tbuckley/cabot-search huggingface dataset:
Hydrogelation of peptides and carnosic acid as regulators of adaptive immunity against postoperative recurrence of cutaneous melanoma
Authors: Mengdi Ren, Yang Wang, Xiaoqiang Zheng, Wenguang Yang, Mutian Liu, Siyun Xie, Yu Yao, Jin Yan, Wangxiao He
Journal: Journal of Controlled Release (2024)

The in-situ activation of adaptive immunity at the surgical site has demonstrated remarkable efficacy in inhibiting various forms of tumour recurrence and even holds the promise of a potential cure. However, extensive research and bioinformatic analysis conducted in this study have unveiled the formidable challenge posed by melanoma-intrinsic β-catenin signaling, which hinders the infiltration of cytotoxic T-lymphocytes (CTLs) and their subsequent anti-tumour action. To overcome this obstacle, a β-catenin antagonist called carnosic acid (CA) was co-assembled with a RADA-rich peptide to create a nanonet-derived hydrogel known as Supra-gelδCA. This injectable hydrogel is designed to be retained at the surgical site while simultaneously promoting hemostasis. Importantly, Supra-gelδCA directly releases CA to the site of residual tumour lesions, thereby enhancing infiltration of CTLs and subsequently activating adaptive immunity. Consequently, it effectively suppresses postoperative recurrence of skin cutaneous melanoma (SKCM) in vivo. Collectively, the presented Supra-gelδCA not only provides an efficacious immunotherapy strategy for regulating adaptive immunity by overcoming the obstacle posed by melanoma-intrinsic β-catenin signaling-induced absence of CTLs but also offers a clinically translatable hydrogel that revolutionizes post-surgical management of SKCM.
Cross‐protection of <scp>AIT</scp>‐induced antibodies to related allergens requires a high degree of structural identity
Authors: Hilal Demir, Christian Radauer, Maria R. Strobl, Stephan Scheurer, Tamar Kinaciyan, Barbara Bohle
Journal: Allergy (2024)

In contrast to sublingual immunotherapy (SLIT) with recombinant Mal d 1 (rMal d 1-SLIT), SLIT with rBet v 1 (rBet v 1-SLIT) induced Mal d 1-cross-reactive antibodies without IgE-blocking activity. To elucidate whether the development of cross-protective IgG responses depends on the degree of molecular identity of allergens we compared the cross-reactivity, cross-blocking activity, and affinity of SLIT-induced antibodies with allergens of varying amino acid sequence identities to Bet v 1 and Mal d 1, namely Cor a 1.04 (hazelnut), Pru av 1 (cherry), and Dau c 1 (carrot).
The added value of affective processes for models of human cognition and learning
Authors: Yoann Stussi, Daniel Dukes, David Sander
Journal: Behavioral and Brain Sciences (2024)

Building on the affectivism approach, we expand on Binz et al.'s meta-learning research program by highlighting that emotion and other affective phenomena should be key to the modeling of human learning. We illustrate the added value of affective processes for models of learning across multiple domains with a focus on reinforcement learning, knowledge acquisition, and social learning.
Meta-learning: Bayesian or quantum?
Authors: Antonio Mastrogiorgio
Journal: Behavioral and Brain Sciences (2024)

Abundant experimental evidence illustrates violations of Bayesian models across various cognitive processes. Quantum cognition capitalizes on the limitations of Bayesian models, providing a compelling alternative. We suggest that a generalized quantum approach in meta-learning is simultaneously more robust and flexible, as it retains all the advantages of the Bayesian framework while avoiding its limitations.
Integrative learning in the lens of meta-learned models of cognition: Impacts on animal and human learning outcomes
Authors: Bin Yin, Xi-Dan Xiao, Xiao-Rui Wu, Rong Lian
Journal: Behavioral and Brain Sciences (2024)

This commentary examines the synergy between meta-learned models of cognition and integrative learning in enhancing animal and human learning outcomes. It highlights three integrative learning modes - holistic integration of parts, top-down reasoning, and generalization with in-depth analysis - and their alignment with meta-learned models of cognition. This convergence promises significant advances in educational practices, artificial intelligence, and cognitive neuroscience, offering a novel perspective on learning and cognition.
Meta-learning: Data, architecture, and both
Authors: Marcel Binz, Ishita Dasgupta, Akshay Jagadish, Matthew Botvinick, Jane X Wang, Eric Schulz
Journal: Behavioral and Brain Sciences (2024)

