Action Network Research Articles

USP51/PD-L1/ITGB1-deployed juxtacrine interaction plays a cell-intrinsic role in promoting chemoresistant phenotypes in non-small cell lung cancer.

Programmed death ligand 1 (PD-L1) has been demonstrated to facilitate tumor progression and therapeutic resistance in an immune-independent manner. Nevertheless, the function and underlying signaling network(s) of cancer cell-intrinsic PD-L1 action remain largely unknown. Herein, we sought to better understand how ubiquitin-specific peptidase 51 (USP51)/PD-L1/integrin beta-1 (ITGB1) signaling performs a cell-intrinsic role in mediating chemotherapeutic resistance in non-small cell lung cancer (NSCLC). Western blotting and flow cytometry were employed for PD-L1 detection in NSCLC cell lines. Coimmunoprecipitation and pulldown analyses, protein deubiquitination assay, tissue microarray, bioinformatic analysis and molecular biology methods were then used to determine the significance of PD-L1 in NSCLC chemoresistance and associated signaling pathways in several different cell lines, mouse models and patient tissue samples. Ubiquitin-7-amido-4-methylcoumarin (Ub-AMC)-based deubiquitinase activity, cellular thermal shift and surface plasmon resonance (SPR) analyses were performed to investigate the activity of USP51 inhibitors. We provided evidence that cancer cell-intrinsic PD-L1 conferred the development of chemoresistance by directly binding to its membrane-bound receptor ITGB1 in NSCLC. At the molecular level, PD-L1/ITGB1 interaction subsequently activated the nuclear factor-kappa B (NF-κB) axis to elicit poor response to chemotherapy. We further determined USP51 as a bona fide deubiquitinase that targeted the deubiquitination and stabilization of the PD-L1 protein in chemoresistant NSCLC cells. Clinically, we found a significant direct relationship between the USP51, PD-L1 and ITGB1 contents in NSCLC patients with chemoresistant potency. The elevated USP51, PD-L1 and ITGB1 levels were strongly associated with worse patient prognosis. Of note, we identified that a flavonoid compound dihydromyricetin (DHM) acted as a potential USP51 inhibitor and rendered NSCLC cells more sensitive to chemotherapy by targeting USP51-dependent PD-L1 ubiquitination and degradation in vitro and in vivo. Together, our results demonstrated that the USP51/PD-L1/ITGB1 network potentially contributes to the malignant progression and therapeutic resistance in NSCLC. This knowledge is beneficial to the future design of advanced cancer therapy.

THE HISTORY OF THE RESCUE OF THE MONUMENT OF INDUSTRIAL HERITAGE "SEVERSKAYA DOMNA" IN THE 1960-1980S

The article reconstructs the struggle of the inhabitants of two Ural cities - Sverdlovsk and Polevskoy - for the preservation of a unique monument of industrial heritage - the Severskaya blast furnace - in the 1960s-1980s. The sources for the study were unpublished archival materials, as well as information from the periodical press. The author analyzes the reasons that served as an impetus for this activity, which included a number of stages. The main actors of blast furnace museumification are considered. The author comes to the conclusion that the activities for the preservation of monuments of industrial heritage in the 1960s-1980s. was non-systematic, representing a network of inconsistent actions of different actors. Among the latter, the following should be singled out: representatives of the scientific community, educational institutions, members of the public organization «All-Russian Society for the Protection of Historical and Cultural Monuments», local authorities, representatives of party cells, workers and veterans of the Seversky Pipe Plant. During the specified chronological period, the unique monument of the Sverdlovsk region was museumified twice, with a break for a fierce struggle for its preservation in the 1980s. By the beginning of the 1990s. it became only the second industrial site to undergo serious conservation work within the largest old industrial region of our country.

