Abstract Background Leclercia adecarboxylata is an emerging clinical pathogen within the Enterobacteriaceae family and causes infections in immunocompromised and immunocompetent patients. It frequently exhibits resistance to a broad spectrum of β-lactams as well as to colistin, which is a last-resort antibiotic against multidrug-resistant Gram-negative bacteria. The global spread of mcr genes poses a serious public health concern. This study aimed to examine the prevalence and genetic contexts of mcr genes in clinical Leclercia spp. isolates from China. Results This study identified three mobile colistin resistance genes, namely, mcr-9.1, mcr-9.2, and a novel variant mcr-11.1, among 11 clinical Leclercia isolates. Some of these isolates were assigned to two novel species: Leclercia sp. LecN1 and Leclercia sp. LecN2. mcr-11.1 showed the highest sequence similarity to mcr-9.1 and was located within two novel chromosomal integrative mobile elements (IMEs): Tn6572 and Tn6573. mcr-9.1 was carried by a chromosomally located Tn7-family/Tn6230-subfamily transposon (Tn6574) and by three IncHI2 plasmids: p707804-mcr, p1106151-mcr, and pJ807-mcr. Meanwhile, mcr-9.2 was identified in plasmid pP10164-2. These mcr genes showed diverse local genetic contexts, including mcr-11.1–wbuC–qseCB in Tn6573-family IMEs as well as IS903B–mcr-9.1–wbuC–qseCB–exeA–int–IS26 and its variants within the Tn6230-related regions of Tn6574 and IncHI2 plasmids. Tn1696-related regions carried multiple mobile genetic elements (MGEs), further facilitating the dissemination of various antibiotic resistance genes (ARGs). Conclusions In clinical Leclercia spp. isolates, this study identified a novel colistin resistance gene (mcr-11.1), three novel chromosomal MGEs (including Tn6572–Tn6574), as well as evidence for two new putative species. Along with four identified plasmids, these chromosomal MGEs demonstrated diverse mcr genetic contexts and suggested great potentials for the dissemination of ARGs. This study highlighted a concerning mechanism for mcr gene transfer, providing an urgent indication for ongoing resistance surveillance.
Abstract The molecular classification of endometrial carcinomas (ECs) is now integrated into clinical practice. However, identification of polymerase‐ε (POLE) variants remains reliant on DNA sequencing, which limits broader implementation. Given the strong prognostic value of pathogenic POLE mutations and the established efficacy of immunohistochemistry (IHC) for detecting mismatch repair (MMR) deficiency and p53 abnormalities, there is a clear need for IHC‐based screening strategies to identify patients likely to carry POLE variants and prioritize them for confirmatory sequencing. In this study, we analyzed 24 cases with POLE pathogenic mutations (POLEmut ECs), 3 with benign POLE variants, and 32 matched cases with no specific molecular profile (NSMP) from a cohort of 378 ECs. IHC evaluation of the ataxia telangiectasia mutated (ATM) protein revealed that POLE‐mutated ECs (with pathogenic or benign POLE variants) exhibited significantly higher frequencies of non‐diffuse positive staining patterns, including null, heterogeneous positive, and subclonal loss, compared with NSMP cases. Targeted next‐generation sequencing of all exons across 474 cancer‐related genes in the 27 POLE‐mutated ECs and 20 NSMP cases with ATM non‐diffuse positive staining patterns confirmed that POLE‐mutated ECs typically had high tumor mutational burden and were enriched for ATM truncating variants. ATM molecular alterations, including various variant subtypes and multisite mutations, also closely correlated with these staining patterns. Based on these findings, we refined the ATM IHC interpretation framework to integrate staining patterns with sequencing data for improved molecular correlation. Specifically, the null and subclonal loss patterns showed high specificity (96.9%), positive predictive value (94.1%), and accuracy (79.7%) for identifying POLE variants. Notably, the null pattern appeared exclusively in ECs with pathogenic POLE mutations. These results suggest that ATM IHC staining is an effective screening tool for identifying patients who may benefit from confirmatory POLE sequencing among those lacking MMR deficiency or p53 abnormalities.