We are encouraged by the many positive commentaries on our target article. In this response, we recapitulate some of the points raised and identify synergies between them. We have arranged our response based on the tension between data and architecture that arises in the meta-learning framework. We additionally provide a short discussion that touches upon connections to foundation models.
Quo vadis, planning?
Authors: Jacques Pesnot-Lerousseau, Christopher Summerfield
Journal: Behavioral and Brain Sciences (2024)

Deep meta-learning is the driving force behind advances in contemporary AI research, and a promising theory of flexible cognition in natural intelligence. We agree with Binz et al. that many supposedly "model-based" behaviours may be better explained by meta-learning than by classical models. We argue that this invites us to revisit our neural theories of problem solving and goal-directed planning.
Bayes beyond the predictive distribution
Authors: Anna Székely, Gergő Orbán
Journal: Behavioral and Brain Sciences (2024)

Binz et al. argue that meta-learned models offer a new paradigm to study human cognition. Meta-learned models are proposed as alternatives to Bayesian models based on their capability to learn identical posterior predictive distributions. In our commentary, we highlight several arguments that reach beyond a predictive distribution-based comparison, offering new perspectives to evaluate the advantages of these modeling paradigms.
Meta-learning in active inference
Authors: O Penacchio, A Clemente
Journal: Behavioral and Brain Sciences (2024)

Binz et al. propose meta-learning as a promising avenue for modelling human cognition. They provide an in-depth reflection on the advantages of meta-learning over other computational models of cognition, including a sound discussion on how their proposal can accommodate neuroscientific insights. We argue that active inference presents similar computational advantages while offering greater mechanistic explanatory power and biological plausibility.
The hard problem of meta-learning is what-to-learn
Authors: Yosef Prat, Ehud Lamm
Journal: Behavioral and Brain Sciences (2024)

Binz et al. highlight the potential of meta-learning to greatly enhance the flexibility of AI algorithms, as well as to approximate human behavior more accurately than traditional learning methods. We wish to emphasize a basic problem that lies underneath these two objectives, and in turn suggest another perspective of the required notion of "meta" in meta-learning: knowing what to learn.
Learning and memory are inextricable
Authors: Sue Llewellyn
Journal: Behavioral and Brain Sciences (2024)

The authors' aim is to build "more biologically plausible learning algorithms" that work in naturalistic environments. Given that, first, human learning and memory are inextricable, and, second, that much human learning is unconscious, can the authors' first research question of how people improve their learning abilities over time be answered without addressing these two issues? I argue that it cannot.
Meta-learned models as tools to test theories of cognitive development
Authors: Kate Nussenbaum, Catherine A Hartley
Journal: Behavioral and Brain Sciences (2024)

Binz et al. argue that meta-learned models are essential tools for understanding adult cognition. Here, we propose that these models are particularly useful for testing hypotheses about why learning processes change across development. By leveraging their ability to discover optimal algorithms and account for capacity limitations, researchers can use these models to test competing theories of developmental change in learning.
Challenges of meta-learning and rational analysis in large worlds
Authors: Margherita Calderan, Antonino Visalli
Journal: Behavioral and Brain Sciences (2024)

We challenge Binz et al.'s claim of meta-learned model superiority over Bayesian inference for large world problems. While comparing Bayesian priors to model-training decisions, we question meta-learning feature exclusivity. We assert no special justification for rational Bayesian solutions to large world problems, advocating exploring diverse theoretical frameworks beyond rational analysis of cognition for research advancement.
Meta-learning as a bridge between neural networks and symbolic Bayesian models
Authors: R Thomas McCoy, Thomas L Griffiths
Journal: Behavioral and Brain Sciences (2024)

Meta-learning is even more broadly relevant to the study of inductive biases than Binz et al. suggest: Its implications go beyond the extensions to rational analysis that they discuss. One noteworthy example is that meta-learning can act as a bridge between the vector representations of neural networks and the symbolic hypothesis spaces used in many Bayesian models.
Probabilistic programming versus meta-learning as models of cognition
Authors: Desmond C Ong, Tan Zhi-Xuan, Joshua B Tenenbaum, Noah D Goodman
Journal: Behavioral and Brain Sciences (2024)

