Explore the vast range of titles available.

Advances in CRISPR technology have immensely improved our ability to manipulate nucleic acids, and the recent discovery of the RNA-targeting endonuclease Cas13 adds even further functionality. Here, we show that Cas13 works efficiently in Drosophila , both ex vivo and in vivo. We test 44 different Cas13 variants to identify enzymes with the best overall performance and show that Cas13 could target endogenous Drosophila transcripts in vivo with high efficiency and specificity. We also develop Cas13 applications to edit mRNAs and target mitochondrial transcripts. Our vector collection represents a versatile tool collection to manipulate gene expression at the post-transcriptional level.

We investigate jointly modelling age–year-specific rates of various causes of death in a multinational setting. We apply multi-output Gaussian processes (MOGPs), a spatial machine learning method, to smooth and extrapolate multiple cause-of-death mortality rates across several countries and both genders. To maintain flexibility and scalability, we investigate MOGPs with Kronecker-structured kernels and latent factors. In particular, we develop a custom multi-level MOGP that leverages the gridded structure of mortality tables to efficiently capture heterogeneity and dependence across different factor inputs. Results are illustrated with datasets from the Human Cause-of-Death Database (HCD). We discuss a case study involving cancer variations in three European nations and a US-based study that considers eight top-level causes and includes comparison to all-cause analysis. Our models provide insights into the commonality of cause-specific mortality trends and demonstrate the opportunities for respective data fusion.

Iron Regulatory Protein 1 (IRP1) is a bifunctional cytosolic iron sensor. When iron levels are normal, IRP1 harbours an iron-sulphur cluster (holo-IRP1), an enzyme with aconitase activity. When iron levels fall, IRP1 loses the cluster (apo-IRP1) and binds to iron-responsive elements (IREs) in messenger RNAs (mRNAs) encoding proteins involved in cellular iron uptake, distribution, and storage. Here we show that mutations in the Drosophila 1,4-Alpha-Glucan Branching Enzyme ( AGBE ) gene cause porphyria. AGBE was hitherto only linked to glycogen metabolism and a fatal human disorder known as glycogen storage disease type IV. AGBE binds specifically to holo-IRP1 and to mitoNEET, a protein capable of repairing IRP1 iron-sulphur clusters. This interaction ensures nuclear translocation of holo-IRP1 and downregulation of iron-dependent processes, demonstrating that holo-IRP1 functions not just as an aconitase, but throttles target gene expression in anticipation of declining iron requirements. Higher organisms regulate cellular iron concentrations through Iron Regulatory Proteins (IRPs), which regulate specific messenger RNAs. Here Huynh et al. show that IRP1 requires a Glycogen Branching Enzyme for proper function, and that IRP1 has additional regulatory roles in cell nuclei.

The final body size of any given individual underlies both genetic and environmental constraints. Both mammals and insects use target of rapamycin (TOR) and insulin signaling pathways to coordinate growth with nutrition. In holometabolous insects, the growth period is terminated through a cascade of peptide and steroid hormones that end larval feeding behavior and trigger metamorphosis, a nonfeeding stage during which the larval body plan is remodeled to produce an adult. This irreversible decision, termed the critical weight (CW) checkpoint, ensures that larvae have acquired sufficient nutrients to complete and survive development to adulthood. How insects assess body size via the CW checkpoint is still poorly understood on the molecular level. We show here that the Drosophila transcription factor Snail plays a key role in this process. Before and during the CW checkpoint, snail is highly expressed in the larval prothoracic gland (PG), an endocrine tissue undergoing endoreplication and primarily dedicated to the production of the steroid hormone ecdysone. We observed two Snail peaks in the PG, one before and one after the molt from the second to the third instar. Remarkably, these Snail peaks coincide with two peaks of PG cells entering S phase and a slowing of DNA synthesis between the peaks. Interestingly, the second Snail peak occurs at the exit of the CW checkpoint. Snail levels then decline continuously, and endoreplication becomes nonsynchronized in the PG after the CW checkpoint. This suggests that the synchronization of PG cells into S phase via Snail represents the mechanistic link used to terminate the CW checkpoint. Indeed, PG-specific loss of snail function prior to the CW checkpoint causes larval arrest due to a cessation of endoreplication in PG cells, whereas impairing snail after the CW checkpoint no longer affected endoreplication and further development. During the CW window, starvation or loss of TOR signaling disrupted the formation of Snail peaks and endocycle synchronization, whereas later starvation had no effect on snail expression. Taken together, our data demonstrate that insects use the TOR pathway to assess nutrient status during larval development to regulate Snail in ecdysone-producing cells as an effector protein to coordinate endoreplication and CW attainment.

We investigate joint modelling of longevity trends using the spatial statistical framework of Gaussian process (GP) regression. Our analysis is motivated by the Human Mortality Database (HMD) that provides unified raw mortality tables for nearly 40 countries. Yet few stochastic models exist for handling more than two populations at a time. To bridge this gap, we leverage a spatial covariance framework from machine learning that treats populations as distinct levels of a factor covariate, explicitly capturing the cross-population dependence. The proposed multi-output GP models straightforwardly scale up to a dozen populations and moreover intrinsically generate coherent joint longevity scenarios. In our numerous case studies, we investigate predictive gains from aggregating mortality experience across nations and genders, including by borrowing the most recently available “foreign” data. We show that in our approach, information fusion leads to more precise (and statistically more credible) forecasts. We implement our models in R, as well as a Bayesian version in Stan that provides further uncertainty quantification regarding the estimated mortality covariance structure. All examples utilise public HMD datasets.

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with beneficial effects in models of Huntington’s Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases.

Purpose This study aims to explore the nexus between insurance penetration and economic development in Vietnam, one of the fastest-growing economies over the past two decades. Design/methodology/approach This study uses an updated data set of the insurance sector in Vietnam from 1996 to 2020. The autoregressive lagging distribution and cointegrating non-linear autoregressive lagging distribution (NARDL) models are used to explore the nexus between the insurance market development and economic growth. Findings This study confirms the unidirectional causality and positive impacts of insurance market development on economic growth both in the short and long term, supporting the “supply-leading” hypothesis. Nonlife insurance has more significant but slower impacts on contributing to economic development in the long run. From the NARDL approach, this study also discloses the asymmetric relationship between the insurance industry and economic growth. Aggregate and life insurance display short- and long-term asymmetric impacts, whereas nonlife insurance shows long-term asymmetry. Originality/value To the best of the authors’ knowledge, this is the first study to examine the hidden asymmetries of the insurance-growth nexus in Vietnam from non-linear models. Notwithstanding the theoretical contributions to the prior literature, several practical implications are proposed for insurance businesses, policymakers and investors.

The chemical recycling of polyethylene terephthalate (PET) via glycolysis is a promising pathway for the circular economy; however, it often requires harsh conditions or expensive catalysts. This study investigates the depolymerization of waste PET using ethylene glycol (EG) mediated by a deep eutectic solvent (DES) synthesized from zinc acetate dihydrate (Zn(OAc)2·2H2O) and urea. The formation of the DES via hydrogen bonding was confirmed by Fourier transform infrared analysis. Experimental results demonstrated a remarkable enhancement in catalytic activity owing to the DES, increasing the depolymerization efficiency from negligible levels (using only EG) to complete conversion. Under optimal conditions, a Zn(OAc)2·2H2O : urea molar ratio of 1 : 1.5, reaction temperature of 180°C, and reaction time of 90 min, the PET conversion reached 100% with a bis(2-hydroxyethyl) terephthalate (BHET) yield of 63.8%. The structure of the recovered BHET monomer was verified by 1Hnuclear magnetic resonance spectroscopy. Furthermore, a synergistic mechanism involving the Lewis acidity of Zn2+ and the hydrogen-bonding network of urea is proposed to explain the superior catalytic performance. This work presents a cost-effective, green and highly efficient protocol for PET waste valorization.

PurposeThis study explores the economic impact of the COVID-19 crisis on herding behaviour in the Australian equity market by considering liquidity, government interventions and sentiment contagion.Design/methodology/approachThis study utilizes a daily dataset of the top 500 stocks in the Australian market from January 2009 to December 2021. Both predictive regression and portfolio approaches are employed to consider the impact of COVID-19 on herding intention.FindingsThis study confirms that herding propensity is more pronounced at the beginning of the crisis and becomes less significant towards later phases when reverse herding is more visible. Investors herd more toward sectors with less available information on financial support from the government during the financial meltdown. Conditioning the stock liquidity, herding is only detectable during highly liquid periods and high-liquid stocks, which is more observable during the initial phases of the crisis. Further, the mood contagion from the United States (US) market to Australian market and asymmetric herding intention are evident during the pandemic.Originality/valueThis is the first study to shed further light on the impact of a health crisis on the trading behaviour of Australian investors, which is driven by liquidity, public information and sentiment. Notwithstanding the theoretical contributions to the prior literature, several practical implications are proposed for businesses, policymakers and investors during uncertainty periods.

The natural ability of stem cells to self-organize into functional tissue has been harnessed for the production of functional human intestinal organoids. Although dynamic mechanical forces play a central role in intestinal development and morphogenesis, conventional methods for the generation of intestinal organoids have relied solely on biological factors. Here, we show that the incorporation of uniaxial strain, using compressed nitinol springs, in human intestinal organoids transplanted into the mesentery of mice induces growth and maturation of the organoids. Assessment of morphometric parameters, transcriptome profiling and functional assays of the strain-exposed tissue revealed higher similarities to native human intestine, with regard to tissue size and complexity, and muscle tone. Our findings suggest that the incorporation of physiologically relevant mechanical cues during the development of human intestinal tissue enhances its maturation and enterogenesis. Uniaxial strain provided by compressed nitinol springs incorporated in human intestinal organoids transplanted into the mouse mesentery enhances organoid growth and maturation, and improves the similarity of the organoids to native human intestine.