Ship energy scheduling with DQN-CE algorithm combining bi-directional LSTM and attention mechanism

At present, Ship energy scheduling based on deep reinforcement learning (DRL) is an important research direction in which Deep Q learning algorithm (DQN) has been successfully applied in the field of energy scheduling. In terms of the shortcomings of DQN in all electric ships(AES) energy scheduling, such that, the insufficient performance of using multiplayer perceptron(MLP) as action network and the degradation of convergence performance due to over estimation of Q value, in this paper, we propose a DQN cross entropy(DQN-CE) energy scheduling algorithm combing bi-directional LSTM and attention mechanism(BI-LSTM-Att). Instead of using MLP, firstly, we use BI-LSTM-Att as action network of DQN, and then an improved DRL algorithm called DQN-CE is proposed, that is, we add cross-entropy loss of predicted action of the next time for the target network and the action network to DQN. Finally, we conduct simulation experiments on an all-electric ferry, and the experimental results show that the energy scheduling algorithm proposed in this paper reduces the economic consumption by 4.11%, increases the utilization rate of energy storage system by 24.4%, and reduces the exploration time of agent training by 31.3% compared with the original DQN energy scheduling algorithm. Finally, the effectiveness and superiority of the algorithm were further verified in the new case study.

The community engagement course and action network: strengthening community and academic research partnerships to advance health equity.

Historically Black Colleges and Universities and Minority Serving Institutions are uniquely positioned to implement community-campus research partnerships based on a history of service, the pursuit of community trustworthiness and student demographics often similar to surrounding marginalized communities. The Morehouse School of Medicine Prevention Research Center collaborates with members of Historically Black Colleges and Universities, Minority Serving Institutes, and community organizations on the Community Engaged Course and Action Network. This network is the first of its kind and aims to strengthen members' ability to implement Community-Based Participatory Research (CBPR) principles and partnerships. Projects address public health priorities including mental health among communities of color, zoonotic disease prevention, and urban food deserts. To assess the effectiveness of the network, a Participatory Evaluation framework was implemented to conduct process evaluation which included review of partnership structures, operations, project implementation processes, and preliminary outcomes of the research collaborations. A focus group of Community Engagement Course and Action Network members (community and academic) was also conducted to identify benefits and challenges of the network with emphasis on key areas for improvement to further enhance the relationships between partners and to facilitate their subsequent community-campus research. Network improvements were tied to themes strengthening community-academic partnerships including sharing and fellowship, coalition building and collaboration, and greater connections and awareness of community needs through their current community-academic partnerships. The need to conduct ongoing evaluation during and after implementation, for determining the early adoption of CBPR approaches was also identified. Evaluation of the network's processes, infrastructure, and operation provides early lessons learned to strengthen the network. Ongoing assessment is also essential for ensuring continuous quality improvement across partnerships such as determining CBPR fidelity, assessing partnership synergy, and dynamics, and for quality improvement of research protocol. The implications and potential for advancing implementation science through this and similar networks are great towards advancing leadership in modeling how foundations in community service can advance to CBPR partnership formation and ultimately, health equity approaches, that are local defined and assessed.

Climate Change Policy of India: G20 Presidency and Climate Action

One of the greatest challenges for humanity is how to address climate change, in a coordinated manner globally. Address this catastrophe requires a massive effort that needs to be undertaken now, not later. IPCC report suggests two step that would be necessary to avoid this disastrous trend of climate change, first, industrialized nations would have to reduce CO2 emissions by half by 2030, and second is to stop adding CO2 to atmosphere altogether by the early 2050s. So, the world needs to consciously uncouple from fossil fuel to more toward a low carbon future. Much of the intended transition far from fossil fuel depends on G20 countries, which accounts for 80% of global GDP, avails 75% of international trade, and collectively consume 85% of the world’s coal, oil, and natural gas annually. Recently, India has been ranked amongst top five countries in the world, and the best among the G20 countries, based on its Climate Change Performance. India jumps two spots higher, and is now ranked 8th as per Climate Change Performance Index (CCPI), 2023 published by German Watch, New Climate Institute and Climate Action Network International based in Germany. The aggressive policies of India towards climate change, and rapid deployment of renewables and robust framework for energy efficiency programs have shown considerable impact. As per CCPI report, India is on track to meet its 2030 emission targets. Currently, India has hosting its presidency toward G20 meeting 2023, and it will be an opportune time to show the world, about its climate mitigation policies such as deployment of renewable sources of energy and other energy transition programmes. The top 10 rank globally reflects that India is implementing energy transition programmes such as renewable capacity installation at much faster rate than anywhere in the world.