We summarize the recent progress made by probabilistic programming as a unifying formalism for the probabilistic, symbolic, and data-driven aspects of human cognition. We highlight differences with meta-learning in flexibility, statistical assumptions and inferences about cogniton. We suggest that the meta-learning approach could be further strengthened by considering Connectionist
Meta-learning goes hand-in-hand with metacognition
Authors: Chris Fields, James F Glazebrook
Journal: Behavioral and Brain Sciences (2024)

Binz et al. propose a general framework for meta-learning and contrast it with built-by-hand Bayesian models. We comment on some architectural assumptions of the approach, its relation to the active inference framework, its potential applicability to living systems in general, and the advantages of the latter in addressing the explanation problem.
Combining meta-learned models with process models of cognition
Authors: Adam N Sanborn, Haijiang Yan, Christian Tsvetkov
Journal: Behavioral and Brain Sciences (2024)

Meta-learned models of cognition make optimal predictions for the actual stimuli presented to participants, but investigating judgment biases by constraining neural networks will be unwieldy. We suggest combining them with cognitive process models, which are more intuitive and explain biases. Rational process models, those that can sequentially sample from the posterior distributions produced by meta-learned models, seem a natural fit.
The meta-learning toolkit needs stronger constraints
Authors: Erin Grant
Journal: Behavioral and Brain Sciences (2024)

The implementation of meta-learning targeted by Binz et al. inherits benefits and drawbacks from its nature as a connectionist model. Drawing from historical debates around bottom-up and top-down approaches to modeling in cognitive science, we should continue to bridge levels of analysis by constraining meta-learning and meta-learned models with complementary evidence from across the cognitive and computational sciences.
The reinforcement metalearner as a biologically plausible meta-learning framework
Authors: Tim Vriens, Mattias Horan, Jacqueline Gottlieb, Massimo Silvetti
Journal: Behavioral and Brain Sciences (2024)

We argue that the type of meta-learning proposed by Binz et al. generates models with low interpretability and falsifiability that have limited usefulness for neuroscience research. An alternative approach to meta-learning based on hyperparameter optimization obviates these concerns and can generate empirically testable hypotheses of biological computations.
Is human compositionality meta-learned?
Authors: Jacob Russin, Sam Whitman McGrath, Ellie Pavlick, Michael J Frank
Journal: Behavioral and Brain Sciences (2024)

Abstract Recent studies suggest that meta-learning may provide an original solution to an enduring puzzle about whether neural networks can explain compositionality – in particular, by raising the prospect that compositionality can be understood as an emergent property of an inner-loop learning algorithm. We elaborate on this hypothesis and consider its empirical predictions regarding the neural mechanisms and development of human compositionality.
Quantum Markov blankets for meta-learned classical inferential paradoxes with suboptimal free energy
Authors: Kevin B. Clark
Journal: Behavioral and Brain Sciences (2024)

Quantum active Bayesian inference and quantum Markov blankets enable robust modeling and simulation of difficult-to-render natural agent-based classical inferential paradoxes interfaced with task-specific environments. Within a non-realist cognitive completeness regime, quantum Markov blankets ensure meta-learned irrational decision making is fitted to explainable manifolds at optimal free energy, where acceptable incompatible observations or temporal Bell-inequality violations represent important verifiable real-world outcomes.
Meta-learning modeling and the role of affective-homeostatic states in human cognition
Authors: Ignacio Cea
Journal: Behavioral and Brain Sciences (2024)

The meta-learning framework proposed by Binz et al. would gain significantly from the inclusion of affective and homeostatic elements, currently neglected in their work. These components are crucial as cognition as we know it is profoundly influenced by affective states, which arise as intricate forms of homeostatic regulation in living bodies.
MTA-cooperative PRMT5 inhibitors enhance T cell-mediated antitumor activity in MTAP-loss tumors
Authors: Si Chen, Jiakai Hou, Roshni Jaffery, Ashley Guerrero, Rongjie Fu, Leilei Shi, Ningbo Zheng, Ritu Bohat, Nicholas A Egan, Chengtai Yu, Sana Sharif, Yue Lu, Wei He, Shuyue Wang, Donjeta Gjuka, Everett M Stone, Pooja Anil Shah, Jordi Rodon Ahnert, Taiping Chen, Xinli Liu, Mark T Bedford, Han Xu, Weiyi Peng
Journal: Journal for ImmunoTherapy of Cancer (2024)