Post-traumatic stress disorder (PTSD) is a debilitating psychiatric condition characterized by dysregulated fear memory, emotional disturbances, and impaired cognition, driven by dysfunctions across multiple neural circuits and neuroinflammatory processes. While current treatments demonstrate limited efficacy, repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising non-invasive intervention. This review synthesizes preclinical and clinical evidence, highlighting rTMS mechanisms—including enhanced neuroplasticity, normalizing network connectivity, and reducing neuroinflammation. We then propose five innovative strategies, including accelerated protocols, precision targeting using neuroimaging, neuroinformatic approaches, closed-loop systems, and biomarker-guided treatment. By bridging circuit-level insights with clinical innovation, rTMS offers a transformative approach to PTSD treatment, though standardization and personalized paradigms require further development and validation. Future research should integrate multimodal biomarkers with hybrid therapies to optimize outcomes.
Дмитро Соломатін, Андрій Седляр, Вадим Пасічник
et al.
Сучасна війна відрізняється насиченістю засобами та системами озброєння, які мають різне значення та призначення на всіх етапах збройного конфлікту. Цю війну часто називають війною “безпілотників”. Із широким упровадженням безпілотних авіаційних систем у війська вони почали виконувати важливі тактичні задачі на полі бою. Логіка війни визначає, що після появи нових засобів нападу обов’язково з’являються засоби, які чинять їм протидію. Якщо необхідно забезпечити максимальну ефективність розв’язання задачі стосовно протидії безпілотним авіаційним системам, слід використовувати комбінацію різних засобів: радіолокаційних, радіотехнічних, оптико-електронних, тепловізійних, засобів ураження та радіоелектронного подавлення. За роки війни складність та різноманіття систем управління безпілотними авіаційними системами збільшились у багато разів. У зв’язку із цим знадобилося збільшити й кількість засобів знаходження та подавлення безпілотних авіаційних систем, які налаштовані на різні частоти сигналів управління. Тому питання щодо системи активного захисту броньованої техніки від безпілотних авіаційних систем є актуальним.
Зважаючи на викладене вище, слід зазначити, що ця стаття спрямована на обґрунтування технічних характеристик до систем активного захисту броньованої техніки від FPV-дронів (First Person View) та дронів зі “скидами”.
Провідним методом для дослідження цього питання є метод аналізу, що дозволяє комплексно розглянути та обґрунтувати всі технічні характеристики до систем активного захисту броньованої техніки. Досвід війн та збройних конфліктів сучасності свідчить про те, що ефективність виконання військами (силами) завдань за призначенням цілком залежить від наявності у їхньому складі сучасного озброєння та військової техніки, здатних забезпечити надійне ураження противника та захист свого особового складу в бою. При цьому досвід широкомасштабної російсько-української війни наочно підтверджує істотне змінення форм і способів застосування військ (сил) під впливом безпілотних авіаційних систем, а саме FPV-дронів та дронів зі “скидами”.
У статті обґрунтовано технічні вимоги до системи активного захисту броньованої техніки від FPV-дронів та дронів зі “скидами” за допомогою постановки радіоелектронних перешкод, кінетичного впливу, відстрілу антидронових сіток та протидронових систем з автономними модулями.
Матеріали статті становлять практичну цінність для виробників, які мають досвід розроблення такої зброї, а саме для визначення можливості модернізації своїх спецзасобів та адаптування їх для встановлення на бронетанкове озброєння як активний захист від FPV-дронів та дронів зі “скидами”.
Оleksіі Solomitskyі, Boris Butvin, Andrii Ihnatiev
et al.
