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The article focuses on Evidences of airborne coronavirus transmission and necessary cautionary measures. Topics discussed include according to a research by six experts from the Grate Britain, USA and Canada at the Cooperative Institute for Research in Environmental Sciences (CIRES) and the University of Colorado Boulder; and public health interventions that fail to address the virus as primarily airborne leave individuals defenseless and allow the virus to spread.

The article presents the discussion on exacerbating the climate change catastrophe in the biosphere. Topics include extinction of several plant and animal species, depletion of ozone layer, increasing air pollution, and rising sea levels; and species anticipating in migrate away from the scorching Equator in search of more suitable environments.

The article discusses microbiome that serves several roles in human wellbeing and mentions convergence of interest in use of live bacteria for disease treatment. Topics discussed include colonization of microbes in gastrointestinal tract, skin and malignancies, utilization of bacteria for therapies and bacteria based cancer treatment for non-muscle invasive bladder cancer.

The article announces appointment of Ajay Kumar Sood as Principal Scientific Advisor (PSA) to Government of India.

The article presents the discussion on regular exercise and healthy food keeping strong and helping in maintaining the weight. Topics include declining physical strength and cognitive capacities deteriorating by offering neuroprotection for the brain during the ageing process; and undergoing tests designed particularly for evaluating cognitive ability as well as magnetic resonance imaging for examining brain function.

The article discusses cardiovascular diseases (CVDs) account for 18 million deaths every year, an estimated 31% of all deaths worldwide. Topics include high blood pressure and high cholesterol have not treated properly has the medications prescribed to treat; and 30% of all deaths and 15% of morbidities have caused by CVDs.

Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, reveal genes’ changing functional roles across tissues, and illuminate disease-disease relationships. We introduce NetWAS, which combines genes with nominally significant GWAS p-values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS, and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than one hundred human tissues and cell types.

The article informs that Arthritis is a widespread health issue that is a leading cause of disability, and how Bioengineers are making efforts to finding a cure for arthritis. Topics include implant incorporates a tissue scaffold comprising of poly-L lactic acid (PLLA) nanofibres, which are commonly used to repair surgical wounds that disintegrate after healing; and scaffold can help create favourable surface charge for cartilage formation with regulated mechanical stimulation.

The article explores Over the years, technology has developed in ways we could never have envisioned. Humans first thought of autonomous machines for household tasks decades ago, with widespread industrial uses at present. The foundations for the invention of robots were laid following the Industrial Revolution, although the concept of robots can be traced back to ancient history.

Challenges in modern biology demand shifting focus from components--genes and proteins--to their interacting whole. Integrating information from multiple genomic datasets is seen as a means to this end, capable of providing robust and accurate ways to unravel these functional associations. Integrative strategies, both novel and adapted from other well-studied organisms, are being employed in the model plant Arabidopsis thaliana to interpret genomewide expression, metabolic profiling and protein interaction studies. Exciting inroads are being made in mining and interpretation of developmental, physiological and environmental-response 'programs' using sequence and functional information. The fundamental transcriptional regulatory logic is emerging in Arabidopsis, presently revealed as isolated conditional, spatial or temporal regulatory 'modules'. This immediately calls for efforts towards assembling these building blocks together into a unifying model, thus creating standards for future work to compare with. As a young field, Arabidopsis systems biology is ripe with such an opportunity, now scarcely realizable in other model organisms. [ABSTRACT FROM AUTHOR]

The article discusses the applicability of tests for non-literal infringement of computer software in India. The article cites U.S. and British laws relating to non-literal infringement of computer programs. Copyright in computer software has been largely untested by courts in India. Although copyright in computer programs has been recognized in the Indian Copyright Act of 1957, the issue of copyright in non-literal elements of computer software has to be considered.

The article explores the connection between nutrition and children's mental well-being, and reveals that youngsters who eat more fruits and vegetables are more likely to have better mental well-being than those who eat less of the above. It mentions association between a healthy diet and improved mental health persisted, even after accounting for all of the other factors.

