Identification of novel kinases regulating lung cancer metastasis

Lung cancer is the commonest cancer killer worldwide. The appearance of distant metastasis is one of the main reasons for failing to cure patients with this disease. Thus, understanding the mechanisms regulating lung cancer metastasis should highlight novel therapeutic strategies to improve clinical outcome. We performed an RNA interference screen for cell migration using A549 non-small cell lung cancer (NSCLC) cells. Seventy kinases modulating A549 cell motility were identified, including several members of the ribosomal-S6 kinase (Rsk) family. Indeed, Rsk1 silencing increased, while Rsk2 and Rsk4 downregulation decreased, cell migration. We then assessed the ability of our candidates to regulate A549 cell invasiveness in a 3-D invasion assay. We found that 38 of these similarly regulated cell migration and invasion, including Rsk1 and Rsk4 but not Rsk2. Further work demonstrated that the motility effects of Rsk1, but not Rsk2 or 4, silencing were reproduced in additional NSCLC cell lines. Hence, we focussed on Rsk1 as the principal Rsk isoform regulating NSCLC cell motility. In silico analysis and biochemical experimentation revealed that Rsk1 interacted with the actin regulators Vasp and Mena. This correlated with the ability of Rsk1 to phosphorylate Vasp on Thr-278, a site regulating Vasp-mediated actin dynamics. Furthermore, Vasp and Mena downregulation prevented the migratory effects of Rsk1 silencing. To assess the in vivo relevance of our findings, we developed a zebrafish metastasis model and showed that Rsk1 silencing enhanced the metastatic potential of A549 cells. Moreover, immunohistochemical staining of human isogenically-matched samples demonstrated that Rsk1 expression decreased, while Rsk2 or 4 expression increased, in metastatic versus primary lung cancer lesions. Also, patients with Rsk1-negative primary tumours showed an increased number of metastases. Taken together, our findings establish that Rsk1 is a metastasis suppressor in NSCLC and may be a biomarker for the progression of this disease

Higgs-mode radiance and charge-density-wave order in 2H-NbSe2_2

Despite being usually considered two competing phenomena, charge-density-wave and superconductivity coexist in few systems, the most emblematic one being the transition metal dichalcogenide 2H-NbSe$_2$. This unusual condition is responsible for specific Raman signatures across the two phase transitions in this compound. While the appearance of a soft phonon mode is a well-established fingerprint of the charge-density-wave order, the nature of the sharp sub-gap mode emerging below the superconducting temperature is still under debate. In this work we use the external pressure as a knob to unveil the delicate interplay between the two orders, and consequently the nature of the superconducting mode. Thanks to an advanced extreme-conditions Raman technique we are able to follow the pressure evolution and the simultaneous collapse of the two intertwined charge density wave and superconducting modes. The comparison with microscopic calculations in a model system supports the Higgs-type nature of the superconducting mode and suggests that charge-density-wave and superconductivity in 2H-NbSe$_2$ involve mutual electronic degrees of freedom. These findings fill knowledge gap on the electronic mechanisms at play in transition metal dichalcogenides, a crucial step to fully exploit their properties in few-layers systems optimized for devices applications

Halyomorpha halys (Hemiptera, Pentatomidae): ten years after in Europe

We describe the situation of the invasion of Halyomorpha halys (HH) in Europe, since its discovery in Switzerland in 2007, but with effective presence since 2004. After a relative stagnation for many years, the dispersal of H. halys seems to increase, probably due to growing populations and passive transport by human activities. We suppose that it is not possible to stop the invasion of this species in Europe due to global warming but mostly to ecological characteristics such as high dispersal capability especially with human assistance , a broad host spectrum, a high female fecundity, and a high overwintering survival. The particularly mild winter 2013-2014 in France and Western Europe may further contribute to its progressive dispersal. It is likely that H. halys is already much wider distributed than previously assumed because it is easily confused with the native species, Raphigaster nebulosa. Most recently H. halys was recorded from Hungary, which is nearly 1,000 km east of its centre of distribution in Switzerland. In France, H. halys was first recorded in the Alsace in 2012, but in fall 2013 it was also discovered 400 km further west in Paris and Ile de France. The ongoing dispersal in western France will be monitored and prevention methods will be investigated. After the first occurrence in 2012, a “citizen-science” type of survey allowed to detect many specimens of H. halys in different areas of Northern Italy, with a bigger nucleus centred in the territory of first detection, in the Emilia Romagna region. As this region has extended areas cultivated with high value fruit crops, field monitoring is currently being performed to verify H. halys presence and damage

