Advances of Recombinant Adenoviral Vectors in Preclinical and Clinical Applications.

Adenoviruses (Ad) have the potential to induce severe infections in vulnerable patient groups. Therefore, understanding Ad biology and antiviral processes is important to comprehend the signaling cascades during an infection and to initiate appropriate diagnostic and therapeutic interventions. In addition, Ad vector-based vaccines have revealed significant potential in generating robust immune protection and recombinant Ad vectors facilitate efficient gene transfer to treat genetic diseases and are used as oncolytic viruses to treat cancer. Continuous improvements in gene delivery capacity, coupled with advancements in production methods, have enabled widespread application in cancer therapy, vaccine development, and gene therapy on a large scale. This review provides a comprehensive overview of the virus biology, and several aspects of recombinant Ad vectors, as well as the development of Ad vector, are discussed. Moreover, we focus on those Ads that were used in preclinical and clinical applications including regenerative medicine, vaccine development, genome engineering, treatment of genetic diseases, and virotherapy in tumor treatment.

Development of oncolytic and gene therapy vectors based on adenovirus serotype 4 as an alternative to adenovirus serotype 5.

BACKGROUND: Adenoviral vectors are among the most frequently used vectors for gene therapy and cancer treatment. Most vectors are derived from human adenovirus (Ad) serotype 5 despite limited applicability caused by pre-existing immunity and unfavorable liver tropism, whereas the other more than 100 known human serotypes remain largely unused. Here, we screened a library of human Ad types and identified Ad4 as a promising candidate vector. METHODS: Reporter-gene-expressing viruses representative of the natural human Ad diversity were used to transduce an array of muscle cell lines and two- or three-dimensional tumor cultures. The time-course of transgene expression was monitored by fluorescence or luminescence measurements. To generate replication-deficient Ad4 vector genomes, successive homologous recombination was applied. RESULTS: Ad4, 17 and 50 transduced human cardiomyocytes more efficiently than Ad5, whereas Ad37 was found to be superior in rhabdomyocytes. Despite its moderate transduction efficiency, Ad4 showed efficient and long-lasting gene expression in papillomavirus (HPV) positive tumor organoids. Therefore, we aimed to harness the potential of Ad4 for improved muscle transduction or oncolytic virotherapy of HPV-positive tumors. We deleted the E1 and E3 transcription units to produce first generation Ad vectors for gene therapy. The E1- and E1/E3-deleted vectors were replication-competent in HEK293 cells stably expressing E1 but not in the other cell lines tested. Furthermore, we show that the Ad5 E1 transcription unit can complement the replication of E1-deleted Ad4 vectors. CONCLUSIONS: Our Ad4-based gene therapy vector platform contributes to the development of improved Ad vectors based on non-canonical serotypes for a broad range of applications.

Preschoolers search longer when there is more information to be gained.

What drives children to explore and learn when external rewards are uncertain or absent? Across three studies, we tested whether information gain itself acts as an internal reward and suffices to motivate children's actions. We measured 24-56-month-olds' persistence in a game where they had to search for an object (animal or toy), which they never find, hidden behind a series of doors, manipulating the degree of uncertainty about which specific object was hidden. We found that children were more persistent in their search when there was higher uncertainty, and therefore, more information to be gained with each action, highlighting the importance of research on artificial intelligence to invest in curiosity-driven algorithms. RESEARCH HIGHLIGHTS: Across three studies, we tested whether information gain itself acts as an internal reward and suffices to motivate preschoolers' actions. We measured preschoolers' persistence when searching for an object behind a series of doors, manipulating the uncertainty about which specific object was hidden. We found that preschoolers were more persistent when there was higher uncertainty, and therefore, more information to be gained with each action. Our results highlight the importance of research on artificial intelligence to invest in curiosity-driven algorithms.

Meta-Learned Models of Cognition.

Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. While the former requires the specification of a fixed set of computational structures and a definition of how these structures interact with each other, the latter necessitates the commitment to a particular prior and a likelihood function which - in combination with Bayes' rule - determine the model's behavior. In recent years, a new framework has established itself as a promising tool for building models of human cognition: the framework of meta-learning. In contrast to the previously mentioned model classes, meta-learned models acquire their inductive biases from experience, i.e., by repeatedly interacting with an environment. However, a coherent research program around meta-learned models of cognition is still missing to this day. The purpose of this article is to synthesize previous work in this field and establish such a research program. We accomplish this by pointing out that meta-learning can be used to construct Bayes-optimal learning algorithms, allowing us to draw strong connections to the rational analysis of cognition. We then discuss several advantages of the meta-learning framework over traditional methods and reexamine prior work in the context of these new insights.