Hyperactivated protein arginine methyltransferases (PRMTs) are implicated in human cancers. Inhibiting tumor intrinsic PRMT5 was reported to potentiate antitumor immune responses, highlighting the possibility of combining PRMT5 inhibitors (PRMT5i) with cancer immunotherapy. However, global suppression of PRMT5 activity impairs the effector functions of immune cells. Here, we sought to identify strategies to specifically inhibit PRMT5 activity in tumor tissues and develop effective PRMT5i-based immuno-oncology (IO) combinations for cancer treatment, particularly for methylthioadenosine phosphorylase (MTAP)-loss cancer.
BCMA-BBZ-OX40 CAR-T Therapy Using an Instant Manufacturing Platform in Multiple Myeloma
Authors: Ting Wang, Yazi Yang, Lie Ma, Ru Feng, Jiangtao Li, Chunli Zhang, Jiefei Bai, Yanping Ding, Guanghua Liu, Fei Wu, Xinan Lu, Shi Feng, Zhenling Li, Ting He, Jian Li, Hui Liu
Journal: Journal for ImmunoTherapy of Cancer (2024)

Background Chimeric antigen receptor (CAR)-T cell has revolutionary efficacy against relapsed/refractory multiple myeloma (R/R MM). However, current CAR-T cell therapy has several limitations including long vein-to-vein time and limited viability. Methods A 4-1BB-costimulated B-cell maturation antigen (BCMA) CAR-T integrating an independently-expressed OX40 (BCMA-BBZ-OX40) was designed and generated by a traditional manufacturing process (TraditionCART) or instant manufacturing platform (named InstanCART). The tumor-killing efficiency, differentiation, exhaustion, and expansion level were investigated in vitro and in tumor-bearing mice. An investigator-initiated clinical trial was performed in patients with R/R MM to evaluate the outcomes of both TraditionCART and InstanCART. The primary objective was safety within 1 month after CAR-T cell infusion. The secondary objective was the best overall response rate. Results Preclinical studies revealed that integrated OX40 conferred BCMA CAR-T cells with superior cytotoxicity and reduced exhaustion levels. InstanCART process further enhanced the proliferation and T-cell stemness of BCMA-BBZ-OX40 CAR-T cells. BCMA-BBZ-OX40 CAR-T cells were successfully administered in 22 patients with R/R MM, including 15 patients with TraditionCART and 7 patients with InstanCART. Up to 50% (11/22) patients had a high-risk cytogenetic profile and 36% (8/22) had extramedullary disease. CAR-T therapy caused grade 1–2 cytokine release syndrome in 19/22 (80%) patients, grade 1 neurotoxicity in 2/22 (9%) patients and led to ≥grade 3 adverse events including neutropenia (20/22, 91%), thrombocytopenia (15/22, 68%), anemia (12/22, 55%), creatinine increased (1/22, 5%), hepatic enzymes increased (5/22, 23%), and sepsis (1/22, 5%). The best overall response rate was 100%, and 64% (14/22) of the patients had a complete response or better. The median manufacturing time was shorter for InstanCART therapy (3 days) than for TraditionCART therapy (10 days). Expansion and duration were dramatically higher for InstanCART cells than for TraditionCART cells. Conclusions BCMA-BBZ-OX40 CAR-T cells were well tolerated and exhibited potent responses in patients with R/R MM. InstanCART shortened the manufacturing period compared to TraditionCART, and improved the cellular kinetics. Our results demonstrated the potency and feasibility of OX40-modified BCMA CAR-T cells using InstanCART technology for R/R MM therapy. Trial registration number This trial was registered at www.clinicaltrials.gov as # NCT04537442 .
At the interface of tumor-associated macrophages and fibroblasts: immune-suppressive networks and emerging exploitable targets
Authors: Eleonora Timperi, Hugo Croizer, Darya Khantakova, Mhaidly Rana, Martina Molgora, Jennifer L. Guerriero, Fatima Mechta-Grigoriou, Emanuela Romano
Journal: Clinical Cancer Research (2024)

