicity. • Ancestral heritage is likely to affect risk of some radiation toxicities. • Insufficient evidence from reviewing effects of race and ethnicity on toxicity. • Reporting race and ethnicity in radiation studies must be harmonised and improved. • Socioeconomic and comorbidity effects must be addressed. • Radiogenomic research needs diversifying beyond euro-centricity. [ABSTRACT FROM AUTHOR]

• Innovative phenotype-genotype association analysis for categorical genomic data having non-ignorable missing values. • Statistical learning method of random forest is used for variable selection in the analysis. • Weighted logistic ridge regression with EM algorithm is used for missing data imputation in the analysis. • Linear statistical hypothesis testing is used for determining the missingness mechanism in the analysis. • An application to analyzing real data from Australia breast cancer genome-wide association study (GWAS). Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing values with non-ignorable missingness mechanism. The intrinsic complications of genomic data present significant challenges in developing an effective and efficient procedure of phenotype-genotype association analysis by a statistical variable selection approach. In this paper we develop a coherent procedure of categorical phenotype-genotype association analysis, in the presence of missing values with non-ignorable missingness mechanism in genomic data. It is developed by integrating the statistical learning methods of random forest for variable selection, joint weighted logistic ridge regression with EM algorithm for missing data imputation, and linear statistical hypothesis testing for determining the missingness mechanism. Two simulated genomic datasets are used to undertake the phenotype-genotype association analysis by the proposed procedure, with the performance validated. The proposed procedure is then applied to analyze a real data set from breast cancer GWAS. [ABSTRACT FROM AUTHOR]

Statistical techniques incorporated with machine-learning algorithms in unison with gene-environment interaction are giving unparalleled understanding of complex diseases. Accurate analysis and intricate capturing of common, rare, and low MAF (Minor Allele Frequency) variants alongside gene-environmental interaction is pivotal whilst concluding reliable and accurate classification of complex diseases. Various complex diseases including genres of diabetes Type 1 and Type 2 alongside the vastly under-researched Lada (Latent Autoimmune Diabetes in Adults) diabetes require further investigation alongside significant machine learning research to gain a deeper understanding of the disease complexities. Despite existing efforts, an ideal combination of statistical techniques with optimal machine-learning algorithms that can accurately capture and model the gene-environment interaction is lacking. Intentionally exploring future and simultaneously exploiting modern-day computational methods in genomic analysis, this paper profoundly investigates both the future and present interaction of statistical analysis techniques and machine-learning algorithms and Ensembles with gene-environmental factors. In this context, this paper firstly presents a conceptual understanding of genomic conventions; secondly, conducts potential future machine learning algorithms alongside an extensive analysis of a range of classification, regression and Ensemble techniques along with exhibiting their imperative relationship and roles in investigating and classifying common, rare variants and a wide array of gene-environmental factors; and thirdly, utilisation of statistical techniques in Genome Wide Association Studies is scrutinised whilst analysing common, rare and MAF variants. As an important contribution, this paper identifies efficient machine-learning algorithms alongside Ensemble models and future potential analysis techniques and exhibits their inherent characteristics that can enhance the reliability and accuracy of the gene-environment classification analysis. [ABSTRACT FROM AUTHOR]

Sporadic colorectal cancer has traditionally been viewed as a malignancy of older individuals. However, as the global prevalence of the disease diagnosed in younger individuals (<50 years) is expected to increase within the next decade, greater recognition is now being given to early-onset colorectal cancer. The cause of the predicted rise in prevalence is largely unknown and probably multifactorial. In this Series paper, we discuss the potential underlying causes of early-onset colorectal cancer, the role of energy balance, biological and genomic mechanisms (including microbiome aspects), and the treatment of early-onset colorectal cancer. We have specifically considered the psychosocial challenges of being diagnosed with colorectal cancer at younger age and the potential financial toxicity that might ensue. This Series paper brings a comprehensive review based on the existing data in the hopes of optimising the overall outcomes for patients with early-onset colorectal cancer. [ABSTRACT FROM AUTHOR]

Salinity is one of the major threats to sustainable agriculture that globally decreases plant production by impairing various physiological, biochemical, and molecular function. In particular, salinity hampers germination, growth, photosynthesis, transpiration, and stomatal conductance. Salinity decreases leaf water potential and turgor pressure and generates osmotic stress. Salinity enhances reactive oxygen species (ROS) content in the plant cell as a result of ion toxicity and disturbs ion homeostasis. Thus, it imbalances nutrient uptake, disintegrates membrane, and various ultrastructure. Consequently, salinity leads to osmotic and ionic stress. Plants respond to salinity by modulating various morpho-physiological, anatomical, and biochemical traits by regulating ion homeostasis and compartmentalization, antioxidant machinery, and biosynthesis of osmoprotectants and phytohormones, i. e, auxins, abscisic acid, brassinosteroids, cytokinins, ethylene, gibberellins, salicylic acid, jasmonic acid, and polyamines. Thus, this further modulates plant osmoticum, decreases ion toxicity, and scavenges ROS. Plants upregulate various genes and proteins that participate in salinity tolerance. They also promote the production of various phytohormones and metabolites that mitigate the toxic effect of salinity. Based on recent papers, the deleterious effect of salinity on plant physiology is discussed. Furthermore, it evaluates the physiological and biochemical responses of the plant to salinity along with phytohormone response. This review paper also highlights omics (genomics, transcriptomics, proteomics, and metabolomics) approach to understand salt stress tolerance. • Salt stress creates ionic and osmotic stress on plants. • Salinity harms morphological and biochemical functions in plants. • Plants respond to salinity by various strategies involve the complex physiological traits, and metabolic pathways. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers. This paper presents an innovative multi-model XAI and a probabilistic causal inference framework to identify and validate key genetic biomarkers for HCC prognosis. Our methodology involved analyzing clinical and gene expression data to identify potential biomarkers with prognostic significance. The study utilized robust AI models validated against extensive gene expression datasets, demonstrating not only the predictive accuracy but also the clinical relevance of the identified biomarkers through explainable metrics. The findings highlight the importance of biomarkers such as TOP3B, SSBP3, and COX7A2L, which were consistently influential across multiple models, suggesting their role in improving the predictive accuracy for HCC prognosis beyond AFP. Notably, the study also emphasizes the relevance of these biomarkers to the Hispanic population, aligning with the larger goal of demographic-specific research. The application of XAI in biomarker discovery represents a significant advancement in HCC research, offering a more nuanced understanding of the disease and laying the groundwork for improved diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Genomics has received significant attention in recent years. Nurses can uniquely contribute to the field of genomics and improve genomic healthcare. However, they lack the necessary knowledge to accomplish this, impacting their confidence, willingness, and ability to implement genomics healthcare negatively. Given Oman's unique healthcare context, its prospective genomics application, and the global trend toward genomic competency, it is essential to gauge nurses' genomic knowledge as basis for equipping them. This study assessed the genomic knowledge among nurses in the Sultanate of Oman. It also explored the predictors and outcomes of their genomic knowledge. This study used a quantitative, descriptive, cross-sectional design. Nurses from four public hospitals in Muscat Governorate, Sultanate of Oman were recruited to participate in the study. A total of 661 out of 700 nurses responded to the pen and paper self-report questionnaire (94 % response rate). Univariate, bivariate, and regression analyses were used for data analysis. Consistent with international studies, nurses in Oman had low to moderate genomic knowledge. Years of experience in genetics healthcare and working in either the surgical and maternity units were positively associated with higher genomic knowledge. Working in the bone marrow transplant unit and having a family history of consanguinity were associated with poorer genomic knowledge. Higher genomic knowledge was associated with an increased willingness to care for patients with genetic issues, higher confidence in providing genomic healthcare, and a lower intention to pursue continuing education on genomics. Strategies targeting variables, particularly those that are amenable to interventions, should be developed and implemented to foster genomic knowledge in nurses. • Factors associated with nurses' genomic knowledge include years of experience in genetics healthcare, unit of assignment, and family history of consanguinity. • Nurses with higher genomic knowledge are more likely to demonstrate willingness and confidence in providing genomic healthcare. • Nurses with higher genomic knowledge tend to have lower intention to pursue continuing education on genomics. [ABSTRACT FROM AUTHOR]

