Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study

Data accessibility: This article has no additional data.Declaration of AI use: We have not used AI-assisted technologies in creating this article.Footnotes: One contribution of 10 to a discussion meeting issue ‘Causes and consequences of stochastic processes in development and disease’ [see: https://doi.org/10.1098/rstb/379/1900].Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue ‘Causes and consequences of stochastic processes in development and disease’.No specific funding for this study. Work in the Pina lab is funded by the Lady Tata Memorial Trust, by the Little Princess Trust through the Children’s Cancer and Leukaemia Group, and by an MRC Pilot Scheme / Brunel University London Research Development Fund award

M-FISH evaluation of chromosome aberrations to examine for historical exposure to ionising radiation due to participation at British nuclear test sites

All data that support the findings of this study are included within the article (and any supplementary files).Data availability statement: All data that support the findings of this study are included within the article (and any supplementary files) available online at: https://doi.org/10.1088/1361-6498/ad1743 .Veterans of the British nuclear testing programme represent a population of ex-military personnel who had the potential to be exposed to ionising radiation through their participation at nuclear testing sites in the 1950s and 1960s. In the intervening years, members of this population have raised concerns about the status of their health and that of their descendants, as a consequence. Radiation dose estimates based on film badge measurements of external dose recorded at the time of the tests suggest any exposure to be limited for the majority of personnel, however, only ∼20% of personnel were monitored and no measurement for internalised exposure are on record. Here, to in-part address families concerns, we assay for chromosomal evidence of historical radiation exposure in a group of aged nuclear test (NT) veterans, using multiplex in situ hybridisation (M-FISH), for comparison with a matched group of veterans who were not present at NT sites. In total, we analysed 9379 and 7698 metaphase cells using M-FISH (24-colour karyotyping) from 48 NT and 38 control veteran samples, representing veteran servicemen from the army, Royal Airforce and Royal Navy. We observed stable and unstable simple- and complex-type chromosome aberrations in both NT and control veterans' samples, however find no significant difference in yield of any chromosome aberration type between the two cohorts. We do observe higher average frequencies of complex chromosome aberrations in a very small subset of veterans previously identified as having a higher potential for radiation exposure, which may be indicative of internalised contamination to long-lived radionuclides from radiation fallout. By utilising recently published whole genome sequence analysis data of a sub-set of the same family groups, we examined for but found no relationship between paternal chromosome aberration burden, germline mutation frequency and self-reported concerns of adverse health in family members, suggesting that the previously reported health issues by participants in this study are unlikely to be associated with historical radiation exposure. We did observe a small number of families, representing both control and NT cohorts, showing a relationship between paternal chromosome aberrations and germline mutation sub-types which should be explored in future studies. In conclusion, we find no cytogenetic evidence of historical radiation exposure in the cohort of nuclear veterans sampled here, offering reassurance that attendance at NTs sites by the veterans sampled here, was not associated with significant levels of exposure to radiation.Nuclear Community Charity Fund (NCCF) through funds received by The Armed Forces Covenant Fund Trust under the Aged Veterans Fund Grants AVF15A and AVF16. The funding organization had no role in the design and conduct of the study; in the collection, management, analysis and interpretation of the data; or in the preparation, review or approval of the manuscript

A plausibility database summarizing the level of evidence regarding the hazards induced by the exposome on children health

Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S143846392300202X#appsec1 .Copyright © 2023 The Authors. Childhood diseases correspond to major public health issues. A large number of studies using different approaches provide evidence regarding effects of environmental exposures, encompassed in the exposome, on children's health. We aimed to summarize the overall level of evidence (LoE) from all streams of evidence regarding exposome effects on child health. For 88 selected chemical and urban factors, we retrieved the conclusions of agency reports or literature reviews published between 2015 and 2021 regarding effects on child health, including cardiovascular, metabolic, neurodevelopmental, respiratory and other health outcomes. Adapted versions of PRISMA flowchart and AMSTAR-2 tool were used to select and assess the quality of the systematic reviews retrieved from PubMed and SCOPUS databases. For each factor-outcome pair, conclusions in three streams of evidence (epidemiological, toxicological and mechanistic, the latter corresponding to in vitro and in silico approaches) were translated into stream-specific LoEs and then combined into an overall LoE ranging from “very unlikely” to “very likely”. The 88 environmental factors were implied in 611 factor-outcome pairs. Forty-four pairs (7%), corresponding to 16 factors, had a very likely overall LoE (≥80%); 127 pairs (21%), corresponding to 49 factors, had a likely or more overall LoE (≥60%). For 81 pairs (13%), no evidence was available in agency reports or published reviews, while for 275 pairs (45%), corresponding to 68 factors, the overall LoE was very unlikely (<20%). Exposure factors with the greatest number of associated health outcomes with a high overall LoE were HCB, PCBs, temperature (8 outcomes), PFOA (7 outcomes), PFOS, cotinine (6 outcomes), arsenic, lead (5 outcomes), bisphenols A and S, PFNA and PM2.5 (4 outcomes), DDT, DDE and DDD, PFHxA, PFDA, green space, UV radiation (3 outcomes). We developed an approach to extract and summarize the existing evidence about effects of environmental factors on health. The plausibility database built for children's health can be used to identify research gaps, conduct quantitative risk assessment studies. It could be expanded to consider a larger fraction of the exposome and other age groups and should be updated on a regular basis.The ATHLETE project was funded by The European Commission, through its Horizon 2020 Framework Program for Research and Innovation (grant agreement 874583). This work was also supported by HERA (Integrating Environment and Health Research: a Vision for the EU) Horizon 2020 project (grant agreement 825417). We acknowledge support from the grant CEX 2018-000806-S funded by MCIN/AEI/10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program