Optimal reconstruction of constrained janbu method with ADP and non-integral safety factor

Notwithstanding the rich research on the slicing method, it is still impossible to accomplish the limit equilibrium analysis of all phases before the evaluation of the safety factor. Bearing this dilemma in view, in this paper, the Janbu method is optimized and reformulated by introducing adaptive dynamic programming (ADP). The fractional average safety factor is no longer used; instead, a sequence of safety factors is used. The critical slip state is chosen as the equilibrium point, the nonlinearity of safety coefficients of adjacent blocks is approximated by the neural network, and the remaining variables and constraints of the system are added to assist in transforming the original redundant safety coefficient evaluation-finding optimal solution problem into a Janbu-qualified cost function. The action network and critic network of ADP accomplish the boundary trajectory solving and safety evaluation, respectively. The validity of the method is verified when the optimized and reorganized framework is applied to solve the critical slip surface of a clay slope.

Data 4 Climate Action Edinburgh

Data 4 Climate Action Edinburgh is a group of volunteers, donating our time and skills with data to support climate action and biodiversity in and around Edinburgh. The formation of the group was the idea of Pauline Ward, inspired on hearing discussions about the how climate action groups could be supported, and how they could help each other, through regional networks. Discussions with the Edinburgh Voluntary Organisations Council and the Scottish Secretariat for Climate Action Networks identified a role for D4CAE as part of such a network in Edinburgh. The Scottish Government has been working on a programme of support for such regional networks, the first being North East Scotland Climate Action Network. Specifically, there appeared to be great potential in facilitating the making of connections between local climate groups and prospective volunteers from among the staff and students of the University of Edinburgh. With practical support and dialogue with the University of Edinburgh Sustainability team and Community Engagement team, D4CAE carried out a survey of members of the public at the Edinburgh Climate Festival 2022. Underneath the University gazebo, individuals volunteered to help and talked with us about what areas of work would be most worthwhile in their view. The participants indicated their choices by placing chips into paper cups labelled with the options ‘mapping’, ‘sharing’ etc. The top result was ‘storytelling with data’. Volunteers asked us to create a mailing list so they could keep in touch and help. Climate activists told us they wanted help with presenting data about their activities for inclusion in funding bids, and mapping. We now have a facebook page, website and a github repository. We use free tools from Google for email and file sharing (Drive, Sheets, Docs). We hold a monthly online drop-in on Zoom. We’re working on building links into the university, and working with data. Biggest challenges – Many of our contacts who want to help are doing so in a professional capacity, and therefore mostly available during office hours, while the volunteer effort happens outside of working hours. Meanwhile, making the local groups aware of our offer is an area we need to work on, in partnership with the climate action network which is evolving and taking shape. Looking ahead – hoping to organise a datathon event near or at the university in the near future. Delivering skills – we’re in the business of sharing skills, and building our own skills with data along the way. We have fielded requests for help with non-climate data, both from professional life science researchers looking for help with data visualisation, and others through the community engagement network. The experience suggests there may be potential for a platform and other work to build more links for voluntary organisations to find help with specialised skills among the staff and students of the university.