Notions of naturalness are widely assumed to drive how people perceive genetic engineering (GE). As newer forms of genetic engineering—namely, gene editing, gene drives, and synthetic biology—are reshaping life forms in both agriculture and conservation, they are increasingly raising questions of what a 'natural' food, organism or ecosystem is, and whether objections toward 'unnaturalness' or preferences for 'naturalness' might reveal a deeper ethical or value-based logic. A number of fields have sought, both directly and indirectly, to define the concept, but insights have not yet been applied to new forms of genetic engineering. This paper proposes that systematically reviewing scholarly interpretations of 'naturalness' might offer weight to a concept that is often dismissed as irrational. Here, we review and synthesize insights from a range of fields, outlining possible logics public groups might employ to reason about what is '(un)natural'. We also offer a novel thought experiment in which we apply these logics to a sample of novel GE applications. One of our core findings is that '(un)naturalness' may be understood not necessarily as a quality of an object, but rather as a characteristic of ecological, social, cultural, and spiritual relationships. Such an understanding implies the need for ongoing engagement with the values embedded in ideas of naturalness and empirical explorations of how such values inform debates on novel engineered foods, organisms and ecosystems. • New forms of genetic engineering (GE) in agriculture and conservation are raising questions of 'naturalness'. • 'Naturalness' is often assumed to be an irrational reason for objecting to particular applications of GE. • Fields from natural sciences to humanities offer varied definitions of and insights into meanings behind 'naturalness'. • Relational understandings of naturalness offer crucial insights for design of future GE applications. [ABSTRACT FROM AUTHOR]

The outbreak of African Swine Fever (ASF) in 2018 has had a massive impact on the Chinese hog industry. Finding treatments for the disease has attracted scientists worldwide working on a solution. Using genomics technology could potentially prevent hogs from getting infected with ASF. Many people, however, hold negative attitudes towards genomics technology, even though the Chinese government supports the technology. This paper attempts to determine hog farmers' motivations to adopt genomics for breeding hogs that are more resistant to the disease. In doing so we focus on the impact of their risk preferences and related peer effects that might influence potential adoption. We present a case study using face to face interviews with hog farmers from two locations in China. Results indicate that hog farmers would likely purchase semen produced with genomics technology if the semen was ASF resistant, reducing the probability of ASF by at least 60%. Most hog farmers we surveyed were risk averse. Findings suggest that more risk-averse hog farmers are more likely to delay the adoption of ASF resistant semen as compared to more risk-prone farmers. Results from social network analysis indicate hog farmers' social network status, such as, centrality, does not affect the time frame in which they would adopt genomics technology. However, the genomics adoption time frame of a particular hog farmer is positively correlated with other closely related hog farmers' time frames. This study also finds hog farmers form networks with other farmers similar to them, not only do they share a similar attitude in adopting genomics technology but they also have similar risk preferences. Overall, this case study provides implications for local governments and companies trying to promote new technologies. • Analyzed hog farmers' motivations to adopt genomics. • Tested impact of risk preferences and peer effects. • Risk-averse hog farmers are more likely to delay adoption of genomics. • Hog farmers closer to others are more influential in disseminating information. • Hog farmers form networks with others who are similar to them. [ABSTRACT FROM AUTHOR]

[Display omitted] • DL algorithms unveil hidden patterns in genomics and biomedicine to empower IHS. • DL optimization enables intricate analysis of molecular-level data in biomedicine. • Open-source DL frameworks democratize access to cutting-edge tools for researchers. • Integration of DL in IHS allows for real-time monitoring and prediction of disease progression for proactive interventions. • DL-powered decision support systems aid clinicians in treatment planning and predicting patient response. The advancements in genomics and biomedical technologies have generated vast amounts of biological and physiological data, which present opportunities for understanding human health. Deep learning (DL) and machine learning (ML) are frontiers and interdisciplinary fields of computer science that consider comprehensive computational models and provide integral roles for disease diagnosis and therapy investigation. DL-based algorithms can discover the intrinsic hierarchies in the training data to show great promise for extracting features and learning patterns from complex datasets and performing various analytical tasks. This review comprehensively discusses the wide-ranging DL approaches for intelligent healthcare systems (IHS) in genomics and biomedicine. This paper explores advanced concepts in deep learning (DL) and discusses the workflow of utilizing role-based algorithms in genomics and biomedicine to integrate intelligent healthcare systems (IHS). The aim is to overcome biomedical obstacles like patient disease classification, core biomedical processes, and empowering patient-disease integration. The paper also highlights how DL approaches are well-suited for addressing critical challenges in these domains, offering promising solutions for improved healthcare outcomes. We also provided a concise concept of DL architectures and model optimization in genomics and bioinformatics at the molecular level to deal with biomedicine classification, genomic sequence analysis, protein structure classification, and prediction. Finally, we discussed DL's current challenges and future perspectives in genomics and biomedicine for future directions. [ABSTRACT FROM AUTHOR]