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Quantifying Risk Factors for Human Brucellosis in Rural Northern Tanzania

Brucellosis is a zoonosis of veterinary, public health and economic significance in most developing countries. Human brucellosis is a severely debilitating disease that requires prolonged treatment with a combination of antibiotics. The disease can result in permanent and disabling sequel, and results in considerable medical expenses in addition to loss of income due to loss of working hours. A study was conducted in Northern Tanzania to determine the risk factors for transmission of brucellosis to humans in Tanzania. This was a matched case-control study. Any patient with a positive result by a competitive ELISA (c-ELISA) test for brucellosis, and presenting to selected hospitals with at least two clinical features suggestive of brucellosis such as headache, recurrent or continuous fever, sweating, joint pain, joint swelling, general body malaise or backache, was defined as a case. For every case in a district, a corresponding control was traced and matched by sex using multistage cluster sampling. Other criteria for inclusion as a control included a negative c-ELISA test result and that the matched individual would present to hospital if falls sick. Multivariable analysis showed that brucellosis was associated with assisted parturition during abortion in cattle, sheep or goat. It was shown that individuals living in close proximity to other households had a higher risk of brucellosis. People who were of Christian religion were found to have a higher risk of brucellosis compared to other religions. The study concludes that assisting an aborting animal, proximity to neighborhoods, and Christianity were associated with brucellosis infection. There was no association between human brucellosis and Human Immunodeficiency Virus (HIV) serostatus. Protecting humans against contact with fluids and tissues during assisted parturition of livestock may be an important means of reducing the risk of transferring brucellosis from livestock to humans. These can be achieved through health education to the communities where brucellosis is common

miR-515-5p controls cancer cell migration through MARK4 regulation

Here, we show that miR-515-5p inhibits cancer cell migration and metastasis. RNA-seq analyses of both oestrogen receptor receptor-positive and receptor-negative breast cancer cells overexpressing miR-515-5p reveal down-regulation of NRAS, FZD4, CDC42BPA, PIK3C2B and MARK4 mRNAs. We demonstrate that miR-515-5p inhibits MARK4 directly 3' UTR interaction and that MARK4 knock-down mimics the effect of miR-515-5p on breast and lung cancer cell migration. MARK4 overexpression rescues the inhibitory effects of miR-515-5p, suggesting miR-515-5p mediates this process through MARK4 down-regulation. Furthermore, miR-515-5p expression is reduced in metastases compared to primary tumours derived from both in vivo xenografts and samples from patients with breast cancer. Conversely, miR-515-5p overexpression prevents tumour cell dissemination in a mouse metastatic model. Moreover, high miR-515-5p and low MARK4 expression correlate with increased breast and lung cancer patients' survival, respectively. Taken together, these data demonstrate the importance of miR-515-5p/MARK4 regulation in cell migration and metastasis across two common cancers

Understanding COVID-19 Dynamics and the Effects of Interventions in the Philippines: A Mathematical Modelling Study