Working for the health of adolescents: a network in action

Working for the health of adolescents: a network in action

PEDS4: Postoperative anticoagulation with bivalirudin versus heparin for HeartMate 3 in children

Background: Bivalirudin has become the standard of care for paracorporeal VAD support in children, although its utilization and outcomes in children with intracorporeal VADs have not been thoroughly studied. We compared the outcomes in children receiving bivalirudin versus unfractionated heparin (UFH) for early (<14 days) post-operative thromboprophylaxis. Methods: Using the ACTION network registry, we conducted a multicenter, retrospective analysis with all patients receiving bivalirudin or UFH for HeartMate 3 (HM3). Survival and adverse events in 14 days post VAD implantation were compared between patients receiving bivalirudin, UFH, or both agents. Results: Between 4/1/18-7/31/22, 171 patients with median age 15.3 years (IQR 12.9-17.3) and weight 62 kg (IQR 45-81) underwent HM3 implantation. Of these, 65 (38%) received bivalirudin, 91 (53%) received UFH and 15 (8.7%) received both agents. At 2 weeks, there was 100% survival in the bivalirudin group, 98% survival in the UFH group, and 100% survival in the bivalirudin/UFH group (p=0.65). Stroke incidence was similar across all anticoagulation agents [4 (6.1%) bivalirudin, 5 (5.5%) UFH, 1 (6.7%) bivalirudin/UFH, p=0.99]. Major bleeding incidence was similar across the anticoagulation agents [2 (3%) bivalirudin, 5 (5.5%) UFH, 1 (6.7%) bivalirudin/UFH, p=0.6). Hemolysis was lower in patients treated with bivalirudin [3 (4.6%)] versus UFH [7 (7.6%)] and bivalirudin/UFH [3 (33%)], p=0.006. Conclusions: In pediatric patients with a HeartMate3, anticoagulation with bivalirudin resulted in similar survival, stroke-free survival, and bleeding-free survival compared to UFH, with lower hemolysis with bivalirudin. More evaluation is required to understand the burden of post-operative hemocompatibility related adverse events.

Talk, test, and take one click to curb prostate cancer mortality.

6541 Background: Prostate cancer remains the most commonly diagnosed cancer and the second leading cause of cancer-related death in men in the United States. BronxCare Health System (BCHS) serves the Bronx neighborhoods of Morris Heights, Fordham South, and Mount Hope, notable for the highest prostate cancer incidence rate in New York City as per the American Cancer Society Cancer Action Network report 2019, highlighting the need for prostate cancer screening among the patient population we serve. An anonymous voluntary paper survey was performed in 2019 at BCHS to determine primary care physician understanding and implementation of current PSA testing guidelines. The study showed that primary care providers at BCHS approach PSA testing in a wide variety of ways and screen a wide age range of patients suggesting a need for intervention. An electronic medical record (EMR) decision support tool activated during primary care visits for men aged 55-69 years was implemented beginning January 1, 2021 which facilitates the provider and patient engagement in a shared decision-making discussion. This a follow-up study to determine the effect of this EMR application on PSA order rates and prostate cancer diagnosis rates at BCHS. Methods: The total number of patients who had PSA and lipid panel (LP) screening were extracted from the EMR of BCHS for potentially eligible men (aged 55-69) seen during primary care visits during the years 2016-2022. LP is done annually as a part of routine screening for adults aged 55-69 and is taken as an equivalent of primary care visits. PSA screening rates were compared to LP screening rates to control for unforeseen variables. Similarly, the number of newly-diagnosed prostate cancers among the same population of men was extracted for the years 2016-2021. Data for 2022 is still not available. 2016-2020 are pre-EMR and 2021 and 2022 are post-EMR flag years. The pre and post-EMR flag PSA and prostate cancer diagnosis numbers were compared by calculating Z scores and p-values. Results: The year-wise PSA, LP, and newly-diagnosed prostate cancers (PC) are tabulated below. The proportion of men who had PSA testing among all men aged 55-69 who had LP testing in each pre-EMR flag year was compared to each post-EMR flag year. A significant increase (p &lt; 0.00001) was noted in the proportion of men who had PSA screening after implementation of the EMR application. Similarly, the proportion of men with newly-diagnosed prostate cancer among all men aged 55-69 who had PSA screening significantly increased after EMR application (p &lt; 0.00001). Conclusions: A decision support tool embedded in the EMR has been an effective approach in increasing prostate cancer screening rates and may help curb prostate cancer mortality in our patient population as early detection in right men at the right age has the potential to reduce prostate cancer mortality. [Table: see text]

Breastfeeding Medicine Across the Globe.

