George C. Marshall, A Dynamic Leader of Transition & Adaptation

George Catlett Marshall was the Chief of Staff of the United States Army during the tumultuous years of the Second World War. Prior to the war, Marshall headed various officers’ schools and professional development centers, mentoring an entire generation of young officers who would become field commanders and general officers during the World War II. Eventually, he oversaw the monumental task of modernizing and enlarging the United States Army as World War II began and escalated. Together with President Franklin D. Roosevelt and his opposites in the British military, he helped formulate the grand strategy that the Allied powers implemented to eventually defeat Fascist Italy, Nazi Germany, and Imperial Japan

Introduction to special issue on "Long-term changes and trends in the stratosphere, mesosphere, thermosphere and ionosphere"

This special issue bundles some of the latest results on decadal-scale variations in the stratosphere, mesosphere, thermosphere, and ionosphere, following on from the 8th Workshop on Long-Term Changes and Trends in the Atmosphere, held in Cambridge, UK, on 28–31 July 2014. Emmert et al. (2015) provided a short report of the workshop. This introduction briefly describes the relevance of the field and highlights some of the recent progress that has been mad

The development and internal validation of a multivariable model predicting 6-month mortality for people with opioid use disorder presenting to community drug services in England: a protocol

BackgroundPeople with opioid use disorder have substantially higher standardised mortality rates compared to the general population; however, lack of clear individual prognostic information presents challenges to prioritise or target interventions within drug treatment services. Previous prognostic models have been developed to estimate the risk of developing opioid use disorder and opioid-related overdose in people routinely prescribed opioids but, to our knowledge, none have been developed to estimate mortality risk in people accessing drug services with opioid use disorder. Initial presentation to drug services is a pragmatic time to evaluate mortality risk given the contemporaneous routine collection of prognostic indicators and as a decision point for appropriate service prioritisation and targeted intervention delivery. This study aims to develop and internally validate a model to estimate 6-month mortality risk for people with opioid use disorder from prognostic indicators recorded at initial assessment in drug services in England.MethodsAn English national dataset containing records from individuals presenting to drug services between 1 April 2013 and 1 April 2023 (n > 800,000) (the National Drug Treatment Monitoring System (NDTMS)) linked to their lifetime hospitalisation and death records (Hospital Episode Statistics-Office of National Statistics (HES-ONS)). Twelve candidate prognostic indicator variables were identified based on literature review of demographic and clinical features associated with increased mortality for people in treatment for opioid use disorder. Variables will be extracted at initial presentation to drug services with mortality measured at 6 months. Two multivariable Cox regression models will be developed one for 6-month all-cause mortality and one for 6-month drug-related mortality using backward elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of both models will be reported using Harrel’s c and d-statistics. Calibration curves and slopes will be presented comparing expected and observed event rates.DiscussionThe models developed and internally validated in this study aim to improve clinical assessment of mortality risk for people with opioid use disorder presenting to drug services in England. External validation in different populations will be required to develop the model into a tool to assist future clinical decision-making

Harnessing naturally randomized transcription to infer regulatory relationships among genes

An approach is developed that utilizes randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level. The approach is applied to an experiment in yeast, yielding new insights into the topology of the yeast transcriptional regulatory network

Relevance of different prior knowledge sources for inferring gene interaction networks

When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structured databases or research articles. Traditionally, inferred networks are validated against these known interactions. Whenever the recovery rate is gauged to be high enough, subsequent high scoring but unknown inferred interactions are deemed good candidates for further experimental validation. Therefore such validation framework strongly depends on the quantity and quality of published interactions and presents serious pitfalls: (1) availability of these known interactions for the studied problem might be sparse; (2) quantitatively comparing different inference algorithms is not trivial; and (3) the use of these known interactions for validation prevents their integration in the inference procedure. The latter is particularly relevant as it has recently been showed that integration of priors during network inference significantly improves the quality of inferred networks. To overcome these problems when validating inferred networks, we recently proposed a data-driven validation framework based on single gene knock-down experiments. Using this framework, we were able to demonstrate the benefits of integrating prior knowledge and expression data. In this paper we used this framework to assess the quality of different sources of prior knowledge on their own and in combination with different genomic data sets in colorectal cancer. We observed that most prior sources lead to significant F-scores. Furthermore, their integration with genomic data leads to a significant increase in F-scores, especially for priors extracted from full text PubMed articles, known co-expression modules and genetic interactions. Lastly, we observed that the results are consistent for three different data sets: experimental knock-down data and two human tumor data sets

Evidence for stratospheric sudden warming effects on the upper thermosphere derived from satellite orbital decay data during 1967–2013