The scaling of mental computation in a sorting task.

Many cognitive models provide valuable insights into human behavior. Yet the algorithmic complexity of candidate models can fail to capture how human reaction times scale with increasing input complexity. In the current work, we investigate the algorithms underlying human cognitive processes. Computer science characterizes algorithms by their time and space complexity scaling with problem size. We propose to use participants' reaction times to study how human computations scale with increasing input complexity. We tested this approach in a task where participants had to sort sequences of rectangles by their size. Our results showed that reaction times scaled close to linearly with sequence length and that participants learned and actively used latent structure whenever it was provided. This behavior was in line with a computational model that used the observed sequences to form hypotheses about the latent structures, searching through candidate hypotheses in a directed fashion. These results enrich our understanding of plausible cognitive models for efficient mental sorting and pave the way for future studies using reaction times to investigate the scaling of mental computations across psychological domains.

Developmental changes in exploration resemble stochastic optimization.

Human development is often described as a 'cooling off' process, analogous to stochastic optimization algorithms that implement a gradual reduction in randomness over time. Yet there is ambiguity in how to interpret this analogy, due to a lack of concrete empirical comparisons. Using data from n = 281 participants ages 5 to 55, we show that cooling off does not only apply to the single dimension of randomness. Rather, human development resembles an optimization process of multiple learning parameters, for example, reward generalization, uncertainty-directed exploration and random temperature. Rapid changes in parameters occur during childhood, but these changes plateau and converge to efficient values in adulthood. We show that while the developmental trajectory of human parameters is strikingly similar to several stochastic optimization algorithms, there are important differences in convergence. None of the optimization algorithms tested were able to discover reliably better regions of the strategy space than adult participants on this task.

Empowerment contributes to exploration behaviour in a creative video game.

Studies of human exploration frequently cast people as serendipitously stumbling upon good options. Yet these studies may not capture the richness of exploration strategies that people exhibit in more complex environments. Here we study behaviour in a large dataset of 29,493 players of the richly structured online game 'Little Alchemy 2'. In this game, players start with four elements, which they can combine to create up to 720 complex objects. We find that players are driven not only by external reward signals, such as an attempt to produce successful outcomes, but also by an intrinsic motivation to create objects that empower them to create even more objects. We find that this drive for empowerment is eliminated when playing a game variant that lacks recognizable semantics, indicating that people use their knowledge about the world and its possibilities to guide their exploration. Our results suggest that the drive for empowerment may be a potent source of intrinsic motivation in richly structured domains, particularly those that lack explicit reward signals.

Muscle Specific Promotors for Gene Therapy - A Comparative Study in Proliferating and Differentiated Cells.

BACKGROUND: Depending on the therapy approach and disease background, the heterogeneity of muscular tissues complicates the development of targeted gene therapy, where either expression in all muscle types or restriction to only one muscle type is warranted. Muscle specificity can be achieved using promotors mediating tissue specific and sustained physiological expression in the desired muscle types but limited activity in non-targeted tissue. Several muscle specific promotors have been described, but direct comparisons between them are lacking. OBJECTIVE: Here we present a direct comparison of muscle specific Desmin-, MHCK7, microRNA206- and Calpain3 promotor. METHODS: To directly compare these muscle specific promotors we utilized transfection of reporter plasmids using an in vitro model based on electrical pulse stimulation (EPS) to provoke sarcomere formation in 2D cell culture for quantification of promotor activities in far differentiated mouse and human myotubes. RESULTS: We found that Desmin- and MHCK7 promotors showed stronger reporter gene expression levels in proliferating and differentiated myogenic cell lines than miR206 and CAPN3 promotor. However, Desmin and MHCK7 promotor promoted gene expression also cardiac cells whereas miR206 and CAPN3 promotor expression was restricted to skeletal muscle. CONCLUSIONS: Our results provides direct comparison of muscle specific promotors with regard to expression strengths and specificity as this is important feature to avoid undesired transgene expression in non-target muscle cells for a desired therapy approach.

Chunking as a rational solution to the speed-accuracy trade-off in a serial reaction time task.