Breastfeeding Medicine Across the Globe.

Achieving Our Moemoeā: Community-Led Food Security Strategy Development.

The Manawatū Food Action Network (MFAN) is a collective of social service and environmental organizations and community stakeholders that work together to promote collaboration, education and awareness of issues surrounding food security, food resilience, and localization in the local community. In 2021, the 4412 neighborhood was identified as requiring urgent assistance, with approximately one third of residents experiencing food insecurity. The 4412 Kai Resilience Strategy was developed with the community to move from food insecurity to food resilience and sovereignty. Recognizing that food security is complex and based on multiple causes, six interwoven workstreams were identified to create a multifaceted, coordinated strategy. This includes education, food economy, community, food support, māra kai, and social enterprise. The strategy cultivates local ownership and commitment to change. It creates a broader constituency of support, balancing the urgent need to feed people today with the long-term need to change systems through step-change initiatives. Through this approach, communities can better make sustainable and meaningful changes to their lives and circumstances rather than relying on external resources.

Energy- and Cost-Efficient Transmission Strategy for UAV Trajectory Tracking Control: A Deep Reinforcement Learning Approach

In this paper, we consider a networked control system (NCS) with network-induced delay, in which the control center needs to control the remote unmanned aerial vehicle (UAV) to complete the trajectory tracking task. The sensor of the controlled UAV adopts the event-triggered mechanism, while the control center uses the adaptive dynamic programming (ADP) based tracking control method to generate control actions. The application of ADP method brings new transmission options, i.e., the control center can choose to transmit control action or neural network model. Considering the fundamental tradeoff between these two transmission options with different transmission energy consumption and tracking cost, we formulate the joint optimization problem as a markov decision process (MDP). Due to the continuous value of state in MDP, we propose the DQN-based strategy, which uses the reinforcement learning (RL) algorithm, specifically deep Q-network (DQN). Besides, we further propose a greedy strategy by calculating the instantaneous expected cost. Simulation results show that DQN-based strategy has better performance but depends on the training process, while greedy strategy is suboptimal but easy to compute. Besides, compared with the benchmark strategies, the proposed strategies can achieve a better compromise in the long-term average energy consumption and tracking cost by adjusting the value of the weighted factor. Furthermore, by comparing the difference of transmission decisions in the proposed strategies, we show that the proper transmission sequence in DQN-based strategy can reduce the tracking cost and transmission energy at the same time.

A history of survival: preserving and working with an archive of single parent activism

ABSTRACT In this article, we relate the story of a grassroots feminist archive. Single Parent Action Network, a Bristol-based national organisation, curated its own archive with great care throughout its history. When the organisation was forced to close in 2016, this irreplaceable repository of records, documents and audio-visual material was unceremoniously dumped in a skip. Thanks to prompt action by two long-time SPAN workers, it was rescued and became the foundation of a public history project run by a university-community partnership. This project was structured around a History Group of community researchers, which met weekly. The project also included the cataloguing and preservation of the archive, in preparation for its accession to the Feminist Archive South. The project team carried out 27 interviews with former SPAN workers, members and volunteers to add to the archive, and also served to create connections between the project team and those who had been involved with the organisation. Finally, the History Group devised a number of creative outputs including a piece of public art, a film, and a series of commemorative mugs. The article reflects on some of the tensions and difficulties that emerged during this work.

Building an Evidence Base for the Protection and Support of Breastfeeding: An Interview With Adriano Cattaneo.

Adriano Cattaneo holds an MD degree from the University of Padua, Italy, and an MSc from the London School of Hygiene and Tropical Medicine. He spent most of his professional career in low-income countries, including 4 years as medical officer with the World Health Organization (WHO) in Geneva. After returning to Italy, he worked for 20 years as an epidemiologist at the Unit for Health Services Research and International Health, the Institute for Maternal and Child Health (IRCCS Burlo Garofolo) in Trieste, a WHO Collaborating Centre for Maternal and Child Health. He is the author of more than 220 publications in scientific journals and books, more than 100 of which are in peer reviewed journals. He has been affiliated with International Baby Food Action Network (IBFAN) in Italy since it was created in 2001. As project coordinator of two European Union funded projects, he played an important role in the development of the document Protection, Promotion and Support of Breastfeeding in Europe: A Blueprint for Action, a tool tested and used for the development of national breastfeeding policies and programs. He retired in 2014.

Eloise Todd: mobilising civil society for pandemic prevention

Eloise Todd: mobilising civil society for pandemic prevention

A Machine Learning Approach for Intrusion Detection

Abstract: Computer networks and virtual machine security are very essential in today’s era. IDS monitors a network or system for malicious action and protects a computer network from unofficial access from users, including perhaps insiders. Various existing systems have already been developed to detect malicious activity on target machines; sometimes any external user creates some malicious behavior and gets unauthorized access to victim machines to such a behavior system considered as malicious activities or Intruder. Machine Learning (ML) algorithms are applied in IDS in order to identify and classify security threats. Numerous machine learning and soft computing techniques are designed to detect the activities in real-time network log audit data. KKDDCUP99 and NLSKDD most utilized data sets to detect the Intruder on the benchmark data set. In this paper, we proposed the identification of impostors using machine learning algorithms. Two different techniques have been proposed a signature with detection and anomaly-based detection. The experimental analysis demonstrates SVM, Naïve Bayes, and ANN algorithms with various data sets and demonstrates system performance in the real-time network environment.

A somato-cognitive action network alternates with effector regions in motor cortex

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action5 and physiological control6, arousal7, errors8 and pain9. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions4 and connectivity to internal organs10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate–isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.

Monitoring Network Traffic for Suspicious Activity

An intrusion detection system investigates hostile behavior within a network or an approach. Software or a gadget called intrusion detection scans a network or system for an untrustworthy action. As computer connectivity increases, intrusion detection becomes increasingly important for network security. Many Intrusion Detection Systems have been built to defend the networks using statistical and machine learning technologies. Accuracy is a crucial factor in how well an intrusion detection system performs. To decrease false detections and boost detection rates, the accuracy of intrusion detection needs to be improved. In recent works, many strategies have been employed to enhance performance. The Intrusion detection system’s primary task is to analyze network traffic data. To solve this problem, a structured classification system is needed. This problem is approached in the suggested manner. Classification methods are often used to address related issues. NSL-KDD knowledge discovery Dataset is used to evaluate the results of these systems. This research aims to find an efficient classifier that detects anomaly traffic with a high accuracy level and minimal error rate by experimenting with possible machine-learning techniques.

Event-triggered near-optimal tracking control based on adaptive dynamic programming for discrete-time systems

Frequent state monitoring and controller updates can enhance the precision of tracking control, while simultaneously overburdening the communication network transmission. In this paper, For the purpose of saving communication costs, we propose event-triggered control algorithms for the optimal tracking control problem. First, we reconstruct the discrete-time nonlinear system into a converted system. Then, the adaptive dynamic programming algorithm is employed to find the optimal controller off-line, and the event-triggered scheme is used to reduce the communication costs online. Novel triggering conditions with fewer assumptions are designed to implement the event-triggered scheme. Different from existing works, the event-triggered scheme can be introduced not only into the converted system but also into the actual system, which is more practical because the actual controller is what one can only access in practice. In addition, with the developed algorithms, the tracking error can be proved to be stable at the origin, in other words, the actual system can be guaranteed to track the desired trajectory. Algorithms developed in this paper are implemented by three neural networks, the model network, the action network and the critic network. Finally, examples are presented to verify the effectiveness and rationality of the algorithms.