The article reports on a study by researchers at Deakin University in Australia, in which they discovered that certain warm-blooded animals started to 'shape-shift' their bodies to adjust to environmental changes like warming temperature. Also cited are how birds use their beaks to regulate heat, while mammals utilize their ears. Also cited is the rule developed by Joel Asaph Allen outlining how creatures adjusted to warmer conditions through their body features.

The article reports that bacterial pneumonia or overreactions of the immunological defense system of the body, played important roles in the increased risk of mortality. Topics include experts link the significant mortality seen in previous viral pandemics such as secondary bacterial infection; and evidence linking the mortalities to a subsequent bacterial infection, probably due to the repeated course of antibiotics given to severely ill patients.

The article focuses on air-powered robot requires no electronics for movements. Topics include the soft robots are intrinsically secure, highly durable and potentially inexpensive, making them appealing for a wide range of applications, the safety advantages they provide to humans who may be required to operate in their vicinity, and the inflatable components and air pressure have played an important role in soft robotics study.

The article offers information on scientists pave way for possible carbon-neutral aviation. Topics include the researchers used a chemical process known as organic combustion method (OCM), which was developed to prepare highly active metal catalysts for a variety of processes, and the catalytic method is likely to be the road to reaching net-zero carbon emissions from the aviation sector in the near future.

Summary PyGenePlexus is a Python package that enables a user to gain insight into any gene set of interest through a molecular interaction network informed supervised machine learning model. PyGenePlexus provides predictions of how associated every gene in the network is to the input gene set, offers interpretability by comparing the model trained on the input gene set to models trained on thousands of known gene sets, and returns the network connectivity of the top predicted genes. Availability and implementation https://pypi.org/project/geneplexus/ and https://github.com/krishnanlab/PyGenePlexus. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Network-based machine learning (ML) has the potential for predicting novel genes associated with nearly any health and disease context. However, this approach often uses network information from only the single species under consideration even though networks for most species are noisy and incomplete. While some recent methods have begun addressing this shortcoming by using networks from more than one species, they lack one or more key desirable properties: handling networks from more than two species simultaneously, incorporating many-to-many orthology information, or generating a network representation that is reusable across different types of and newly-defined prediction tasks. Here, we present GenePlexusZoo, a framework that casts molecular networks from multiple species into a single reusable feature space for network-based ML. We demonstrate that this multi-species network representation improves both gene classification within a single species and knowledge-transfer across species, even in cases where the inter-species correspondence is undetectable based on shared orthologous genes. Thus, GenePlexusZoo enables effectively leveraging the high evolutionary molecular, functional, and phenotypic conservation across species to discover novel genes associated with diverse biological contexts. Author summary: Our work addresses two major challenges; 1) computationally predicting the role a gene plays in various diseases, processes and phenotypes, and 2) accurately transferring genetic information discovered in one species to another. To simultaneously tackle both of these challenges, we developed the GenePlexusZoo method which builds a gene classification model by utilizing molecular interaction information from multiple species, seamlessly handling the complicated mapping of how genes across species are functionally related. We show that machine learning classifiers that utilize information from multiple species outperform those that only consider information from a single species. Additionally, we show that the GenePelxusZoo method is able to accurately transfer knowledge from one species to another, even in the cases where no previous connection would have been detected based solely on shared orthologous genes. Finally, we present an illustrative example of how GenePlexusZoo can provide novel insights into a complicated genetic-based disease. [ABSTRACT FROM AUTHOR]