When exposed to perceptual and motor sequences, people are able to gradually identify patterns within and form a compact internal description of the sequence. One proposal of how sequences can be compressed is people's ability to form chunks. We study people's chunking behavior in a serial reaction time task. We relate chunk representation with sequence statistics and task demands, and propose a rational model of chunking that rearranges and concatenates its representation to jointly optimize for accuracy and speed. Our model predicts that participants should chunk more if chunks are indeed part of the generative model underlying a task and should, on average, learn longer chunks when optimizing for speed than optimizing for accuracy. We test these predictions in two experiments. In the first experiment, participants learn sequences with underlying chunks. In the second experiment, participants were instructed to act either as fast or as accurately as possible. The results of both experiments confirmed our model's predictions. Taken together, these results shed new light on the benefits of chunking and pave the way for future studies on step-wise representation learning in structured domains.

A clearer view of Southern Ocean air-sea interaction using surface heat flux asymmetry.

Progress in understanding Southern Ocean heat exchange and wind forcing is discussed and new results presented. These include a metric of the zonal asymmetry between surface ocean heat gain in the Atlantic/Indian sector and heat loss in the Pacific sector. The asymmetry arises from an intersector variation in the humidity gradient between the sea surface and near-surface atmosphere. This gradient increases by 60% in the Pacific sector enabling a 20 Wm-2 stronger latent heat loss compared with the Atlantic/Indian sector. The new metric is used for intercomparison of atmospheric reanalyses and CMIP6 climate simulations. CMIP6 has weaker Atlantic/Indian sector heat gain compared with the reanalyses primarily due to Indian Ocean sector differences. The potential for surface flux buoys to provide an observation-based counterpart to the asymmetry metric is explored. Over the past decade, flux buoys have been deployed at two sites (south of Tasmania and upstream of Drake Passage). The data record provided by these moorings is assessed and an argument developed for a third buoy to sample the Atlantic/Indian sector of the asymmetry metric. To close, we assess evidence that the main westerly wind belt has strengthened and moved southward in recent decades using the ERA5 reanalysis. This article is part of a discussion meeting issue 'Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities'.

Hippocampal spatio-predictive cognitive maps adaptively guide reward generalization.

The brain forms cognitive maps of relational knowledge-an organizing principle thought to underlie our ability to generalize and make inferences. However, how can a relevant map be selected in situations where a stimulus is embedded in multiple relational structures? Here, we find that both spatial and predictive cognitive maps influence generalization in a choice task, where spatial location determines reward magnitude. Mirroring behavior, the hippocampus not only builds a map of spatial relationships but also encodes the experienced transition structure. As the task progresses, participants' choices become more influenced by spatial relationships, reflected in a strengthening of the spatial map and a weakening of the predictive map. This change is driven by orbitofrontal cortex, which represents the degree to which an outcome is consistent with the spatial rather than the predictive map and updates hippocampal representations accordingly. Taken together, this demonstrates how hippocampal cognitive maps are used and updated flexibly for inference.

Targeting Oncolytic Adenoviruses to Cancer Cells Using a Designed Ankyrin Repeat Protein Lipocalin-2 Fusion Protein.

Oncolytic viruses are a promising technology to attack cancer cells and to recruit immune cells to the tumor site. Since the Lipocalin-2 receptor (LCN2R) is expressed on most cancer cells, we used its ligand LCN2 to target oncolytic adenoviruses (Ads) to cancer cells. Therefore, we fused a Designed Ankyrin Repeat Protein (DARPin) adapter binding the knob of Ad type 5 (knob5) to LCN2 to retarget the virus toward LCN2R with the aim of analyzing the basic characteristics of this novel targeting approach. The adapter was tested in vitro with Chinese Hamster Ovary (CHO) cells stably expressing the LCN2R and on 20 cancer cell lines (CCLs) using an Ad5 vector encoding luciferase and green fluorescent protein. Luciferase assays with the LCN2 adapter (LA) showed 10-fold higher infection compared with blocking adapter (BA) in CHO cells expressing LCN2R and in cells not expressing the LCN2R. Most CCLs showed an increased viral uptake of LA-bound virus compared with BA-bound virus and for five CCLs viral uptake was comparable to unmodified Ad5. Flow cytometry and hexon immunostainings also revealed increased uptake of LA-bound Ads compared with BA-bound Ads in most tested CCLs. Virus spread was studied in 3D cell culture models and nine CCLs showed increased and earlier fluorescence signals for LA-bound virus compared with BA-bound virus. Mechanistically, we show that the LA increases viral uptake only in the absence of its ligand Enterobactin (Ent) and independently of iron. Altogether, we characterized a novel DARPin-based system resulting in enhanced uptake demonstrating potential for future oncolytic virotherapy.