• There is a soaring increase of the N fertilizers prices and the problems caused by excess use of N fertilizers forced the rice breeders to rethink other sustainable, affordable, accessible, and globally acceptable strategies. • Multidisciplinary scientific collaboration promotes innovation that leads to the development of rice varieties that use nitrogen most effectively, facilitate smart technology transfer, and promote the adoption of NUE practices by farmers and stakeholders and minimize environmental impact and contribute to a sustainable agricultural future. • The utilization of recent omics technologies, for instance, genomics and pan-genomics, transcriptomics, proteomics, metabolomics, ionomics, fluxomics and nutrigenomics could provide us an overview and facilitate our deep global understanding on NUE. As rice has no physiological capacity of fixing nitrogen in the soil, its production had always been reliant on the external application of nitrogen (N) to ensure enhanced productivity. In the light of improving nitrogen use efficiency (NUE) in rice, several advanced agronomic strategies have been proposed. However, the soared increase of the prices of N fertilizers and subsequent environmental downfalls caused by the excessive use of N fertilizers, reinforces the prerequisite adaptation of other sustainable, affordable, and globally acceptable strategies. An appropriate alternative approach would be to develop rice cultivars with better NUE. Conventional breeding techniques, however, have had only sporadic success in improving NUE, and hence, this paper proposes a new schema that employs the wholesome benefits of the recent advancements in omics technologies. The suggested approach promotes multidisciplinary research, since such cooperation enables the synthesis of many viewpoints, approaches, and data that result in a comprehensive understanding of NUE in rice. Such collaboration also encourages innovation that leads to developing rice varieties that use nitrogen more effectively, facilitate smart technology transfer, and promotes the adoption of NUE practices by farmers and stakeholders to minimize ecological impact and contribute to a sustainable agricultural future. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine prevalence and perform genomic analysis of Salmonella spp. and Campylobacter spp. isolated from different stages of an integrated NAE broiler complex. Environmental samples were screened with 3M-Molecular Detection System (MDS) and MDS positive samples were further processed for confirmation of results and identification. Core genome-based phylogenies were built for both bacteria isolated from this study along with selected NCBI genomes. The odds ratios and 95% confidence limits were compared among stages and sample types (α < 0.05) using multivariable model. Based on MDS results, 4% and 18% of total samples were positive for Salmonella spp. and Campylobacter spp. respectively. The odds of Salmonella detection in hatchery samples were 2.58 times as likely as compared to its detection in production farms' samples (P = 0.151) while the odds of Campylobacter detection in production farms' samples were 32.19 times as likely as its detection in hatchery (P = 0.0015). Similarly, the odds of Campylobacter detection in boot swabs, soil, water, and miscellaneous samples were statistically significant (P < 0.05) as compared with fly paper as reference group. The serovars identified for Salmonella were Typhimurium, Barranquilla, Liverpool, Kentucky, Enteritidis, Luciana, and Rough_O:r:1,5. For Campylobacter, the species identified were Campylobacter jejuni and Campylobacter coli. Phylogeny results show close genetic relatedness among bacterial strains isolated from different locations within the same stage and between different stages. The results show possibility of multiple entry points of such bacteria entering broiler complex and can potentially contaminate the final raw product in the processing plant. It suggests the need for a comprehensive control strategy with strict biosecurity measures and best management practices to minimize or eliminate such pathogens from the poultry food chain. [ABSTRACT FROM AUTHOR]

Connectivity is frequently cited as a vital component of Marine Protected Area (MPA) networks and was formally identified as one of five key principles for marine network design in European waters. Yet, without the ability to demonstrate connectivity, it is impossible to be certain that sites designated within a MPA network do in fact constitute a network, when they may –irrespective of the diversity and rarity of the taxa within them– be in reality a set of unlinked habitats and associated species assemblages. However, the process of assessing connectivity between MPAs, and which taxa to include in assessments of connectivity, is often difficult and can be dependent on a variety of factors that can be outside the control of managers, stakeholders and policymakers. Among the many methods that have been used to assess connectivity, genetic markers are often used to infer connectivity indirectly by estimating the degree of genetic differentiation between populations of a species or by inferring the origin(s) of migrants using assignment methods. While modern molecular methods can be extremely robust and are now routinely used to address conservation issues, genetic data are, to the authors’ knowledge, rarely used to inform designation of MPA networks. In this paper, several biological and methodological factors are highlighted, consideration of which may help to inform the selection of species for assessments of connectivity between MPAs in a network, and this paper suggests ways in which genetic data may be interpreted to inform MPA design and policy. [ABSTRACT FROM AUTHOR]

The surge in availability of genomic data holds promise for enabling determination of genetic causes of observed individual traits, with applications to problems such as discovery of the genetic roots of phenotypes, be they molecular phenotypes such as gene expression or metabolite concentrations, or complex phenotypes such as diseases. However, the growing sizes of these datasets and the quadratic, cubic or higher scaling characteristics of the relevant algorithms pose a serious computational challenge necessitating use of leadership scale computing. In this paper we describe a new approach to performing vector similarity metrics calculations, suitable for parallel systems equipped with graphics processing units (GPUs) or Intel Xeon Phi processors. Our primary focus is the Proportional Similarity metric applied to Genome Wide Association Studies (GWAS) and Phenome Wide Association Studies (PheWAS). We describe the implementation of the algorithms on accelerated processors, methods used for eliminating redundant calculations due to symmetries, and techniques for efficient mapping of the calculations to many-node parallel systems. Results are presented demonstrating high per-node performance and parallel scalability with rates of more than five quadrillion (5 × 10 15 ) elementwise comparisons achieved per second on the ORNL Titan system. In a companion paper we describe corresponding techniques applied to calculations of the Custom Correlation Coefficient for comparative genomics applications. [ABSTRACT FROM AUTHOR]

• Lactose tolerance and the co-spreading of LCT/MCM6 and the high-risk MS allele, HLA-DRB1*15:01, throughout Europe. • Exposure to dairy-animal pathogen, Clostridium perfringens Epsilon neurotoxin (ETX) • Environmental trigger for multiple sclerosis (MS) On January 10th, 2024, Barrie et al. published a landmark paper identifying the ancestral European origin of the high-risk, multiple sclerosis (MS) allele, HLA-DRB1*15:01. The authors hypothesize that Bronze Age, Yamnaya migration to, and their population of Scandinavia and the British Isles, accounts for the high incidence of MS in these regions, stemming from the geographical spread of HLA-DRB1*15:01 as evidenced by their data. These data further indicate that this immune-related allele likely underwent positive-selection pressures, suggesting that HLA-DRB1*15:01 may have conferred increased immunological resistance to zoonotic pathogens that the Yamnaya would have been exposed to from a lifestyle of sheep, goat, and cattle herding, as well as from the prevalent consumption of dairy products harvested from these animals. We wish to expand the scope of the Barrie et al. hypothesis to include the other gene that emerged in their extensive genomic analysis; the lactase persistence allele, LCT/MCM6, which allows adults to consume milk beyond childhood without adverse physical effects. We propose that, in addition to the Yamnaya spread of HLA-DRB1*15:01, their spread of LCT/MCM6 engendered a cultural propensity for milk consumption that may play a significant role in the present-day, global distribution of MS. Specifically, we hypothesize that lactose tolerance, and the prominent dairy consumption it enables, may expose individuals to the enteric, dairy-animal pathogen, Clostridium perfringens type B/D and its Epsilon neurotoxin that targets the precise brain tissues damaged during each MS relapse. [ABSTRACT FROM AUTHOR]