The relevance of this research is determined by the fact that the current military and political environment is characterised by high dynamism, unpredictability and the interdependence of a wide range of political, economic, military, social and technological factors. Under conditions of increasing complexity in global and regional processes, there arises a critical need for the development of high-quality forecasts that enable informed decision-making in the field of defence and national security. One of the key tools in such forecasting is expert evaluation. However, the effectiveness of this method is directly dependent on the competence of experts and the objectivity of their judgments. Therefore, the problem of selecting experts with a specified level of competence becomes particularly relevant, as it directly affects the reliability of forecasts and the ability to consider a broad spectrum of factors shaping the military and political environment. The aim of the study is to develop recommendations for the selection of experts with a defined level of competence in order to improve the credibility of forecasting military and political processes. To achieve this objective, the study employs methods of expert evaluation, systems analysis, mathematical modelling, pairwise comparison techniques, aggregation of expert opinions, and approaches to determining integrated indicators of competence. The combination of these methods allows for a comprehensive understanding of the experts' potential capabilities, taking into account their formal characteristics such as position, academic degree and professional experience, while simultaneously verifying their practical ability to work with complex multi-factor forecasting tasks. The research resulted in the development of a methodological approach for assessing expert competence. This approach includes the use of competence coefficients, analysis of judgment consistency, identification of transitivity violations and evaluation of objectivity. A group expert evaluation procedure has been proposed, allowing for the adjustment of expert group composition according to criteria of consistency and assessment quality. This approach helps minimise the influence of subjective factors, increases the reliability of outcomes and ensures more accurate forecasts regarding the development of the military and political situation. The practical orientation of the study lies in the fact that the results can be applied by military command bodies, analytical centres and institutions within the security and defence sector for organising expert studies. They can also be used in the preparation and formation of expert groups engaged in strategic forecasting. The proposed recommendations may prove valuable for researchers working on national security and military forecasting, as well as for practitioners responsible for planning and decision-making in the defence and public administration sectors. These findings provide a foundation for further scientific exploration aimed at improving methods of expert forecasting, integrating analytical models with modern information systems and enhancing tools for assessing competence under conditions of uncertainty.
Yuri L. Starenchenko, Natalia L. Dianova, Svetlana A. Mamayeva
et al.
Analysis of the contribution of prominent representatives of military science schools and areas of the Military Medical Academy (MMA, MNMA) to establishment and development of the Russian military psychology supports an important role of the Academy’s scientists in refining of ideas, and establishment and development of military psychology as a research and practice area. Even in a pre-scientific period of military psychology concepts, medical officers (mainly, general practitioners) were the first to raise and study a number of psychological aspects and issues of military service. Later, the baton was picked up by psychiatrists, namely, Bekhterev’s students: Mikhail Dobrotvorsky, Efim Borishpolsky, Gerasim Shumkov, and others. This contribution remains undervalued and ill-studied as part of the history of psychology, medicine, and Russian history in general. This conclusion prompted us to design and write the paper. To show the role and place of prominent academicians in establishment and development of the Russian military psychology in accordance with the accepted historical periodization. The review uses the analysis and arrangement of thesis researches on interdisciplinary topics, and topics pertaining to military psychological issues. As a result of the analysis of the established Academy military science schools’ activities, seven schools were selected. Their representatives have made the greatest contribution to the development of ideas and areas of military psychology. The activities of a number of military science schools of the Academy allow us to recognize their significant contribution to establishment and methodological support of various areas of training and activities of military psychologists.
Object detection is one of the key target tasks of interest in the context of civil and military applications. In particular, the real-world deployment of target detection methods is pivotal in the decision-making process during military command and reconnaissance. However, current domain adaptive object detection algorithms consider adapting one domain to another similar one only within the scope of natural or autonomous driving scenes. Since military domains often deal with a mixed variety of environments, detecting objects from multiple varying target domains poses a greater challenge. Several studies for armored military target detection have made use of synthetic aperture radar (SAR) data due to its robustness to all weather, long range, and high-resolution characteristics. Nevertheless, the costs of SAR data acquisition and processing are still much higher than those of the conventional RGB camera, which is a more affordable alternative with significantly lower data processing time. Furthermore, the lack of military target detection datasets limits the use of such a low-cost approach. To mitigate these issues, we propose to generate RGB-based synthetic data using a photorealistic visual tool, Unreal Engine, for military target detection in a cross-domain setting. To this end, we conducted synthetic-to-real transfer experiments by training our synthetic dataset and validating on our web-collected real military target datasets. We benchmark the state-of-the-art domain adaptation methods distinguished by the degree of supervision on our proposed train-val dataset pair, and find that current methods using minimal hints on the image (e.g., object class) achieve a substantial improvement over unsupervised or semi-supervised DA methods. From these observations, we recognize the current challenges that remain to be overcome.