Since they emerged approximately 125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests that there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function. Topological Data Analysis shows how gene expression shapes the form and function of flowering plants, which have adapted to dominate Earth's varied landscapes over 125 million years. The findings reveal consistent gene patterns across these plants, illuminating their responses to stress and providing a novel way to explore complex biological data. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa) is the primary food source for more than one-half of the world's population. Because rice cultivation is dependent on water availability, drought during flowering severely affects grain yield. Here, we show that the function of a drought-inducible receptor-like cytoplasmic kinase, named GROWTH UNDER DROUGHT KINASE (GUDK), is required for grain yield under drought and well-watered conditions. Loss-of-function gudk mutant lines exhibit sensitivity to salinity, osmotic stress, and abscisic acid treatment at the seedling stage, and a reduction in photosynthesis and plant biomass under controlled drought stress at the vegetative stage. The gudk mutants interestingly showed a significant reduction in grain yield, both under normal well-watered conditions and under drought stress at the reproductive stage. Phosphoproteome profiling of the mutant followed by in vitro assays identified the transcription factor APETALA2/ETHYLENE RESPONSE FACTOR OsAP37 as a phosphorylation target of GUDK. The involvement of OsAP37 in regulating grain yield under drought through activation of several stress genes was previously shown. Our transactivation assays confirmed that GUDK is required for activation of stress genes by OsAP37. We propose that GUDK mediates drought stress signaling through phosphorylation and activation of OsAP37, resulting in transcriptional activation of stress-regulated genes, which impart tolerance and improve yield under drought. Our study reveals insights around drought stress signaling mediated by receptor-like cytoplasmic kinases, and also identifies a primary regulator of grain yield in rice that offers the opportunity to improve and stabilize rice grain yield under normal and drought stress conditions. [ABSTRACT FROM AUTHOR]

Background: Recent studies have suggested that individual variants do not sufficiently explain the variable expressivity of phenotypes observed in complex disorders. For example, the 16p12.1 deletion is associated with developmental delay and neuropsychiatric features in affected individuals, but is inherited in > 90% of cases from a mildly-affected parent. While children with the deletion are more likely to carry additional "second-hit" variants than their parents, the mechanisms for how these variants contribute to phenotypic variability are unknown. Methods: We performed detailed clinical assessments, whole-genome sequencing, and RNA sequencing of lymphoblastoid cell lines for 32 individuals in five large families with multiple members carrying the 16p12.1 deletion. We identified contributions of the 16p12.1 deletion and "second-hit" variants towards a range of expression changes in deletion carriers and their family members, including differential expression, outlier expression, alternative splicing, allele-specific expression, and expression quantitative trait loci analyses. Results: We found that the deletion dysregulates multiple autism and brain development genes such as FOXP1, ANK3, and MEF2. Carrier children also showed an average of 5323 gene expression changes compared with one or both parents, which matched with 33/39 observed developmental phenotypes. We identified significant enrichments for 13/25 classes of "second-hit" variants in genes with expression changes, where 4/25 variant classes were only enriched when inherited from the noncarrier parent, including loss-of-function SNVs and large duplications. In 11 instances, including for ZEB2 and SYNJ1, gene expression was synergistically altered by both the deletion and inherited "second-hits" in carrier children. Finally, brain-specific interaction network analysis showed strong connectivity between genes carrying "second-hits" and genes with transcriptome alterations in deletion carriers. Conclusions: Our results suggest a potential mechanism for how "second-hit" variants modulate expressivity of complex disorders such as the 16p12.1 deletion through transcriptomic perturbation of gene networks important for early development. Our work further shows that family-based assessments of transcriptome data are highly relevant towards understanding the genetic mechanisms associated with complex disorders. [ABSTRACT FROM AUTHOR]