Objective: In this review, we have highlighted the advances over the past year in genetics, genomics and epigenetics in the field of osteoarthritis (OA).Methods: A literature search of PubMed was performed using the criteria: "osteoarthritis" and one of the following terms "genetic(s), genomic(s), epigenetic(s), polymorphism, noncoding ribonucleic acid (RNA), microRNA, long noncoding RNA, lncRNA, circular RNA, RNA sequencing (RNA-seq), single cell sequencing, transcriptomics, or deoxyribonucleic acid (DNA) methylation between April 01, 2020 and April 30, 2021.Results: In total we identified 765 unique publications, which eventually reduced to 380 of relevance to the field as judged by two assessors. Many of these studies included multiple search terms. We summarised advances relating to genetics, functional genetics, genomics and epigenetics, focusing on our personal key papers during the year.Conclusions: This year few studies have identified new genetic variants contributing to OA susceptibility, but a focus has been on refining risk loci or their functional validation. The use of new technologies together with investigating the cross-talk between multiple tissue types, greater sample sizes and/or better patient classification (OA subtypes) will continue to increase our knowledge of disease mechanisms and progress towards understanding and treating OA. [ABSTRACT FROM AUTHOR]

High dimensional data are frequently collected across research fields such as genomics, health sciences, economics, and social sciences. Recently, variable selection in the high dimensional setting has drawn great attention, with many effective methods developed to reduce the dimensionality of the data. However, most of these methods apply only to normally or near normally distributed outcomes in a linear regression model, while few studies focus on variable selection for skewed data. Simulation studies show that ignoring an appropriate transformation for the outcome can lead to biased inferences (e.g., missing important covariates). In this paper, we develop a variable selection procedure for the Box–Cox power transformation model by developing a penalized maximum likelihood estimate and deriving the consistency, oracle property, and asymptotic distribution of this estimate. Simulation studies demonstrate that the proposed method can yield higher sensitivity, while the naive method that without doing transformation can lead to lower sensitivity. We apply the proposed method to a gene expression study. • Variable selection has drawn great attention across research fields. • Few studies focus on variable selection for skewed data. • We develop a variable selection procedure for the Box–Cox power transformation model for handling skewed data. • The proposed method can yield higher sensitivity than naïve methods. [ABSTRACT FROM AUTHOR]

The increasing pace of gene discovery in the last decade has brought a major change in the way the genetic causes of brain malformations are being diagnosed. Unbiased genomic screening has gained the first place in the diagnostic protocol of a child with congenital (brain) anomalies and the detected variants are matched with the phenotypic presentation afterwards. This process is defined as "reverse phenotyping". Screening of DNA, through copy number variant analysis of microarrays and analysis of exome data on different platforms, obtained from the index patient and both parents has become a routine approach in many centers worldwide. Clinicians are used to multidisciplinary team interaction in patient care and disease management and this explains why the majority of research that has led to the discovery of new genetic disorders nowadays proceeds from clinical observations to genomic analysis and to data exchange facilitated by open access sharing databases. However, the relevance of multidisciplinary team interaction has not been object of systematic research in the field of brain malformations. This review will illustrate some examples of how diagnostically driven questions through multidisciplinary interaction, among clinical and preclinical disciplines, can be successful in the discovery of new genes related to brain malformations. The first example illustrates the setting of interaction among neurologists, geneticists and neuro-radiologists. The second illustrates the importance of interaction among clinical dysmorphologists for pattern recognition of syndromes with multiple congenital anomalies. The third example shows how fruitful it can be to step out of the "clinical comfort zone", and interact with basic scientists in applying emerging technologies to solve the diagnostic puzzles. • The paper reflects the opinion of the authors on the approach to the genetic research in the field of brain malformations. • Expertise from different fields, state-of -the-art technologies and interdisciplinary interaction foster advances. • Coordinated interdisciplinary approach promises to accelerate discoveries and to improve disease management. [ABSTRACT FROM AUTHOR]

New media have had a profound impact on the way scientists access, carry out and communicate their research. Driven by the impetus of the Open Science movement, some researchers have put their laboratory notebook online, providing access to this occluded and largely unexplored research genre. Drawing on the notion of the adaptability of language, this paper proposes a case-study of an online genomics notebook from two perspectives, inter-generic and intra-generic. We first investigate the considerable adaptation that takes place when the record of experimental work in the notebook is shaped into a public research claim in the downstream research article genre, focusing on utterer-interpreter relations and the immediacy of the notebook vs the decontextualized reconstruction of the article. We then investigate how migration to the web also results in adaptation by comparing the online notebook with the traditional paper notebooks used in science labs. The comparison shows that the online version places more emphasis on emotive elements and that its language and presentational features are more informal. We conclude with some comments on the metapragmatic awareness of the author of the online notebook as he navigates between these different genres and media. [ABSTRACT FROM AUTHOR]

This is the first of 2 review papers on genetics and genomics appearing as part of the series on "omics." Genomics pertains to all components of an organism's genes, whereas genetics involves analysis of a specific gene or genes in the context of heredity. The paper provides introductory comments, describes the basis of human genetic diversity, and addresses the phenotypic consequences of genetic variants. Rare variants with large effect sizes are responsible for single-gene disorders, whereas complex polygenic diseases are typically due to multiple genetic variants, each exerting a modest effect size. To illustrate the clinical implications of genetic variants with large effect sizes, 3 common forms of hereditary cardiomyopathies are discussed as prototypic examples of single-gene disorders, including their genetics, clinical manifestations, pathogenesis, and treatment. The genetic basis of complex traits is discussed in a separate paper. [ABSTRACT FROM AUTHOR]