Tumor-associated macrophages (TAMs) constitute a prominent immune cell population within various solid cancers, playing a pivotal role in disease progression. Their increased numbers and frequencies often strongly correlate with resistance to therapy and reduced overall survival rates. Within the complex ecosystem of the tumor microenvironment (TME), activated cancer-associated fibroblasts (CAFs) are expanded and contribute significantly to tumor growth and metastasis, and to chemo- or immune-therapy resistance. CAFs exert a critical influence on TAM phenotype and functions by orchestrating the reprogramming of tissue-infiltrating monocytes, thereby modulating their survival and differentiation. This reciprocal interaction between TAMs and CAFs forms a crucial axis in fostering a suppressive crosstalk within the TME, mediated by a diverse array of signals exchanged between these cell types. Recent advancements in single-cell RNA sequencing (sc-RNA seq) technologies and spatial transcriptomics have enhanced our comprehension of the signaling dynamics at the interface between TAMs and CAFs, including their spatial distribution within the tissue. In this review, we delve into the latest discoveries elucidating the biology of TAM and CAF crosstalk. We examine the complexity of TAM-CAF and CAF-TAM interactions within the TME of solid cancers, with particular focus on ligand-receptor interactions and clinically significant targets for novel therapeutic strategies.
Low-dose clonidine infusion to improve sleep in postoperative patients in the high-dependency unit. A randomised placebo-controlled single-centre trial
Authors: David Liu, Elizabeth Hallt, Alanna Platz, Alain Humblet, Melissa Lassig-Smith, Janine Stuart, Cheryl Fourie, Amelia Livermore, Bree-Yana McConnochie, Therese Starr, Kymberley Herbst, Christine A Woods, Jason M Pincus, Michael C Reade
Journal: Intensive Care Medicine (2024)
Obesity-related Asthma: A Pathobiology-based Overview of Existing and Emerging Treatment Approaches
Authors: Meghan D. Althoff, Kristina Gaietto, Fernando Holguin, Erick Forno
Journal: American Journal of Respiratory and Critical Care Medicine (2024)

Although obesity-related asthma is associated with worse asthma outcomes, optimal treatment approaches for this complex phenotype are still largely unavailable. This state-of-the-art review article synthesizes evidence for existing and emerging treatment approaches for obesity-related asthma and highlights pathways that offer potential targets for novel therapeutics. Existing treatments targeting insulin resistance and obesity, including metformin and GLP-1 (glucagon-like-peptide 1) receptor agonists, have been associated with improved asthma outcomes, although GLP-1R agonist data in asthma are limited to individuals with comorbid obesity. Monoclonal antibodies approved for treatment of moderate to severe asthma generally appear to be effective in individuals with obesity, although this is based on retrospective or secondary analysis of clinical trials; moreover, although most of these asthma biologics are approved for use in the pediatric population, the impact of obesity on their efficacy has not been well studied in youth. Potential therapeutic targets being investigated include IL-6, arginine metabolites, nitro-fatty acids, and mitochondrial antioxidants, with clinical trials for each currently underway. Potential therapeutic targets include adipose tissue eosinophils and the GLP-1-arginine-advanced glycation end products axis, although data in humans are still needed. Finally, transcriptomic and epigenetic studies of "obese asthma" demonstrate enrichment of IFN-related signaling pathways, Rho-GTPase pathways, and integrins, suggesting that these too could represent future treatment targets. We advocate for further study of these potential therapeutic mechanisms and continued investigation of the distinct inflammatory pathways characteristic of obesity-related asthma, to facilitate effective treatment development for this unique asthma phenotype.
Metabolomic Fingerprints of Medical Therapy Versus Bariatric Surgery in Patients With Obesity and Type 2 Diabetes: The STAMPEDE Trial
Authors: Christopher L. Axelrod, Adithya Hari, Wagner S. Dantas, Sangeeta R. Kashyap, Philip R. Schauer, John P. Kirwan
Journal: Diabetes Care (2024)

Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) are effective procedures to treat and manage type 2 diabetes (T2D). However, the underlying metabolic adaptations that mediate improvements in glucose homeostasis remain largely elusive. The purpose of this study was to identify metabolic signatures associated with biochemical resolution of T2D after medical therapy (MT) or bariatric surgery.
Validation of Systemic Complement Signatures in the Progression of Chronic Obstructive Pulmonary Disease
Authors: Katarina M. DiLillo, Lisa Ruvuna, Katherine A. Pratte, Karina A. Serban, Wassim W. Labaki, MeiLan K. Han, Christine M Freeman, Russell P. Bowler, Jeffrey L. Curtis, Kelly B Arnold
Journal: American Journal of Respiratory and Critical Care Medicine (2024)