Using cognitive psychology to understand GPT-3.

We study GPT-3, a recent large language model, using tools from cognitive psychology. More specifically, we assess GPT-3's decision-making, information search, deliberation, and causal reasoning abilities on a battery of canonical experiments from the literature. We find that much of GPT-3's behavior is impressive: It solves vignette-based tasks similarly or better than human subjects, is able to make decent decisions from descriptions, outperforms humans in a multiarmed bandit task, and shows signatures of model-based reinforcement learning. Yet, we also find that small perturbations to vignette-based tasks can lead GPT-3 vastly astray, that it shows no signatures of directed exploration, and that it fails miserably in a causal reasoning task. Taken together, these results enrich our understanding of current large language models and pave the way for future investigations using tools from cognitive psychology to study increasingly capable and opaque artificial agents.

Context-dependent choice and evaluation in real-world consumer behavior.

A body of work spanning neuroscience, economics, and psychology indicates that decision-making is context-dependent, which means that the value of an option depends not only on the option in question, but also on the other options in the choice set-or the 'context'. While context effects have been observed primarily in small-scale laboratory studies with tightly constrained, artificially constructed choice sets, it remains to be determined whether these context effects take hold in real-world choice problems, where choice sets are large and decisions driven by rich histories of direct experience. Here, we investigate whether valuations are context-dependent in real-world choice by analyzing a massive restaurant rating dataset as well as two independent replication datasets which provide complementary operationalizations of restaurant choice. We find that users make fewer ratings-maximizing choices in choice sets with higher-rated options-a hallmark of context-dependent choice-and that post-choice restaurant ratings also varied systematically with the ratings of unchosen restaurants. Furthermore, in a follow-up laboratory experiment using hypothetical choice sets matched to the real-world data, we find further support for the idea that subjective valuations of restaurants are scaled in accordance with the choice context, providing corroborating evidence for a general mechanistic-level account of these effects. Taken together, our results provide a potent demonstration of context-dependent choice in real-world choice settings, manifesting both in decisions and subjective valuation of options.

Time pressure changes how people explore and respond to uncertainty.

How does time pressure influence exploration and decision-making? We investigated this question with several four-armed bandit tasks manipulating (within subjects) expected reward, uncertainty, and time pressure (limited vs. unlimited). With limited time, people have less opportunity to perform costly computations, thus shifting the cost-benefit balance of different exploration strategies. Through behavioral, reinforcement learning (RL), reaction time (RT), and evidence accumulation analyses, we show that time pressure changes how people explore and respond to uncertainty. Specifically, participants reduced their uncertainty-directed exploration under time pressure, were less value-directed, and repeated choices more often. Since our analyses relate uncertainty to slower responses and dampened evidence accumulation (i.e., drift rates), this demonstrates a resource-rational shift towards simpler, lower-cost strategies under time pressure. These results shed light on how people adapt their exploration and decision-making strategies to externally imposed cognitive constraints.

A Human In Vitro Model to Study Adenoviral Receptors and Virus Cell Interactions.

To develop adenoviral cell- or tissue-specific gene delivery, understanding of the infection mechanisms of adenoviruses is crucial. Several adenoviral attachment proteins such as CD46, CAR and sialic acid have been identified and studied. However, most receptor studies were performed on non-human cells. Combining our reporter gene-tagged adenovirus library with an in vitro human gene knockout model, we performed a systematic analysis of receptor usage comparing different adenoviruses side-by-side. The CRISPR/Cas9 system was used to knockout CD46 and CAR in the human lung epithelial carcinoma cell line A549. Knockout cells were infected with 22 luciferase-expressing adenoviruses derived from adenovirus species B, C, D and E. HAdV-B16, -B21 and -B50 from species B1 as well as HAdV-B34 and -B35 were found to be CD46-dependent. HAdV-C5 and HAdV-E4 from species E were found to be CAR-dependent. Regarding cell entry of HAdV-B3 and -B14 and all species D viruses, both CAR and CD46 play a role, and here, other receptors or attachment structures may also be important since transductions were reduced but not completely inhibited. The established human knockout cell model enables the identification of the most applicable adenovirus types for gene therapy and to further understand adenovirus infection biology.