The promise of treatments for common complex diseases (CCDs) is understood as an important force driving large scale genetics research over the last few decades. This paper considers the phenomenon of the Genome Wide Association Study (GWAS) via one high profile example, the Wellcome Trust Case Control Consortium (WTCCC). The WTCCC despite not fulfilling promises of new health interventions is still understood as an important step towards tackling CCDs clinically. The 'sociology of expectations' has considered many examples of failure to fulfil promises and the subsequent negative consequences including disillusionment, disappointment and disinvestment. In order to explore why some domains remain resilient in the face of apparent failure, I employ the concept of the 'problematic' found in the work of Giles Deleuze. This alternative theoretical framework challenges the idea that the failure to reach promised goals results in largely negative outcomes for a given field. I will argue that collective scientific action is motivated not only by hopes for the future but also by the drive to create solutions to the actual setbacks and successes which scientists encounter in their day-to-day work. I draw on eighteen interviews. • The Deleuzian 'problematic' helps us to explore the mutual reinforcement between experience and expectations in driving scientific practice. • The 'problematic' challenges the contractual view of scientific promises and outcomes employed by Science and Technology Studies (STS). • A Deleuzian perspective explains how despite not delivering on short term promises Genome Wide Association Studies remain a valued contribution. [ABSTRACT FROM AUTHOR]

Respiratory syncytial virus (RSV) is the most common cause of serious lower respiratory tract illness in infants and children and causes significant disease in the elderly and immunocompromised. Recently there has been an acceleration in the development of candidate RSV vaccines, monoclonal antibodies and therapeutics. However, the effects of RSV genomic variability on the implementation of vaccines and therapeutics remain poorly understood. To address this knowledge gap, the National Institute of Allergy and Infectious Diseases and the Fogarty International Center held a workshop to summarize what is known about the global burden and transmission of RSV disease, the phylogeographic dynamics and genomics of the virus, and the networks that exist to improve the understanding of RSV disease. Discussion at the workshop focused on the implications of viral evolution and genomic variability for vaccine and therapeutics development in the context of various immunization strategies. This paper summarizes the meeting, highlights research gaps and future priorities, and outlines what has been achieved since the meeting took place. It concludes with an examination of what the RSV community can learn from our understanding of SARS-CoV-2 genomics and what insights over sixty years of RSV research can offer the rapidly evolving field of COVID-19 vaccines. [ABSTRACT FROM AUTHOR]

The wider availability of genomic sequencing, notably gene panels, in cancer care allows for personalised medicine or the tailoring of clinical management to the genetic characteristics of tumours. While the primary aim of mainstream genomic sequencing of cancer patients is therapy-focussed, genomic testing may yield three types of results beyond the answer to the clinical question: suspected germline mutations, variants of uncertain significance (VUS), and unsolicited findings pertaining to other conditions. Ideally, patients should be prepared beforehand for the clinical and psychosocial consequences of such findings, for themselves and for their family members, and be given the opportunity to autonomously decide whether or not to receive such unsolicited genomic information. When genomic tests are mainstreamed into cancer care, so should accompanying informed consent practices. This paper outlines what mainstream oncologists may learn from the ethical tradition of informed consent for genomic sequencing, as developed within clinical genetics. It argues that mainstream informed consent practices should focus on preparing patients for three types of unsolicited outcomes, briefly and effectively. Also, it argues that when the chance of unsolicited findings is very low, opt-out options need not be actively offered. The use of a layered approach – integrated in information systems – should render informed consent feasible for non-geneticist clinicians in mainstream settings. (Inter) national guidelines for mainstreaming informed consent for genomic sequencing must be developed. • Genomic sequencing is increasingly offered by oncologists directly ('mainstreamed'). • Informed consent is important and feasible, also in mainstream settings. • Patients must be informed about – otherwise unexpected – possible test outcomes. • Oncologists can use a layered approach to personalise informed consent. [ABSTRACT FROM AUTHOR]

Biodegradation is an efficient and cost–effective approach to remove residual penicillin G sodium (PGNa) from the environment. In this study, the effective PGNa–degrading strain SQW1 (Sphingobacterium sp.) was screened from contaminated soil using enrichment technique. The effects of critical operational parameters on PGNa degradation by strain SQW1 were systematically investigated, and these parameters were optimized by response surface methodology to maximize PGNa degradation. Comparative experiments found the extracellular enzyme to completely degrade PGNa within 60 min. Combined with whole genome sequencing of strain SQW1 and LC–MS analysis of degradation products, penicillin acylase and β–lactamase were identified as critical enzymes for PGNa biodegradation. Moreover, three degradation pathways were postulated, including β–lactam hydrolysis, penicillin acylase hydrolysis, decarboxylation, desulfurization, demethylation, oxidative dehydrogenation, hydroxyl reduction, and demethylation reactions. The toxicity of PGNa biodegradation intermediates was assessed using paper diffusion method, ECOSAR, and TEST software, which showed that the biodegradation products had low toxicity. This study is the first to describe PGNa–degrading bacteria and detailed degradation mechanisms, which will provide new insights into the PGNa biodegradation. [Display omitted] • Sphingobacterium sp. SQW1 isolated from contaminated soil can efficiently degrade PGNa. • Extracellular enzyme contributed the most to PGNa degradation. • The whole genome of strain SQW1 was sequenced, and key degradation enzymes were identified. • Three possible PGNa degradation pathways were identified. • Biodegradation products of PGNa had low toxicity. [ABSTRACT FROM AUTHOR]

Accidentally found, two poisoned brown rats from Hungary were surveyed for presence of circoviral DNA, using specific nested primers, designed against the rep gene of the virus. Both specimens were positive. The whole genomes were amplified using inverse PCR based on the Rep sequence parts and sequenced by the primer walking method. Genomic analyses revealed that these novel rat viruses, together with tawny owl-associated circovirus reported by Italian researchers in 2022, are sequence variations of the same virus from genus Circovirus. In phylogenetic reconstructions, these circovirus strains detected from brown rats clustered closest to circoviruses derived from faeces samples of various predatory mammals. Molecular data as well as the phylogenetic analyses of the complete derived replication-associated protein and the capsid protein, as well as the prey preference of the host species of the recently described tawny owl-associated virus suggest that brown rat could be the evolutionary adapted host of the viruses described in this paper (brown rat circovirus types 1 and 2) and the previously reported tawny owl-associated virus. Possible pathogenic and zoonotic role of these viruses need further studies. • Circoviruses were detected by PCR from organs of free-ranging brown rats. • Full genomes of these viruses were sequenced. • The DNA and protein sequences of the two viruses were compared to each other. • Phylogenetic studies indicated similar circoviruses from predatory mammals and birds. • These viruses are probably genuine circoviruses of rats. [ABSTRACT FROM AUTHOR]