Background COVID-19 initially caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with many high-income countries; possibly because of differing demographics; socioeconomics; surveillance; and policy responses. Here; we investigate the role of multiple factors on COVID-19 dynamics in the Philippines; a LMIC that has had a relatively severe COVID-19 outbreak. Methods We applied an age-structured compartmental model that incorporated time-varying mobility; testing; and personal protective behaviors (through a “Minimum Health Standards” policy; MHS) to represent the first wave of the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon; Central Visayas; and the National Capital Region). We estimated effects of control measures; key epidemiological parameters; and interventions. Findings Population age structure; contact rates; mobility; testing; and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases; hospitalisations; and deaths. The model indicated that MHS reduced the probability of transmission per contact by 13-27%. The February 2021 case detection rate was estimated at ~8%; population recovered at ~9%; and scenario projections indicated high sensitivity to MHS adherence. Interpretation COVID-19 dynamics in the Philippines are driven by age; contact structure; mobility; and MHS adherence. Continued compliance with low-cost MHS should help the Philippines control the epidemic until vaccines are widely distributed; but disease resurgence may be occurring due to a combination of low population immunity and detection rates and new variants of concern

COVID-19 collaborative modelling for policy response in the Philippines, Malaysia and Vietnam

Mathematical models that capture COVID-19 dynamics have supported public health responses and policy development since the beginning of the pandemic, yet there is limited discourse to describe features of an optimal modelling platform to support policy decisions or how modellers and policy makers have engaged with each other. Here, we outline how we used a modelling software platform to support public health decision making for the COVID-19 response in the Western Pacific Region (WPR) countries of the Philippines, Malaysia and Viet Nam. This perspective describes an approach to support evidence-based public health decisions and policy, which may help inform other responses to similar outbreak events. The platform we describe formed the basis for one of the inaugural World Health Organization (WHO) Western Pacific (WPRO) Innovation Challenge awards, and was backed by collaboration between epidemiological modellers, those providing public health advice, and policy makers

Citizen seismology helps decipher the 2021 Haiti earthquake

5 pages, 4 figures, supplementary materials https://doi.org/10.1126/science.abn1045.-- Data and materials availability: All data and code used in this study are openly available. RADAR data can be obtained through ESA (Sentinel) or JAXA (Alos-2). Aftershock data can be obtained from https://ayiti.unice.fr/ayiti-seismes/ (7). The codes used to process or model the data are published and public (8). The catalog of high-precision earthquake relocated with the NLL-SSST-coherence procedure (SM4) is available as supplementary dataOn 14 August 2021, the moment magnitude (Mw) 7.2 Nippes earthquake in Haiti occurred within the same fault zone as its devastating 2010 Mw 7.0 predecessor, but struck the country when field access was limited by insecurity and conventional seismometers from the national network were inoperative. A network of citizen seismometers installed in 2019 provided near-field data critical to rapidly understand the mechanism of the mainshock and monitor its aftershock sequence. Their real-time data defined two aftershock clusters that coincide with two areas of coseismic slip derived from inversions of conventional seismological and geodetic data. Machine learning applied to data from the citizen seismometer closest to the mainshock allows us to forecast aftershocks as accurately as with the network-derived catalog. This shows the utility of citizen science contributing to our understanding of a major earthquakeThis work was supported by the Centre National de la Recherche Scientifique (CNRS) and the Institut de Recherche pour le Développement (IRD) through their “Natural Hazard” program (E.C., S.S., T.M., B.D., F.C., J.P.A., J.C., A.D., D.B., S.P.); the FEDER European Community program within the Interreg Caraïbes “PREST” project (E.C., S.S., D.B.); Institut Universitaire de France (E.C., R.J.); Université Côte d’Azur and the French Embassy in Haiti (S.P.); the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 758210, Geo4D project to R.J. and grant no. 805256 to Z.D.); the French National Research Agency (project ANR-21-CE03-0010 “OSMOSE” to E.C. and ANR-15-IDEX-01 “UCAJEDI Investments in the Future” to Q.B.); the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 949221 to Q.B.); and HPC resources of IDRIS (under allocations 2020-AD011012142, 2021-AP011012536, and 2021-A0101012314 to Q.B.With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewe