Broad Network Research Articles

Safety & feasibility of responsive neurostimulation in children with refractory epilepsy: A single-center experience

ObjectivesResponsive neurostimulation (RNS) is a relatively recent addition to the epilepsy surgery armory, gaining FDA approval in 2013 for use in adults with intractable focal epilepsy. Data for the use of RNS system in patients less than 18 years of age is limited. We aim to determine the safety and feasibility of RNS in children with refractory epilepsy. MethodsA retrospective chart review was conducted for all patients who underwent RNS implantation at an urban tertiary children's hospital. Demographics of the patients were obtained, including age at the time of implant, MRI findings, seizure onset zone identification, and RNS targets. ResultsOut of a fourteen patient cohort, one patient had a post-operative complication of infection at surgical site requiring explantation. Thirteen out of 14 patients had immediate post-operative head imaging that was negative for hemorrhage, infarction, or skull fracture; one patient did not undergo head imaging. No patients reported a worsening clinical seizure frequency at a 6-month follow up visit. In the subset of patients who were implanted with RNS and did not undergo concurrent resections, there was a statistically significant reduction in the average number of long episodes at the most recent visit when compared to the 1-month post-operative visit (p = 0.0268). ConclusionsRNS is a feasible and safe option for children as young as six years with refractory epilepsy when appropriate seizure focus identification has been performed with stereo CT and stereo EEG evaluations, and can be used in conjunction with other surgical epilepsy treatment modalities. Two canister RNS placement is achievable for patients with a broad epileptogenic network or multifocal seizure onset zones.

Comprehensive and Visualized Analysis of Interventional Clinical Trials of Spinal Cord Injury in the Past Two Decades: A Bibliometric Study

ObjectiveTo provide a bibliometric analysis of published articles of interventional SCI clinical trials and present the current status, global trends, and scholars’ knowledge in this field. MethodsData were obtained from the PubMed and Web of Science (WOS) Core Collection. A literature screening process was conducted to select articles of interventional SCI clinical trials. Qualitative and quantitative analysis was performed based on information from the WOS database. Co-occurrence analysis of keywords, collaboration analysis, and co-citation analysis were performed using VOSviewer. ResultsA total of 849 articles were selected for analysis. Therapeutic strategies in the articles were classified into ten subgroups. Locomotor training, local complication related treatments, and neuromodulation are the top three subgroups. Spinal Cord was the most productive and most frequently cited journal in SCI clinical trials. The most productive country, institution, and author are the United States of America, the University of Miami, and Harvey LA, respectively. The collaboration network analysis shows that researchers and institutions from the United States, Canada, the United Kingdom, and Australia were the pivotal driving forces in this field. Spinal cord injury, rehabilitation, individuals, electrical stimulation, and exercise are the top 5 most frequent keywords. The co-citation analysis shows that the most frequently cited references are all international standards and guidelines of SCI clinical trials. ConclusionsThere is a broad research collaboration network although it is distributed unevenly worldwide. International standards and guidelines are needed to conduct high quality clinical trials in the future.

Social capital in cooperatives: A typology and their influence on performance

One of the distinctive characteristics of cooperatives is their social capital. This article contributes to the literature by proposing a classification of cooperatives according to their social capital: internal social networks (relationships between their members, and between their members and their managers) and/or external social networks (relationships with customers, other cooperatives, institutions etc.). The sample comprised managers from 50 agrifood cooperatives located in the Canary Islands (Spain) who answered a questionnaire designed on the basis of the existing literature. Descriptive statistics such as means and non-parametric test (Pearson's Chi-square test) were used to process the information. The largest group of cooperatives developed strong internal and external social networks (i.e., a broad social network). The second group was the opposite: social networks were weak internally as well as externally (the so-called narrow social network). The results also showed that when cooperatives developed only one type of network, they focused on a strong internal network (asymmetric internal social network). The only difference in the performance of cooperatives depending on their social network was observed in cooperatives with an asymmetric external social network. Such cooperatives had a better public image than their competitors.

Adaptive Broad Learning Neural Network for Fault-Tolerant Control of 2-DOF Helicopter Systems

Adaptive Broad Learning Neural Network for Fault-Tolerant Control of 2-DOF Helicopter Systems

Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition

Electroencephalogram (EEG) data is commonly applied in the emotion recognition research area. It can accurately reflect the emotional changes of the human body by applying graphical-based algorithms or models. EEG signals are non-linear signals. Biological tissues’ adjustment and adaptive ability will inevitably affect electrophysiological signals, making EEG have the typical non-linear characteristics. Graph Convolutional Broad Network (GCB-net) extracted features from non-linear signals and abstract features via stacked CNN. It adopted the broad concept and enhanced the feature by the broad learning system (BLS), obtaining sound results. However, it performed poorly with increasing network depth, and the accuracy of some features decreased with BLS. This paper proposed a Residual Graph Convolutional Broad Network (Residual GCB-net), which promotes the performance on a deeper-layer network and extracts higher-level information. It substitutes the original convolutional layer with residual learning blocks, which solves the deep learning network degradation and extracts more features in deeper networks. In SEED data set, GCB-Res net could obtain the best accuracy (94.56%) on the all-frequency band of differential entropy (DE) and promote much on another feature. In Dreamer, it obtained the best accuracy (91.55%) on the dimension of Arousal. The result demonstrated the excellent classification performance of Residual GCB-net in EEG emotion recognition.

The Integration and Development of Franchising in China Take InterContinental Hotel as an Example

This paper has conducted in-depth research on the franchise business model of InterContinental Hotels and its application in the Chinese hotel industry. Using the franchise business model has brought benefits to InterContinental Hotels, including brand expansion, increased market share, additional revenue streams, resource consolidation, and risk sharing. Through the SWOT analysis, this paper analyses the strengths of Intercontinental Hotels high brand awareness, including perfect comprehensive facilities, broad global distribution network; the weakness include high franchise costs, strict operating standards; the opportunities include market demand growth, opportunities and economic fluctuations needed for emerging market expansion, and the threats include strong competitors, and put forward the use of InterContinental Liquor. The brand advantage of the hotel enhances the user experience and explores the development strategy of new market opportunities. The article has also discussed the applicability of the franchise business model and the role of the government in franchise development. At the same time, this experience has extensively explored the franchise business model of InterContinental Hotels and its application in the Chinese hotel industry.

Incremental forecaster using C–C algorithm to phase space reconstruction and broad learning network for short-term wind speed prediction

Wind power gains more and more attention from all over the world as a clean and renewable energy resource, and accurate prediction of wind speed has become a hot issue. This paper presents a novel incremental forecaster based on phase space reconstruction and broad learning network (BLN) for short-term prediction. First, time delay and embedding dimension which take an essential part in phase space reconstruction are determined by the C–C algorithm. Then, these optimal parameters are input to the BLN trained incrementally. Afterward, forecasting values are given by the output layer of BLN. Data collected from a wind farm is adopted for verifying the efficacy of this proposed model. Furthermore, five commonly used assessment indicators are applied to evaluate predictive performance of different models. Results show that the proposed model has the smallest prediction error, which performs better than the other models at one-step to three-step ahead forecasting, and this strength is attributed to address the problem of local optimum. Furthermore, the proposed model consumes less training time than the other models. Therefore, the proposed model tends to be promising for wind speed prediction of the big data era.

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas.

The Tbx20-TLE interaction is essential for the maintenance of the second heart field.

T-box transcription factor 20 (Tbx20) plays a multifaceted role in cardiac morphogenesis and controls a broad gene regulatory network. However, the mechanism by which Tbx20 activates and represses target genes in a tissue-specific and temporal manner remains unclear. Studies show that Tbx20 directly interacts with the Transducin-like Enhancer of Split (TLE) family of proteins to mediate transcriptional repression. However, a function for the Tbx20-TLE transcriptional repression complex during heart development has yet to be established. We created a mouse model with a two amino acid substitution in the Tbx20 EH1 domain, thereby disrupting the Tbx20-TLE interaction. Disruption of this interaction impaired crucial morphogenic events, including cardiac looping and chamber formation. Transcriptional profiling of Tbx20EH1Mut hearts and analysis of putative direct targets revealed misexpression of the retinoic acid pathway and cardiac progenitor genes. Further, we show that altered cardiac progenitor development and function contribute to the severe cardiac defects in our model. Our studies indicate that TLE-mediated repression is a primary mechanism by which Tbx20 controls gene expression.

The Proposed Framework for Cloud Computing System Security and Privacy Services

Public cloud computing has emerged as a popular and efficient solution for organizations to store, process, and access their data and applications. However, with the increasing adoption of public cloud services, concerns regarding security have become a critical factor in decision-making processes. This study aims to investigate the security aspects of public cloud computing and identify potential vulnerabilities and threats that organizations may face. The aim of this study is to examine the challenges associated with cloud computing and its security concerns. Cloud computing is a technology that utilizes the internet to share data and resources. It is characterized by five key features: On-Demand self-service, Resource Pooling, Broad network access, Rapid elasticity, and Measured Service. However, despite these benefits, cloud computing has several open issues, including Security, Availability, Scalability, and Interoperability. Traditional security techniques are insufficient in fully securing cloud computing. Security and privacy issues often arise during cloud deployment and delivery models. As a result, researchers are interested in learning more about cloud computing security and its methods. Industries are creating architectural models with a high level of protection for their data center to solve current security challenges. This article examines the security of cloud deployment strategies and analyzes cloud security and privacy challenges. The cloud computing service models are also discussed, along with any security issues. The study emphasizes the significance of Audit as a key factor in offering a security solution that covers all relevant security characteristics as determined by the literature. The study's conclusions present a fresh challenge to cloud service providers and suggest a new security paradigm that explains customer security needs. Overall, this study offers a parameter-by-parameter assessment of available security solutions and makes recommendations for enhancements to address cloud security issues.

Cyclin D1 extensively reprograms metabolism to support biosynthetic pathways in hepatocytes

Cell proliferation requires metabolic reprogramming to accommodate biosynthesis of new cell components, and similar alterations occur in cancer cells. However, the mechanisms linking the cell cycle machinery to metabolism are not well defined. Cyclin D1, along with its main partner cyclin-dependent kinase 4 (Cdk4), is a pivotal cell cycle regulator and driver oncogene that is overexpressed in many cancers. Here, we examine hepatocyte proliferation to define novel effects of cyclin D1 on biosynthetic metabolism. Metabolomic studies reveal that cyclin D1 broadly promotes biosynthetic pathways including glycolysis, the pentose phosphate pathway, and purine and pyrimidine nucleotide synthesis in hepatocytes. Proteomic analyses demonstrate that overexpressed cyclin D1 binds to numerous metabolic enzymes including those involved in glycolysis and pyrimidine synthesis. In the glycolysis pathway, cyclin D1 activates aldolase and GAPDH, and these proteins are phosphorylated by cyclin D1/Cdk4 in vitro. De novo pyrimidine synthesis is particularly dependent on cyclin D1. Cyclin D1/Cdk4 phosphorylates the initial enzyme of this pathway, CAD (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, dihydroorotase), and metabolomic analysis indicates that cyclin D1 depletion markedly reduces the activity of this enzyme. Pharmacologic inhibition of Cdk4 along with the downstream pyrimidine synthesis enzyme DHODH (dihydroorotate dehydrogenase) synergistically inhibits proliferation and survival of hepatocellular carcinoma cells. These studies demonstrate that cyclin D1 promotes a broad network of biosynthetic pathways in hepatocytes, and this model may provide insights into potential metabolic vulnerabilities in cancer cells.

Presidential Address 2023: The Beauty of Uncertainty: The Rise of Insurance Contracts and Markets in Medieval Europe

Abstract Maritime insurance developed in medieval Europe is the ancestor of all forms of insurance that appeared subsequently. We address the question of why modern insurance was first invented in medieval Europe, and neither earlier nor elsewhere. Drawing from insights from the literature on uncertainty aversion, we show that medieval merchants had to bear more frequently natural risks (they traveled longer distances) and new human risks with unknown probabilities (they faced unpredictable attacks by corsairs due to increased political fragmentation and commercial competition in Europe). The increased demand for protection in medieval seaborne trade met the supply of protection by a small group of wealthy merchants with a broad information network who could pool risks and profit from selling protection through a novel business device: the insurance contract. A new market—the market for insurance—was then born. Next, analyzing more than 7,000 insurance contracts redacted by notaries and about 100 court proceedings housed in the archives of Barcelona, Florence, Genoa, Palermo, Prato, and Venice, we study the main features of medieval trade, the type of risks faced by merchants, and the characteristics of insurance contracts and markets from 1340 to 1500.The empirical analysis delivers two main findings. First, risks related to human activities (e.g., attacks by corsairs) seem to have had a relatively greater impact on insurance premia compared to natural risks (proxied by seasonal risks). Second, distance mattered but the route seems to have had a greater impact on insurance premia. Specific routes (e.g., in the Tyrrhenian and the western Mediterranean) were more plagued by human risks, which were harder to avoid for the majority of merchants who did not have a broad information network compared to the few wealthy merchants, who became the key players in selling insurance in the early stages of the development of insurance markets.

Research on emotion recognition of bimodal bioelectrical features based on D-S evidence theory

As the pace of life becomes faster and faster, people with weaker mental capacity are prone to a range of negative emotions in certain environments, such as mania and anxiety, and these negative emotions can interfere with normal learning and life, and even affect physical and mental health. Therefore, it is more and more important to accurately analyze people's emotional state.In this paper, based on the bimodal signals of EEG and EMG, we combine the broad learning network and D-S evidence theory to do an emotion recognition analysis of bioelectrical signals evoked by visual stimuli. Firstly, a suitable experimental paradigm is designed to acquire EEG signals and EMG signals through emotion evocation, followed by pre-processing, decomposing the signals using a wavelet decomposition algorithm, extracting signal components significantly affected by emotional states, and calculating the corresponding differential entropy as well as power spectrum features; after that, EMG features in time and frequency domains are extracted, and different features are fused and analyzed; BLS is used for classification, and finally, the BLS classification The results are fused with the introduced D-S evidence theory at the decision level. The method can recognize emotions effectively with an accuracy of 85.2% in triple classification scenarios. The proposed emotion recognition model is expected to provide a new technical tool for emotional cognitive science research and emotional brain-computer interface systems.

A-269 Post-pandemic Opportunities for Commercial Laboratories in Infectious Disease Diagnostics and Public Health

Abstract Background Aegis launched COVID-19 testing with a daily capacity of 3000 tests and scaled to over 100 000 tests/day in one year. The lab has performed over 14.2M COVID-19 tests to date, servicing a broad network of physicians, urgent care centers, nursing homes, public schools, departments of health, and retail pharmacies across the United States. As SARS-CoV-2 infections reach an equilibrium, Aegis recognized the opportunity to address broader public health needs and leveraged the partnerships, technologies, and infrastructure developed to build the Infectious Disease Testing (IDT) menu aimed at limiting the spread of health risks within the community. Methods Real-time polymerase chain reaction (RT-PCR) tests for common and rare bacterial, viral, and parasitic pathogens associated with genital health (GH) and sexually transmitted infections (STI), as well as infections of the urinary (UTI), gastrointestinal (GI), and respiratory tracts (RTI) were validated. These tests are performed in 384-well custom PCR plates, pre-spotted with primers and probes capable of detecting the presence of DNA or RNA (LOD = 10 copies/µL) from 30 different organisms in up to 12 patient samples simultaneously. Syndromic panels were developed for ordering based on similar intended use, and providers have the option to order panels or stand-alone tests based on medical necessity. Following receipt into the lab, the total nucleic acid is extracted from the primary specimen and pipetted into the PCR assay plate with Taq DNA polymerase master mix, and analysis and reporting of ordered targets are complete within nine hours, on average. Results and de-identified demographic data are parsed from the laboratory information management system (LIMS) to Microsoft PowerBI software to internally track testing trends, positivity rates, and outbreaks of infectious diseases. Results Historical data including ordered profiles, matrices, and pathogens detected combined with de-identified demographic data such as patient age, gender, race, residing state, and collection month were imported into PowerBI and used to create interactive visuals for a dashboard to monitor trends and predict future testing patterns. The IDT panels ordered most frequently include STI (27.1%), upper RTI (23.6%), bacterial vaginosis (21.0%), GI bacterial (15.1%), candidiasis (13.7%), and GI viral (13.0%). The majority (74%) of GH/STI tests were ordered for patients between the ages of 15 to 34 years, and the most prevalent infections detected were bacterial vaginosis (35%), mycoplasma hominis (22%), and chlamydia trachomatis (4%). Sixty-one percent (61%) of upper RTI tests were ordered for patients between the ages of 0 to 14 years, and fluctuations in positivity rates followed seasonal trends for pathogens such as rhinovirus, influenza, adenovirus, RSV, and enterovirus D68. GI profiles were predominantly ordered for the younger (0–14 years, 35%) and the older (>65 years, 18%) population with e. coli O157 (11%), norovirus (9%), sapovirus (6%), and astrovirus (4%) infections detected at the highest rates. Conclusion The COVID-19 pandemic underscored the importance of molecular diagnostics for SARS-CoV-2 infection and surveillance. Experience gained during the COVID-19 response allowed Aegis to repurpose the resources, capacity, and infrastructure to create business development opportunities aimed at laboratory growth in infectious diseases and public health.

Primary school bathrooms as hybrid technologies: materials, objects and practices (Buenos Aires, 1880-1930)

ABSTRACT This article aims to share part of some middle-term research focused on Argentinian school bathrooms. Bathrooms in Argentina emerged around 1850 and have been present – with nuances – in public and domestic buildings since the last third of the nineteenth century. Particularly, primary-school bathroom history is marked by two facts. First, these spaces are closely related to central schooling topics such as behaviours, privacy, intimacy, as well as gender and generational roles. Second, architectural and engineering developments and the proliferation of bathrooms were contemporary processes. The goal here is to trace some relationships between school spaces, materials, objects, and practices present in school bathrooms in the city of Buenos Aires between 1880 and 1930. The focus will be on school bathrooms, but it is also worth mentioning some connections with broader networks such as sanitary bureaucracy and transnational commercial circuits. As a general method, we pursued the Grounded Theory proposal. We worked with several sources: National Council of Education (CNE) files, construction and sanitary regulations, the Sanitary Ministry memories and documents. Due to our interest in objects (for example, sanitary artefacts) and materials, we took in commercial catalogues, a sanitary workers’ journal and archaeological works. First, we will briefly describe some features of the “latrine model”. Then, we would like to discuss the mutation to a more industrialised model, which is the one that persists to this day.

Hunger- and thirst-sensing neurons modulate a neuroendocrine network to coordinate sugar and water ingestion.

Consumption of food and water is tightly regulated by the nervous system to maintain internal nutrient homeostasis. Although generally considered independently, interactions between hunger and thirst drives are important to coordinate competing needs. In Drosophila, four neurons called the interoceptive subesophageal zone neurons (ISNs) respond to intrinsic hunger and thirst signals to oppositely regulate sucrose and water ingestion. Here, we investigate the neural circuit downstream of the ISNs to examine how ingestion is regulated based on internal needs. Utilizing the recently available fly brain connectome, we find that the ISNs synapse with a novel cell-type bilateral T-shaped neuron (BiT) that projects to neuroendocrine centers. In vivo neural manipulations revealed that BiT oppositely regulates sugar and water ingestion. Neuroendocrine cells downstream of ISNs include several peptide-releasing and peptide-sensing neurons, including insulin producing cells (IPCs), crustacean cardioactive peptide (CCAP) neurons, and CCHamide-2 receptor isoform RA (CCHa2R-RA) neurons. These neurons contribute differentially to ingestion of sugar and water, with IPCs and CCAP neurons oppositely regulating sugar and water ingestion, and CCHa2R-RA neurons modulating only water ingestion. Thus, the decision to consume sugar or water occurs via regulation of a broad peptidergic network that integrates internal signals of nutritional state to generate nutrient-specific ingestion.

Hunger- and thirst-sensing neurons modulate a neuroendocrine network to coordinate sugar and water ingestion

Consumption of food and water is tightly regulated by the nervous system to maintain internal nutrient homeostasis. Although generally considered independently, interactions between hunger and thirst drives are important to coordinate competing needs. In Drosophila, four neurons called the interoceptive subesophageal zone neurons (ISNs) respond to intrinsic hunger and thirst signals to oppositely regulate sucrose and water ingestion. Here, we investigate the neural circuit downstream of the ISNs to examine how ingestion is regulated based on internal needs. Utilizing the recently available fly brain connectome, we find that the ISNs synapse with a novel cell-type bilateral T-shaped neuron (BiT) that projects to neuroendocrine centers. In vivo neural manipulations revealed that BiT oppositely regulates sugar and water ingestion. Neuroendocrine cells downstream of ISNs include several peptide-releasing and peptide-sensing neurons, including insulin producing cells (IPCs), crustacean cardioactive peptide (CCAP) neurons, and CCHamide-2 receptor isoform RA (CCHa2R-RA) neurons. These neurons contribute differentially to ingestion of sugar and water, with IPCs and CCAP neurons oppositely regulating sugar and water ingestion, and CCHa2R-RA neurons modulating only water ingestion. Thus, the decision to consume sugar or water occurs via regulation of a broad peptidergic network that integrates internal signals of nutritional state to generate nutrient-specific ingestion.

Farm advisors play a key role in integrating crop-livestock at the farm level: perceptions and experiences in Brazil and France

ABSTRACT Purpose Integrated crop-livestock systems (ICLS) can foster agroecological transitions. However, knowledge on how extension services influence such systems is still lacking. The manuscript analyzes ICLS advisors’ perceptions of the conditions toward recoupling crops and livestock at the farm level. Methodology/Approach We conducted and analyzed 20 semi-structured interviews with advisors and project managers currently working with ICLS in Brazil and France. Findings The results revealed the following advisors’ activities undertaken to support recoupling crop and livestock systems: i) gathering ICLS knowledge by building a network as a knowledge source, along with mobilizing and combining tools and skills, ii) communicating and facilitating arrangements between farmers and farmers´ groups, iii) supporting ICLS co-design by assisting farmers in setting goals and plans, and iv) assisting on-farm transitions by supporting implementation and monitoring. Practical implications This research highlights the key role of advisors in the transition toward the recoupling of crops and livestock at the farm level. ICLS advising could be improved and simplified with better-adapted tools, training to acquire knowledge, and skills specific to the ICLS context. Theoretical implications Systemic thinking, facilitation, communication, and mobilizing a broad network to exchange knowledge are key requirements for advising complex systems such as ICLS. This is a typical case of a ‘knowledge brokering’ approach in which advisors engage in systemic facilitation to encourage innovation adoption. Originality Our analysis provides promising insights into the specific advisor experiences in the transition towards adopting agroecological approaches, such as ICLS. This research opens avenues for future research to encourage ICLS adoption.

System-wide mapping of peptide-GPCR interactions in C. elegans

Neuropeptides and peptide hormones are ancient, widespread signaling molecules that underpin almost all brain functions. They constitute a broad ligand-receptor network, mainly by binding to G protein-coupled receptors (GPCRs). However, the organization of the peptidergic network and roles of many peptides remain elusive, as our insight into peptide-receptor interactions is limited and many peptide GPCRs are still orphan receptors. Here we report a genome-wide peptide-GPCR interaction map in Caenorhabditis elegans. By reverse pharmacology screening of over 55,384 possible interactions, we identify 461 cognate peptide-GPCR couples that uncover a broad signaling network with specific and complex combinatorial interactions encoded across and within single peptidergic genes. These interactions provide insights into peptide functions and evolution. Combining our dataset with phylogenetic analysis supports peptide-receptor co-evolution and conservation of at least 14 bilaterian peptidergic systems in C.elegans. This resource lays a foundation for system-wide analysis of the peptidergic network.

Groundwork for AI: Enforcing a benchmark for neoantigen prediction in personalized cancer immunotherapy.

This article expands on recent studies of machine learning or artificial intelligence (AI) algorithms that crucially depend on benchmark datasets, often called 'ground truths.' These ground-truth datasets gather input-data and output-targets, thereby establishing what can be retrieved computationally and evaluated statistically. I explore the case of the Tumor nEoantigen SeLection Alliance (TESLA), a consortium-based ground-truthing project in personalized cancer immunotherapy, where the 'truth' of the targets-immunogenic neoantigens-to be retrieved by the would-be AI algorithms depended on a broad technoscientific network whose setting up implied important organizational and material infrastructures. The study shows that instead of grounding an undisputable 'truth', the TESLA endeavor ended up establishing a contestable reference, the biology of neoantigens and how to measure their immunogenicity having slightly evolved alongside this four-year project. However, even if this controversy played down the scope of the TESLA ground truth, it did not discredit the whole undertaking. The magnitude of the technoscientific efforts that the TESLA project set into motion and the needs it ultimately succeeded in filling for the scientific and industrial community counterbalanced its metrological uncertainties, effectively instituting its contestable representation of 'true' neoantigens within the field of personalized cancer immunotherapy (at least temporarily). More generally, this case study indicates that the enforcement of ground truths, and what it leaves out, is a necessary condition to enable AI technologies in personalized medicine.