The dynamic evolution of volatile flavor compounds and microbial community structure during the entire fermentation process was analyzed using high throughput sequencing and GC-MS. The results showed that a total of 42 volatile flavor compounds including alcohols, esters and alkenes were detected throughout the fermentation process. High-throughput sequencing showed that the number of microorganisms in the sample peaked at 24 h of fermentation. Proteobacteria was the most abundant microbiota throughout the fermentation period, underscoring its relative stability during the fermentation process. Correlation analysis showed that Rheinheimera had the highest correlation with alkenes and other volatile flavor compounds. There was a significant correlation between Enterobacteriaceae , Ewingella and ester volatile flavor compounds. There was a significant correlation between Enterobacter , Stenotrophomonas , Sphingomonas and Rheinheimera and alkenes and other volatile flavor compounds. There was also a significant correlation between Prevotella and acid-volatile flavor compounds. In summary, this study provides new insights into the formation of flavor substances and the dynamic changes of microflora during the production of kefir-fermented vegetable juice. At the same time, it also provides a reference for improving the economic added value of agricultural products. Industry relevance: For consumers, water kefir (WK) has become an increasingly attractive natural beverage. In this paper, we elucidated the dynamic properties of WK fermentation by GC-MS combined with gene sequencing. The internal relationship between the composition of the microbiota and the formation of flavor substances in WK fermentation was revealed. These findings represent an important milestone in the understanding of WK microbiology and pave the way for the development of starters with clear flavor substances. • The main volatile flavor compounds include alcohols, esters and alkenes. • Proteobacteria is the most abundant microbial community during the entire fermentation period. • Rheinheimera had the highest correlation with alkenes and other volatile flavor compounds. • There was significant correlation between Enterobacteriaceae , Ewingella and ester volatile flavor compounds. • There was significant correlation between Prevotella and acid-volatile flavor compounds. [ABSTRACT FROM AUTHOR]

The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. Statement of Significance In this opinion paper, we postulate that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. [ABSTRACT FROM AUTHOR]

In this paper I track the history of post-1945 human genetics and genomics emphasizing the importance of ideas about risk to the scientific study and medical management of human heredity. Drawing on my own scholarship as it is refracted through important new work by other scholars both junior and senior, I explore how radiation risk and then later disease risk mattered to the development of genetics and genomics, particularly in the United States. In this context I excavate one of the central ironies of post-war human genetics: while studies of DNA as the origin and cause of diseases have been lavishly supported by public institutions and private investment around the world, the day-to-day labor of intensive clinical innovation has played a far more important role in the actual human experience of genetic disease and genetic risk for affected families. This has implications for the archival record, where clinical interactions are less readily accessible to historians. This paper then suggests that modern genomics grew out of radiation risk; that it was and remains a risk assessment science; that it is temporally embedded as a form of both prediction and historical reconstruction; and that it has become a big business focused more on risk and prediction (which can be readily marketed) than on effective clinical intervention. [ABSTRACT FROM AUTHOR]

In the post-genomic era the availability of genomic tools and resources is leading us to novel generation methods in plant breeding, as they facilitate the study of the genotype and its relationship with the phenotype, in particular for complex traits. In this study we have mainly concentrated on the Cucumis sativus and (but much less) Cucurbitaceae family several important vegetable crops. There are many reports on research conducted in Cucurbitaceae plant breeding programs on the ripening process, phloem transport, disease resistance , cold tolerance and fruit quality traits. This paper presents the role played by new omic technologies in the creation of knowledge on the mechanisms of the formation of the breeding features. The analysis of NGS (NGS—next generation sequencing) data allows the discovery of new genes and regulatory sequences, their positions, and makes available large collections of molecular markers. Genome-wide expression studies provide breeders with an understanding of the molecular basis of complex traits. Firstly a high density map should be created for the reference genome, then each re-sequencing data could be mapped and new markers brought out into breeding populations. The paper also presents methods that could be used in the future for the creation of variability and genomic modification of the species in question. It has been shown also the state and usefulness in breeding the chloroplastomic and mitochondriomic study. [ABSTRACT FROM AUTHOR]

BayeSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks (BNs) that can scale to tens of thousands of nodes while providing fast and friendly user experience. All the necessary features that enable this are reviewed in this paper; these features include scalability, extensibility, interoperability, ease of use, and interpretability. Scalability is the key factor in learning and processing massive networks within reasonable time; for a maintainable software open to new functionalities, extensibility and interoperability are necessary. Ease of use and interpretability are fundamental aspects of model interpretation, fairly similar to the case of the recent explainable artificial intelligence trend. We present the capabilities of our proposed framework by highlighting a real example of a BN learned from genomic data obtained from Allen Institute for Brain Science. The extensibility properties of the software are also demonstrated with the help of our BN-based probabilistic clustering implementation, together with another genomic-data example. [ABSTRACT FROM AUTHOR]

Objective: To foster implementation of genomic testing in medical care by providing a cadre of physicians with 'hands on' experience in genomics, positioning them as opinion leaders in their medical speciality. This paper presents qualitative evaluation of immediate outcomes, in particular its impact on peer interactions.Methods: Program design and delivery was informed by implementation science, behavior change and experiential learning theories. Inductive content analysis of transcribed audio-recordings from semi-structured post-project interviews with all participants (n = 12) was conducted.Results: Participants reported the immersion experience improved their genomic capability, established them as credible genomic experts within their speciality and altered their practice in genomic medicine. Participants reported strengthening and widening of peer-to-peer and interdisciplinary communication, with both passive diffusion and active dissemination of information to peers. Some also became a resource for genetic professionals.Conclusions: Genomic immersion participants described elements which support sustained integration of an innovation, including immediate changes (e.g. use of genomic tests) and wider impacts (e.g. professional networks).Practice Implications: This study supports a role for immersion as a successful strategy for enhancing engagement of non-geneticist physicians in genomics. Additional study is needed to understand how immersion experiences change the delivery of genomic services at the provider, practice and health system level. [ABSTRACT FROM AUTHOR]

Cancer is the second leading cause of death in the U.S. Utilizing family health history in cancer prevention holds promise in lessening the burden of cancer. Nevertheless, family health history is underutilized in public health and preventive medicine. Community health workers, also known as lay health educators, are ideal candidates to offer basic cancer family history-based education and services to the general public. The authors developed the first cancer family history-based genomics training program in cancer prevention tailored for community health workers. This paper details the development and pilot testing findings of the training. Specifically, a multidisciplinary research team of geneticists, genetic counselors, health educators, community health workers, and community health worker instructors developed a 7-module, 6-hour, bilingual (English and Spanish) cancer family history-based training focusing on cancer family history-based risk assessment, lifestyle recommendations, and genetic evaluation and testing. The curriculum was based on an integrated theoretical framework, the National Comprehensive Cancer Network guidelines, the community health worker core competencies, and the 4MAT instructional model. The Texas Department of State Health Services approved and certified the curriculum with 2 delivery formats: in-person/face-to-face workshops and online training. A total of 34 community health workers completed the pilot training in person (n=17) and online (n=17) in 2018 and 2019. Participating community health workers' knowledge, attitudes, self-efficacy, and intention in delivering basic cancer family history-based genomics education and services significantly increased on the immediate post-test measures compared with their pretest data. Positive ratings and feedback were also reported by the community health workers. Findings from this pilot study suggest that wider training is warranted for educating more community health workers in the U.S. [ABSTRACT FROM AUTHOR]