We investigate possible impact of stratospheric sudden warmings (SSWs) on the thermosphere by using long-term data of the global average thermospheric total mass density derived from satellite orbital drag during 1967–2013. Residuals are analyzed between the data and empirical Global Average Mass Density Model (GAMDM) that takes into account density variability due to solar activity, season, geomagnetic activity, and long-term trend. A superposed epoch analysis of 37 SSW events reveals a density reduction of 3–7% at 250–575 km around the time of maximum polar vortex weakening. The relative density perturbation is found to be greater at higher altitudes. The temperature perturbation is estimated to be −7.0 K at 400 km. We show that the density reduction can arise from enhanced wave forcing from the lower atmosphere

Gotcha! I Know What You are Doing on the FPGA Cloud: Fingerprinting Co-Located Cloud FPGA Accelerators via Measuring Communication Links

In recent decades, due to the emerging requirements of computation acceleration, cloud FPGAs have become popular in public clouds. Major cloud service providers, e.g. AWS and Microsoft Azure have provided FPGA computing resources in their infrastructure and have enabled users to design and deploy their own accelerators on these FPGAs. Multi-tenancy FPGAs, where multiple users can share the same FPGA fabric with certain types of isolation to improve resource efficiency, have already been proved feasible. However, this also raises security concerns. Various types of side-channel attacks targeting multi-tenancy FPGAs have been proposed and validated. The awareness of security vulnerabilities in the cloud has motivated cloud providers to take action to enhance the security of their cloud environments. In FPGA security research papers, researchers always perform attacks under the assumption that attackers successfully co-locate with victims and are aware of the existence of victims on the same FPGA board. However, the way to reach this point, i.e., how attackers secretly obtain information regarding accelerators on the same fabric, is constantly ignored despite the fact that it is non-trivial and important for attackers. In this paper, we present a novel fingerprinting attack to gain the types of co-located FPGA accelerators. We utilize a seemingly non-malicious benchmark accelerator to sniff the communication link and collect performance traces of the FPGA-host communication link. By analyzing these traces, we are able to achieve high classification accuracy for fingerprinting co-located accelerators, which proves that attackers can use our method to perform cloud FPGA accelerator fingerprinting with a high success rate. As far as we know, this is the first paper targeting multi-tenant FPGA accelerator fingerprinting with the communication side-channel.Comment: To be published in ACM CCS 202

Seasonal dependence of northern high‐latitude upper thermospheric winds: A quiet time climatological study based on ground‐based and space‐based measurements

This paper investigates the large‐scale seasonal dependence of geomagnetically quiet time, northern high‐latitude F region thermospheric winds by combining extensive observations from eight ground‐based (optical remote sensing) and three space‐based (optical remote sensing and in situ) instruments. To provide a comprehensive picture of the wind morphology, data are assimilated into a seasonal empirical vector wind model as a function of season, latitude, and local time in magnetic coordinates. The model accurately represents the behavior of the constituent data sets. There is good general agreement among the various data sets, but there are some major offsets between GOCE and the other data sets, especially on the duskside. The assimilated wind patterns exhibit a strong and large duskside anticyclonic circulation cell, sharp latitudinal gradients in the duskside auroral zone, strong antisunward winds in the polar cap, and a weaker tendency toward a dawnside cyclonic circulation cell. The high‐latitude wind system shows a progressive intensification of wind patterns from winter to equinox to summer. The latitudinal extent of the duskside circulation cell does not depend strongly on season. Zonal winds show a mainly diurnal variation (two extrema) around polar and middle latitudes and semidiurnal variation (four extrema) at auroral latitudes; meridional winds are primarily diurnal at all high latitudes. The strength of zonal winds channeling through the auroral zone on the duskside is strongest in the summer season. The vorticity of the wind pattern increases from winter to summer, whereas divergence is maximum in equinox. In all three seasons, divergence is weaker than vorticity.Key PointsFirst ever investigation of the large‐scale seasonal dependence of northern high‐latitude upper thermospheric winds in magnetic coordinatesResults show progressive intensification of wind circulation from winter to equinox to summerThe vorticity increases from winter to summer. In all the seasons, the strongest divergences occur primarily in and above auroral latitudesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136373/1/jgra53329.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136373/2/jgra53329_am.pd

On the importance of neutral composition and temperature measurements in the 100-200 km altitude region

Currently, thermospheric species densities and temperatures between &sim;100 and 200&nbsp;km are not known to the accuracy needed to fully characterize how the thermosphere transitions from a well-mixed atmosphere to a diffusively separated atmosphere with zero temperature gradient. This greatly inhibits scientific discovery attainable from either models or observations in this region, especially the understanding of mechanisms that drive thermosphere and ionospheric variability from space weather to climatological time scales. The purpose of this paper is to highlight the importance and critical need for new, global, height-resolved neutral composition (O, O2, N2) and temperature measurements in the new ignorosphere: the 100&ndash;200 km region of the thermosphere. We conclude with observation recommendations and requirements for new comprehensive composition and temperature measurements in the 100&ndash;200 km altitude region that would lead to significant advances in thermosphere-ionosphere science, space weather, and space climate. &nbsp;</p