A novel optimal allocation of STATCOM to enhance voltage stability in power networks

Crown Copyright © 2024 The Authors. Utilizing a static synchronous compensator (STATCOM) in the electrical power grid greatly improves the grid's voltage profile by enhancing voltage stability. This article proposes a novel approach based on Mixed Integer Distributed Ant Colony Optimization (MIDACO) to determine the optimal STATCOM installation in the electrical power grid. This approach has two control variables to optimize: the STATCOM size and location. This optimization aims to enhance voltage stability with minimum cost by minimizing two objectives: the voltage deviation index and the STATCOM cost. Also, this article presents a sensitivity analysis to show the stochastic nature of MIDACO and to explain the effect of MIDACO parameters on the optimization approach and the process of reaching the optimal solution. The proposed method has been evaluated on three standard test systems: IEEE 14-bus, IEEE 57-bus, and IEEE 118-bus. In addition, the MIDACO results are compared to those of the artificial bee colony algorithm, the genetic algorithm, and particle swarm optimization.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

Brexit-like rhetoric on immigration no longer works

BlogAll articles posted on this blog give the views of the author(s), and not the position of LSE British Politics and Policy, nor of the London School of Economics and Political Science. Image credit: 1000 words on ShutterstockThe Government’s rhetoric on curbing immigration, and “stopping the small boats” in particular, has strong echoes of the Brexit campaign. As Matilde Rosina and Cristina Juverdeanu point out, the same themes, keywords, and even graphics are being used by the Government that were originally used by Brexit campaigners. The only difference is, this time the campaign doesn’t seem to be working

MDEmoNet: A Multimodal Driver Emotion Recognition Network for Smart Cockpit

The automotive smart cockpit is an intelligent and connected in-vehicle consumer electronics product. It can provide a safe, efficient, comfortable, and enjoyable human-machine interaction experience. Emotion recognition technology can help the smart cockpit better understand the driver's needs and state, improve the driving experience, and enhance safety. Currently, driver emotion recognition faces some challenges, such as low accuracy and high latency. In this paper, we propose a multimodal driver emotion recognition model. To our best knowledge, it is the first time to improve the accuracy of driver emotion recognition by using facial video and driving behavior (including brake pedal force, vehicle Y-Axis position and Z-Axis position) as inputs and employing a multi-Task training approach. For verification, the proposed scheme is compared with some mainstream state-of-The-Art methods on the publicly available multimodal driver emotion dataset PPB-Emo.10.13039/501100001809-National Natural Science Foundation of China; 10.13039/100006190-Research and Development; 10.13039/501100003009-Science and Technology Development Fund

Changes in social norms during the early stages of the COVID-19 pandemic across 43 countries