Genome sequences provide an unprecedented resource to rapidly develop modern crops. A recent paper by Varshney et al. provides genome variation maps of 3366 chickpea accessions. Here, we highlight how this breakthrough research can fundamentally change breeding practices of chickpea and potentially other crops. [ABSTRACT FROM AUTHOR]

Many theorists in recent years have been calling for evolutionary biology to move beyond the Modern Synthesis – the paradigm that has long provided the theoretical backbone for the discipline. Terms like "postmodern synthesis," "integrative synthesis," and "extended evolutionary synthesis" have been invoked by various critics in connection with the many recent developments that pose deep challenges – even contradictions – to the traditional model and underscore the need for an update, or a makeover. However, none of these critics, to this author's knowledge, has to date offered an explicit alternative that could provide a unifying theoretical paradigm for our vastly increased knowledge about living systems and the history of life on Earth (but see Noble 2015, 2017). This paper briefly summarizes the case against the Modern Synthesis and its many amendments over the years, and a new paradigm is proposed, called an "Inclusive Biological Synthesis," which, it is argued, can provide a more general framework for the biological sciences. The focus of this framework is the fundamental nature of life as a contingent dynamic process – an always at-risk "survival enterprise." The ongoing, inescapable challenge of earning a living in a given environmental context – biological survival and reproduction – presents an existential problem to which all biological phenomena can be related and comprehended. They and their "parts" can be analyzed in relation to ethologist Niko Tinbergen's four key questions. Some basic properties and guiding assumptions related to this alternative paradigm are also identified. • It is argued here that the time has come to replace the Modern Synthesis. • Many recent developments go beyond or contradict this venerable paradigm. • The case against the Modern Synthesis is briefly summarized. • An alternative called an "Inclusive Biological Synthesis" is proposed. • Tinbergen's four key questions are advanced as a common research agenda. [ABSTRACT FROM AUTHOR]

Genome rearrangements are events where large blocks of DNA exchange places during evolution. The analysis of these events is a promising tool for understanding evolutionary genomics. Many pairwise rearrangement distances have been proposed, based on finding the minimum number of rearrangement events to transform one genome into the other, using some predefined operation. When more than two genomes are considered, we have the more challenging problem of rearrangement-based phylogeny reconstruction. One important problem is the Closest Genome Problem (CGP), that aims to find, for a given distance notion, a genome that minimizes the maximum distance to any other, which can be seen as finding a genome in the center of all others. The Hamming Closest String Problem (Hamming-CSP) was already studied and settled to be NP-complete. In this paper, we show that the CGP is NP-complete for well-known genome rearrangement distances, such as the single-cut-or-join, the breakpoint and the block interchange. [ABSTRACT FROM AUTHOR]

• Third-generation sequencing makes up for the defect of short-read sequencing and improves the efficiency of disease diagnosis. • Third-generation sequencing shows great potential in detecting genetic diseases with its unique advantages. • Rare and complex structural variants are accurately detected by Third-generation sequencing based SMRT sequencing. • Third-generation sequencing is expected to become an important tool for molecular diagnosis of genetic diseases. Third-generation sequencing (TGS) has led to a brave new revolution in detecting genetic diseases over the last few years. TGS has been rapidly developed for genetic disease applications owing to its significant advantages such as long read length, rapid detection, and precise detection of complex and rare structural variants. This approach greatly improves the efficiency of disease diagnosis and complements the shortcomings of short-read sequencing. In this paper, we first briefly introduce the working mechanism of one of the most important representatives of TGS, single-molecule real-time (SMRT) sequencing by Pacific Bioscience (PacBio), followed by a review and comparison of the advantages and disadvantages of different sequencing technologies. Finally, we focused on the progress of SMRT sequencing applications in genetic disease detection. Future perspectives on the applications of TGS in other fields were also presented. With the continuous innovation of the SMRT technologies and the expansion of their fields of application, SMRT sequencing has broad clinical application prospects in genetic diseases detection, and is expected to become an important tool for the molecular diagnosis of other diseases. [ABSTRACT FROM AUTHOR]

Precision oncology has made remarkable strides in improving clinical outcomes, offering hope to patients with historically difficult-to-treat, as well as rare or neglected cancers. However, despite rapid advancement, precision oncology has reached a critical juncture, where patient access to these life-saving medicines may be hampered by strict requirements by Health Technology Assessment (HTA) bodies for randomised controlled trials (RCTs) for assessing new medicines against appropriate comparator. The very nature of precision oncology—matching a tumour's unique molecular alterations to targeted therapies predicted to elicit response—can make the use of RCTs very difficult, as only a very small number of patients might qualify for a given therapy within a traditional clinical trial setting. Real-world evidence (RWE) has been accepted for regulatory decision-making but has yet to reach widespread acceptance by HTA bodies. As the oncology treatment landscape has evolved towards favouring the concept of precision oncology, there is a growing need for flexibility in the way HTA bodies evaluate new medicines. We must acknowledge that current assessment methodologies can limit access to life-changing medicines for many patients who have no alternative options and that a growing number of precision oncology medicines with proven clinical benefits in rare tumours cannot be reasonably evaluated using traditional methodologies. The objectives of this paper are to advocate a change in mindset regarding best practices in drug assessment models and to propose alternative approaches when considering indications for which RWE is the most compelling data source available. • RCTs are often not feasible for medicines that target rare genomic alterations. • Current HTA processes may not fully recognise the potential of these new drugs. • As a result, access is difficult for patients with otherwise limited options. • Real-world evidence can provide relevant data for precision oncology medicines. • Such data should be considered as an additional approach to traditional HTA models. [ABSTRACT FROM AUTHOR]

Discovering developmental stuttering's biological explanation has been an enduring concern. Novel advances in genomics and neuroscience are making it possible to isolate and pinpoint genetic and brain differences implicated in stuttering. This is giving rise to a hope that, in the future, dysfluency could be better managed if stuttering's biological basis could be better understood. Concurrent to this, there is another hope rising: a hope of a future where differing fluencies would not be viewed through a reductive lens of biology and associated pathologies. The central aim of this paper is to edge out ethical implications of novel research into stuttering's biological explanation. In doing so, the paper proposes to look beyond the bifurcation sketched by the medical and social model of disability. The paper demonstrates how the scientific hope of discovering stuttering's biological explanation acts as an accessory of disablement due to the language of 'lack' and 'deficit' employed in reporting scientific findings and proposes participatory research with people who stutter as an antidote to manage this disablement. • Genomic and neuroscientific research on stuttering raises an ethical concern of pathologizing difference. • There is lack of research on how people who stutter ethically reflect on scientific research on stuttering. • The language of 'lack' and 'deficit' used in biological explanations of stuttering acts as an accessory of disablement. • We propose to look beyond an either/or approach to medical and social model of disability as applied to stuttering. • Views of people who stutter should be sought by incorporating insights from participatory research methodologies. [ABSTRACT FROM AUTHOR]