Scientific research faces high costs and inefficiencies with traditional methods, but the rise of deep learning and large language models (LLMs) offers innovative solutions. This survey reviews transformer-based LLM applications across scientific fields such as biology, medicine, chemistry, and meteorology, underscoring their role in advancing research. However, the continuous expansion of model size has led to significant memory demands, hindering further development and application of LLMs for science. This survey systematically reviews and categorizes memory-efficient pre-training techniques for large-scale transformers, including algorithm-level, system-level, and hardware-software co-optimization. Using AlphaFold 2 as an example, we demonstrate how tailored memory optimization methods can reduce storage needs while preserving prediction accuracy. By bridging model efficiency and scientific application needs, we hope to provide insights for scalable and cost-effective LLM training in AI for science.
Community science observational datasets are useful in epidemiology and ecology for modeling species distributions, but the heterogeneous nature of the data presents significant challenges for standardization, data quality assurance and control, and workflow management. In this paper, we present a data workflow for cleaning and harmonizing multiple community science datasets, which we implement in a case study using eBird, iNaturalist, GBIF, and other datasets to model the impact of highly pathogenic avian influenza in populations of birds in the subantarctic. We predict population sizes for several species where the demographics are not known, and we present novel estimates for potential mortality rates from HPAI for those species, based on a novel aggregated dataset of mortality rates in the subantarctic.
As a result of the Arab Spring and the return to building the sphere of interest of the Ottoman past, Turkey’s relations with the countries of the North African region are increasingly appreciating, and they seem to be a longer-term vision in Turkish geopolitical thinking. Libya has historically been a part of the Ottoman Empire, a traditionally Turkish sphere of interest in this sense. The study seeks to present the drivers and goals of Turkish foreign policy in relation to a North African state, Libya. In the analysis, examining Turkey’s expansive foreign policy, we can also get an idea of how Ankara intends to increase its sphere of interest in the wider region, namely in the Eastern Mediterranean, through its military support.
Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR's can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed.
As a result of recent advancements in generative AI, the field of data science is prone to various changes. The way practitioners construct their data science workflows is now irreversibly shaped by recent advancements, particularly by tools like OpenAI's Data Analysis plugin. While it offers powerful support as a quantitative co-pilot, its limitations demand careful consideration in empirical analysis. This paper assesses the potential of ChatGPT for data science analyses, illustrating its capabilities for data exploration and visualization, as well as for commonly used supervised and unsupervised modeling tasks. While we focus here on how the Data Analysis plugin can serve as co-pilot for Data Science workflows, its broader potential for automation is implicit throughout.
The development of Courses of Action (COAs) in military operations is traditionally a time-consuming and intricate process. Addressing this challenge, this study introduces COA-GPT, a novel algorithm employing Large Language Models (LLMs) for rapid and efficient generation of valid COAs. COA-GPT incorporates military doctrine and domain expertise to LLMs through in-context learning, allowing commanders to input mission information - in both text and image formats - and receive strategically aligned COAs for review and approval. Uniquely, COA-GPT not only accelerates COA development, producing initial COAs within seconds, but also facilitates real-time refinement based on commander feedback. This work evaluates COA-GPT in a military-relevant scenario within a militarized version of the StarCraft II game, comparing its performance against state-of-the-art reinforcement learning algorithms. Our results demonstrate COA-GPT's superiority in generating strategically sound COAs more swiftly, with added benefits of enhanced adaptability and alignment with commander intentions. COA-GPT's capability to rapidly adapt and update COAs during missions presents a transformative potential for military planning, particularly in addressing planning discrepancies and capitalizing on emergent windows of opportunities.
Heidy Khlaaf, Sarah Myers West, Meredith Whittaker
Discussions regarding the dual use of foundation models and the risks they pose have overwhelmingly focused on a narrow set of use cases and national security directives-in particular, how AI may enable the efficient construction of a class of systems referred to as CBRN: chemical, biological, radiological and nuclear weapons. The overwhelming focus on these hypothetical and narrow themes has occluded a much-needed conversation regarding present uses of AI for military systems, specifically ISTAR: intelligence, surveillance, target acquisition, and reconnaissance. These are the uses most grounded in actual deployments of AI that pose life-or-death stakes for civilians, where misuses and failures pose geopolitical consequences and military escalations. This is particularly underscored by novel proliferation risks specific to the widespread availability of commercial models and the lack of effective approaches that reliably prevent them from contributing to ISTAR capabilities. In this paper, we outline the significant national security concerns emanating from current and envisioned uses of commercial foundation models outside of CBRN contexts, and critique the narrowing of the policy debate that has resulted from a CBRN focus (e.g. compute thresholds, model weight release). We demonstrate that the inability to prevent personally identifiable information from contributing to ISTAR capabilities within commercial foundation models may lead to the use and proliferation of military AI technologies by adversaries. We also show how the usage of foundation models within military settings inherently expands the attack vectors of military systems and the defense infrastructures they interface with. We conclude that in order to secure military systems and limit the proliferation of AI armaments, it may be necessary to insulate military AI systems and personal data from commercial foundation models.