Data availability: The data generated in this study have been deposited in the Open Science Framework (https://doi.org/10.17605/OSF.IO/STKFR). Non-experimental data included in our datasets (i.e., intensity of government response to COVID-19 is the Stringency Index, COVID-19 deaths and cases per million) are taken from the Oxford COVID−19 Government Response Tracker [22 Hale, T. et al. A global panel database of pandemic policies (Oxford COVID−19 Government Response Tracker). Nat. Human Behav. https://doi.org/10.1038/s41562-021-01079-8 (2021).] and Our World in Data [38 Ritchie, H. et al. Coronavirus Pandemic (COVID-19). Our World in Data. https://ourworldindata.org/coronavirus (2020).] (downloaded November 2020). Wave 0 data are from [3 Gelfand, M. J. et al. Differences between tight and loose cultures: a 33-nation study. Science 332, 1100–1104 (2011).[ and Wave 1 data are from [5 Eriksson, K. et al. Perceptions of the appropriate response to norm violation in 57 societies. Nat. Commun. 12, 1481 (2021).].Code availability: The survey and analysis code are available at the Open Science Framework (https://doi.org/10.17605/OSF.IO/STKFR).Supplementary information is available online at: https://www.nature.com/articles/s41467-024-44999-5#Sec40 .The emergence of COVID-19 dramatically changed social behavior across societies and contexts. Here we study whether social norms also changed. Specifically, we study this question for cultural tightness (the degree to which societies generally have strong norms), specific social norms (e.g. stealing, hand washing), and norms about enforcement, using survey data from 30,431 respondents in 43 countries recorded before and in the early stages following the emergence of COVID-19. Using variation in disease intensity, we shed light on the mechanisms predicting changes in social norm measures. We find evidence that, after the emergence of the COVID-19 pandemic, hand washing norms increased while tightness and punishing frequency slightly decreased but observe no evidence for a robust change in most other norms. Thus, at least in the short term, our findings suggest that cultures are largely stable to pandemic threats except in those norms, hand washing in this case, that are perceived to be directly relevant to dealing with the collective threat.Knut and Wallenberg Grant “How do human norms form and change?” 2016.0167. (G.An.). The Swedish Research Council grant “Norms & Risk: Do social norms help dealing with collective threats” 2021-06271 (G.An.). Ministero dell’Istruzione dell’Università e della Ricerca, PRIN 2017, prot. 20178TRM3F (D.B.). Universidad de Los Andes, Fondo Vicerrectoría de Investigaciones (J.-C.C.). Ministry of Innovation and Technology of Hungary, National Research, Development and Innovation Fund NKFIH-OTKA K135963 (M.F.). Grant 23-061770 S of the Czech Science Foundation (M.H. and S.G.). RVO: 68081740 of the Institute of Psychology, Czech Academy of Sciences (M.H. and S.G.). RA Science Committee, research project N.20TTSH-070 (A.Gr. and N.Khac.). Open University of Israel, 511687 (R.N.). HSE University Basic Research Program (E.O.). Project BASIC (PID2022-141802NB-I00) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” (A.Sá.). US Army Research Office Grant W911NF-19-1-910281 (B.S.). Netherlands Organisation for Scientific Research, 019.183SG.001 (E.S.). Netherlands Organisation for Scientific Research, VI.Veni.201 G.013 (E.S.). European Commission, Horizon 2020-ID 870827 (E.S.). UKRI Grant “Secret Power” No. EP/X02170X/1 awarded under the European Commission’s “European Research Council - STG” Scheme (G.A.T.)

Renewable energy sources integration via machine learning modelling: A systematic literature review

The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources —solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided

E-service failure and recovery strategy in times of crisis: effect on peer attitudes, expectation and future intention

JEL classification: M0This study analyses the impact of the critical issues on Travel and Tourism e-service failure and explores specifically how peer-to-peer accommodation business can cope with the potential collapse in demand caused by global crises. The purpose is to examine the impact of peer-to-peer accommodation’s recovery offer on revisiting intentions and relationships termination in light of justice-, fairness-, and attribution theory. In this vein, the main aim is to develop a theoretical model which is underpinned by an understanding of the consequences of e-service failure and the effectiveness of recovery strategies for business competitiveness. To gauge peer perceptions of peer-to-peer accommodations, we employed a mixed-method approach. Alongside 17 interviews with peers and industry experts, a survey involving 404 peer-to-peer accommodation users was conducted. Structural equation modelling was applied to unravel the intricate relationships and influences at play. The findings suggest that managers and service providers need to focus on timely recovery and building stronger relationships with peers, to increase repurchase intention and post-recovery satisfaction and to better front the crises times. This could be implemented efficiently via the platform of social media. This study offers specific theoretical and practical implications by providing a fair recovery strategy to result in the satisfaction of both parties.Università degli Studi di Salerno within the CRUI-CARE Agreement

Reliable uncertainties of tests and surveys – a data-driven approach

MSC Classification 60J10, 91Exx, 91E45, 05A18.Supplementary material are available online at: https://www.metrology-journal.org/10.1051/ijmqe/2023018/olm . The article is accompanied by supplementary information that includes proofs to the theorems that are stated within the text of the article; linking our advanced methods to extant congeneric methods in the literature; comparison of the methods discussed herein, for partitioning a set of integers into 2 subsets and presentation of results on simulated data.Policy decisions are often motivated by results attained by a cohort of responders to a survey or a test. However, erroneous identification of the reliability or the complimentary uncertainty of the test/survey instrument, will distort the data that such policy decisions are based upon. Thus, robust learning of the uncertainty of such an instrument is sought. This uncertainty is parametrised by the departure from reproducibility of the data comprising responses to questions of this instrument, given the responders. Such departure is best modelled using the distance between the data on responses to questions that comprise the two similar subtests that the given test/survey can be split into. The paper presents three fast and robust ways for learning the optimal-subtests that a given test/survey instrument can be spilt into, to allow for reliable uncertainty of the given instrument, where the response to a question is either binary, or categorical − taking values at multiple levels − and the test/survey instrument is realistically heterogeneous in the correlation structure of the questions (or items); prone to measuring multiple traits; and built of small to a very large number of items. Our methods work in the presence of such messiness of real tests and surveys that typically violate applicability of conventional methods. We illustrate our new methods, by computing uncertainty of three real tests and surveys that are large to very-large in size, subsequent to learning the optimal subtests.There is no funding to be reported