The diagnosis of Parkinson's disease (PD) is usually not established until advanced neurodegeneration leads to clinically detectable symptoms. Previous blood PD transcriptome studies show low concordance, possibly resulting from the use of microarray technology, which has high measurement variation. The Leucine-rich repeat kinase 2 ( LRRK2) G2019S mutation predisposes to PD. Using preclinical and clinical studies, we sought to develop a novel statistically motivated transcriptomic-based approach to identify a molecular signature in the blood of Ashkenazi Jewish PD patients, including LRRK2 mutation carriers. Using a digital gene expression platform to quantify 175 messenger RNA (mRNA) markers with low coefficients of variation (CV), we first compared whole-blood transcript levels in mouse models (1) overexpressing wild-type (WT) LRRK2, (2) overexpressing G2019S LRRK2, (3) lacking LRRK2 (knockout), and (4) and in WT controls. We then studied an Ashkenazi Jewish cohort of 34 symptomatic PD patients (both WT LRRK2 and G2019S LRRK2) and 32 asymptomatic controls. The expression profiles distinguished the four mouse groups with different genetic background. In patients, we detected significant differences in blood transcript levels both between individuals differing in LRRK2 genotype and between PD patients and controls. Discriminatory PD markers included genes associated with innate and adaptive immunity and inflammatory disease. Notably, gene expression patterns in levodopa-treated PD patients were significantly closer to those of healthy controls in a dose-dependent manner. We identify whole-blood mRNA signatures correlating with LRRK2 genotype and with PD disease state. This approach may provide insight into pathogenesis and a route to early disease detection. © 2015 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Drought stress affects cereals especially during the reproductive stage. The maize (Zea mays) drought transcriptome was studied using RNA-Seq analysis to compare drought-treated and well-watered fertilized ovary and basal leaf meristem tissue. More drought-responsive genes responded in the ovary compared with the leaf meristem. Gene Ontology enrichment analysis revealed a massive decrease in transcript abundance of cell division and cell cycle genes in the drought-stressed ovary only. Among Gene Ontology categories related to carbohydrate metabolism, changes in starch and Suc metabolism-related genes occurred in the ovary, consistent with a decrease in starch levels, and in Suc transporter function, with no comparable changes occurring in the leaf meristem. Abscisic acid (ABA)-related processes responded positively, but only in the ovaries. Related responses suggested the operation of low glucose sensing in drought-stressed ovaries. The data are discussed in the context of the susceptibility of maize kernel to drought stress leading to embryo abortion and the relative robustness of dividing vegetative tissue taken at the same time from the same plant subjected to the same conditions. Our working hypothesis involves signaling events associated with increased ABA levels, decreased glucose levels, disruption of ABA/sugar signaling, activation of programmed cell death/senescence through repression of a phospholipase C-mediated signaling pathway, and arrest of the cell cycle in the stressed ovary at 1 d after pollination. Increased invertase levels in the stressed leaf meristem, on the other hand, resulted in that tissue maintaining hexose levels at an "unstressed" level, and at lower ABA levels, which was correlated with successful resistance to drought stress. [ABSTRACT FROM AUTHOR]

Freshwater is a limited and dwindling global resource; therefore, efficient water use is required for food crops that have high water demands, such as rice, or for the production of sustainable energy biomass. We show here that expression of the Arabidopsis HARDY (HRD) gene in rice improves water use efficiency, the ratio of biomass produced to the water used, by enhancing photosynthetic assimilation and reducing transpiration. These drought-tolerant, low-water-consuming rice plants exhibit increased shoot biomass under well irrigated conditions and an adaptive increase in root biomass under drought stress. The HRD gene, an AP2/ERF-like transcription factor, identified by a gain-of-function Arabidopsis mutant hrd-D having roots with enhanced strength, branching, and cortical cells, exhibits drought resistance and salt tolerance, accompanied by an enhancement in the expression of abiotic stress associated genes. HRD overexpression in Arabidopsis produces thicker leaves with more chloroplast-bearing mesophyll cells, and in rice, there is an increase in leaf biomass and bundle sheath cells that probably contributes to the enhanced photosynthesis assimilation and efficiency. The results exemplify application of a gene identified from the model plant Arabidopsis for the improvement of water use efficiency coincident with drought resistance in the crop plant rice. [ABSTRACT FROM AUTHOR]