Narrative Science examines the use of narrative in scientific research over the last two centuries. It brings together an international group of scholars who have engaged in intense collaboration to find and develop crucial cases of narrative in science. Motivated and coordinated by the Narrative Science project, funded by the European Research Council, this volume offers integrated and insightful essays examining cases that run the gamut from geology to psychology, chemistry, physics, botany, mathematics, epidemiology, and biological engineering. Taking in shipwrecks, human evolution, military intelligence, and mass extinctions, this landmark study revises our understanding of what science is, and the roles of narrative in scientists' work. This title is also available as Open Access.
Vinod Kumar Chauhan, Anna Ledwoch, Alexandra Brintrup
et al.
Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cause massive economic losses, affect airlines' reputations, waste passengers' time and money, and directly impact the environment. This study adopts a network science approach for solving the delay propagation problem by modeling and analyzing the flight schedules and historical operational data of an airline. We aim to determine the most disruptive airports, flights, flight-connections, and connection types in an airline network. Disruptive elements are influential or critical entities in an airline network. They are the elements that can either cause (airline schedules) or have caused (historical data) the largest disturbances in the network. An airline can improve its operations by avoiding delays caused by the most disruptive elements. The proposed network science approach for disruptive element analysis was validated using a case study of an operating airline. The analysis indicates that potential disruptive elements in a schedule of an airline are also actual disruptive elements in the historical data and they should be considered to improve operations. The airline network exhibits small-world effects and delays can propagate to any part of the network with a minimum of four delayed flights. Finally, we observed that passenger connections between flights are the most disruptive connection type. Therefore, the proposed methodology provides a tool for airlines to build robust flight schedules that reduce delays and propagation.
Doing quantum ethics properly will require detailed socio-political analysis of the technologies and the organizations trying to build them. In this paper, I contribute to this task by analysing the public rhetoric of American military stakeholders in the quantum industry. I look at Air Force Research Laboratory involvement in the 2020 Quantum 2 Business conference, where they were the main sponsor. A critical thematic analysis shows a focus on enacting the violence of war, maintaining narratives that the Air Force provides a secure future for Americans, and marrying quantum technology with the aesthetics of war. I contextualize this with anti-imperialist theory, arguing that this rhetoric and the desire for quantum arms aligns with the reproduction of existing violent power structures. Insights about this example of military involvement in quantum spaces should help orient nascent critical quantum ethics interventions.
Computer science (CS) education finds itself at a pivotal moment to reckon with what it means to accept, use, and create technologies, with the continued recruitment of minoritized students into the field. In this paper, we build on the oral traditions of educating with stories, and take the reader on two journeys. We begin with a story that leads us in thinking about where computer science education is, in the wake of slavery, under the New Jim Code. Within a BlackCrit framework, we shake the grounds of the computer science field, where technologies are often promoted as objective, but reflect and reproduce existing inequalities. In tune with maintaining current systems of power, efforts to broaden participation in computer science have been heavily driven by industry, government, and military interests. These interests ultimately push us farther away from sustainable relations with the earth and with each other, and risk the very lives of the same communities the field claims to help. However, we can rewrite the narratives of the role of technology in our lives. We present a second story in which we place abolitionist theories and practices in conversation with computer science education. In this paper we explore (1) In what ways does computing education support systems that enable Black death? and (2) How might integrating an abolitionist framework into computer science open up possibilities for world-building and dreaming in the name of Black Life? We imagine a different future where computer science is used as a tool in life-affirming, world-building projects. We invite readers to engage with this piece as a part of an active dialogue towards combating anti-Black logics in the field of computer science education.