Monogonont rotifers are common species in aquatic environments and make model species for ecotoxicology studies. Whole genomes of several species of the genus Brachionus have been assembled, but no information on the freshwater rotifer Brachionus rubens has been reported. In this study, the whole-genome sequence of B. rubens was successfully assembled using NextDenovo. The total length of the genome was 132.7 Mb (N50 = 2.51 Mb), including 122 contigs. The GC contents accounted for 29.96% of the genome. Aquatic organisms are always exposed to various external stresses, and a comprehensive genomic analysis is needed to better understand the adverse effects on organisms. This paper focuses on the ecotoxicological aspect and conducted genome analysis of representative gene families involved in detoxification mechanisms against environmental stressors. Specifically, we identified cytochrome P450 genes (CYPs) of phase I, glutathione S -transferase genes (GSTs) of phase II, and ATP-binding cassette transporter genes (ABCs) of phase III in the genome of B. rubens. Gene duplications were found in CYP , GST , and ABC genes, as is the case for other Brachionus rotifers. Our results suggest that these detoxification-related gene families have evolved in a species-specific and/or lineage-specific manner. This paper improves our understanding of how the freshwater Brachionus rotifers respond to environmental stressors in a molecular ecotoxicology context. [Display omitted] • The whole-genome sequence of B. rubens was successfully assembled using NextDenovo. • The total length of the genome was 132.7 Mb (N50 = 2.51 Mb), including 122 contigs. • Representative genes (CYP s, GST sI, and ABC s) for detoxification mechanisms were identified. • Gene duplications were found in CYP , GST , and ABC genes. [ABSTRACT FROM AUTHOR]

Abstract: The development of a genomics-derived discipline within geology is timely, as a result of major advances in acquiring and processing geologically relevant genetic data. This paper articulates the emerging field of “geogenomics”, which involves the use of large-scale genetic data to constrain geological hypotheses. The paper introduces geogenomics and discusses how hypotheses can be addressed through collaboration between geologists and evolutionary biologists. As an example, geogenomic methods are applied to evaluate competing hypotheses regarding the timing of the Andean uplift, the closure of the Isthmus of Panama, the onset of trans-Amazon drainage, and Quaternary climate variation in the Neotropics. [Copyright &y& Elsevier]

Abstract: The paper introduces the empirical science of ‘mind genomics’, whose objective is to understand the dimensions of ordinary, everyday experience, identify mind-set segments of people who value different aspects of that everyday experience, and then assign a new person to a mind-set by a statistically appropriate procedure. By studying different experiences using experimental design of ideas, ‘mind genomics’ constructs an empirical, inductive science of perception and experience, layer by layer. The ultimate objective of ‘mind genomics’ is a large-scale science of experience created using induction, with the science based upon emergent commonalities across many different types of daily experience. The particular topic investigated in the paper is the experience of healthful snacks, what makes a person ‘want’ them, and the dollar value of different sensory aspects of the healthful snack. [Copyright &y& Elsevier]

Abstract: In modern proteomics, prediction of protein–protein interactions (PPIs) is a key research line, as these interactions take part in most essential biological processes. In this paper, a new approach is proposed to PPI data classification based on the extraction of genomic and proteomic information from well-known databases and the incorporation of semantic measures. This approach is carried out through the application of data mining techniques and provides very accurate models with high levels of sensitivity and specificity in the classification of PPIs. The well-known support vector machine paradigm is used to learn the models, which will also return a new confidence score which may help expert researchers to filter out and validate new external PPIs. One of the most-widely analyzed organisms, yeast, will be studied. We processed a very high-confidence dataset by extracting up to 26 specific features obtained from the chosen databases, half of them calculated using two new similarity measures proposed in this paper. Then, by applying a filter–wrapper algorithm for feature selection, we obtained a final set composed of the eight most relevant features for predicting PPIs, which was validated by a ROC analysis. The prediction capability of the support vector machine model using these eight features was tested through the evaluation of the predictions obtained in a set of external experimental, computational, and literature-collected datasets. [Copyright &y& Elsevier]

Abstract: This paper provides exact analytical expressions for the bias, variance, and RMS for the resubstitution and leave-one-out error estimators in the case of linear discriminant analysis (LDA) in the univariate heteroskedastic Gaussian model. Neither the variances nor the sample sizes for the two classes need be the same. The generality of heteroskedasticity (unequal variances) is a fundamental feature of the work presented in this paper, which distinguishes it from past work. The expected resubstitution and leave-one-out errors are represented by probabilities involving bivariate Gaussian distributions. Their second moments and cross-moments with the actual error are represented by 3- and 4-variate Gaussian distributions. From these, the bias, deviation variance, and RMS for resubstitution and leave-one-out as estimators of the actual error can be computed. The RMS expressions are applied to the determination of sample size and illustrated in biomarker classification. [Copyright &y& Elsevier]

CRISPR/Cas9 is a tool which has unprecedented gene editing capabilities which if harnessed correctly can revolutionize our lives. But, the system is still in its infancy and there is scope for substantial optimization particularly in the process of guide selection. There are thousands of RNA guides which can theoretically provide gene knockouts. However, these guides do not share the same knockout efficiency. In this paper, a convolution neural network (CNN) learning model (named as DeepSgRNA) is proposed to identify & predict RNA guides for achieving better efficiency. The proposed model removes the need for any feature construction. Previously there have been attempts at automating guide selection process using machine learning (ML) with handcrafted features. The ML models have been heavily reliant on feature engineering and thus are not scalable. The hierarchical feature generation abilities of CNN's have been leveraged for this task. The model is trained on approximately 400,000 instances of sgRNA sequences from the GenomeCRISPR project dataset. A performance comparison with existing models is presented based on the Spearman correlation index. Finally, it has been seen that the proposed CNN model (DeepSgRNA) is better at predicting sgRNA guide efficiencies than all the existing models. • A novel deep learning model (DeepSgRNA) using convolutional neural network is proposed for SgRNA Prediction. • One-hot coding is used to convert the genetic string to matrix. • Performance is evaluated using Spearman correlation and area under curve. • The 10-fold cross validation is used for training and testing. • Results suggest that DeepSgRNA provided better performance and more scalable as compared to other methods. [ABSTRACT FROM AUTHOR]