The growing availability of genome-wide association studies (GWAS) and large-scale biobanks provides an unprecedented opportunity to explore the genetic basis of complex traits and diseases. However, with this vast amount of data comes the challenge of interpreting numerous associations across thousands of traits, especially given the high polygenicity and pleiotropy underlying complex phenotypes. Traditional clustering methods, which identify global patterns in data, lack the resolution to capture overlapping associations relevant to subsets of traits or genes. Consequently, there is a critical need for innovative analytic approaches capable of revealing local, biologically meaningful patterns that could advance our understanding of trait comorbidities and gene-trait interactions. Here, we applied BiBit, a biclustering algorithm, to transcriptome-wide association study (TWAS) results from PhenomeXcan, a large resource of gene-trait associations derived from the UK Biobank. BiBit allows simultaneous grouping of traits and genes, identifying biclusters that represent local, overlapping associations. Our analyses uncovered biologically interpretable patterns, including asthma-related biclusters enriched for immune-related gene sets, connections between eye traits and blood pressure, and associations between dietary traits, high cholesterol, and specific loci on chromosome 19. These biclusters highlight gene-trait relationships and patterns of trait co-occurrence that may otherwise be obscured by traditional methods. Our findings demonstrate that biclustering can provide a nuanced view of the genetic architecture of complex traits, offering insights into pleiotropy and disease mechanisms. By enabling the exploration of complex, overlapping patterns within biobank-scale datasets, this approach provides a valuable framework for advancing research on genetic associations, comorbidities, and polygenic traits.

Radiology, as a highly technical and information-rich medical specialty, is well-suited for artificial intelligence (AI) product development, and many FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical Perspective, we discuss the deployment of AI tools in radiology, exploring regulatory processes, the need for transparency, and other practical challenges. We further highlight the importance of rigorous validation, real-world testing, seamless workflow integration, and end-user education. We emphasize the role for continuous feedback and robust monitoring processes, to guide AI tools' adaptation and help ensure sustained performance. Traditional standalone and alternative platform-based approaches to radiology AI implementation are considered. The presented strategies will help achieve successful deployment and fully realize AI's potential benefits in radiology.

Background: Despite a potentially greater burden of dementia, racial and ethnic minority populations around the world may be more likely to be excluded from research examining risk factors for incident dementia. We aimed to systematically investigate and quantify racial and ethnic minority representation in dementia risk factor research., Methods: We performed a two-stage systematic search of databases-MEDLINE (Ovid SP), Embase (Ovid SP) and Scopus-from inception to March 2021 to identify population-based cohort studies looking at risk factors for dementia incidence. We included cohort studies which were population-based and incorporated a clinical dementia diagnosis., Results: Out of the 97 identified cohort studies, fewer than half (40 studies; 41%) reported the race or ethnicity of participants and just under one-third (29 studies; 30%) reported the inclusion of racial and ethnic minority groups. We found that inadequate reporting frequently prevented assessment of selection bias and only six studies that included racial and ethnic minority participants were at low risk for measurement bias in dementia diagnosis. In cohort studies including a multiethnic cohort, only 182 out of 337 publications incorporated race or ethnicity in data analysis-predominantly (90%) through adjustment for race or ethnicity as a confounder. Only 14 publications (4.2% of all publications reviewed) provided evidence about drivers of any observed inequalities., Conclusions: Racial and ethnic minority representation in dementia risk factor research is inadequate. Comparisons of dementia risk between different racial and ethnic groups are likely hampered by significant selection and measurement bias. Moreover, the focus on 'adjusting out' the effect of race and ethnicity as a confounder prevents understanding of underlying drivers of observed inequalities. There is a pressing need to fundamentally change the way race, ethnicity and the inclusion of racial and ethnic minorities are considered in research if health inequalities are to be adequately addressed., Competing Interests: Competing interests: All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer., Competing Interests: Ethics declaration Competing interests The authors declare no competing interests.

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed ∼4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.