Novel Group C Oncolytic Adenoviruses Carrying a miRNA Inhibitor Demonstrate Enhanced Oncolytic Activity In Vitro and In Vivo.

Oncolytic adenoviruses (OAd) represent an attractive treatment option for cancer. Clinical efficacy of commonly utilized human adenovirus type 5 (Ad5)-based oncolytic viruses is limited by variable expression levels of the coxsackie- and adenovirus receptor (CAR) in tumor cells and high prevalence of neutralizing antibodies against human Ad5. However, previous studies have highlighted alternative human Ad types as promising candidates for oncolytic therapy. In this study, we generated novel OAds based on Ad1, -2, -5, and -6 derived from species C Ads. These OAds contain a 24-bp deletion in the early gene E1A for tumor selective replication and express the RNAi inhibitor P19. We examined these OAds for in vitro anticancer activity on various cancer cell lines derived from lung, colon, gynecologic, bone, and pancreatic carcinoma. In most surveyed cell lines, OAds based on Ad1, -2, and -6 demonstrated higher cell lysis capability compared with Ad5, suggesting enhanced oncolytic potential. Moreover, enhanced oncolytic activity was associated with P19 expression in a cell type-dependent manner. We further explored a A549 tumor xenograft mouse model to compare the novel OAds directly with Ad5 and H101, an oncolytic adenovirus used in clinical trials. These P19-containing OAds based on Ad1, -2, and -6 showed significantly decelerated tumor progression compared with H101, indicating better antitumor potency in vivo. Our studies provide a novel path for OAd development based on alternative Ad types with improved effectiveness by RNA interference suppression.

Heuristics from bounded meta-learned inference.

Numerous researchers have put forward heuristics as models of human decision-making. However, where such heuristics come from is still a topic of ongoing debate. In this work, we propose a novel computational model that advances our understanding of heuristic decision-making by explaining how different heuristics are discovered and how they are selected. This model-called bounded meta-learned inference (BMI)-is based on the idea that people make environment-specific inferences about which strategies to use while being efficient in terms of how they use computational resources. We show that our approach discovers two previously suggested types of heuristics-one reason decision-making and equal weighting-in specific environments. Furthermore, the model provides clear and precise predictions about when each heuristic should be applied: Knowing the correct ranking of attributes leads to one reason decision-making, knowing the directions of the attributes leads to equal weighting, and not knowing about either leads to strategies that use weighted combinations of multiple attributes. In three empirical paired comparison studies with continuous features, we verify predictions of our theory and show that it captures several characteristics of human decision-making not explained by alternative theories. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Seroprevalence of Binding and Neutralizing Antibodies against 39 Human Adenovirus Types in Patients with Neuromuscular Disorders.

High pre-existing antibodies against viral vectors reduce their functionality and may lead to adverse complications. To circumvent this problem in future gene therapy approaches, we tested the seroprevalence of a large range of human adenovirus types in patients with neuromuscular disorders (NMDs) to find appropriate viral vector candidates for gene replacement therapy for NMDs. Binding and neutralizing antibodies against 39 human adenovirus types were tested in the sera of 133 patients with NMDs and 76 healthy controls aged 17-92 years. The influence of age, sex, and NMDs on antibody levels was analyzed. The seroprevalence of different adenoviruses in the cohort varied widely. The highest levels of binding antibodies were detected against HAdV-D27, -C1, -D24, -D70, -B14, -C6, -D13, -B34, and -E4, whereas the lowest reactivity was detected against HAdV-F41, -A31, -B11, -D75, -D8, -D65, -D26, -D80, and -D17. The highest neutralizing reactivity was observed against HAdV-B3, -C2, -E4, -C1, -G52, -C5, and -F41, whereas the lowest neutralizing reactivity was observed against HAdV-D74, -B34, -D73, -B37, -D48, -D13, -D75, -D8, -B35, and -B16. We detected no influence of sex and only minor differences between different age groups. Importantly, there were no significant differences between healthy controls and patients with NMDs. Our data show that patients with NMDs have very similar levels of binding and neutralizing antibodies against HAdV compared to healthy individuals, and we identified HAdV-A31, -B16, -B34, -B35, -D8, -D37, -D48, -D73, -D74, -D75, and -D80 as promising candidates for future vector development due to their low binding and neutralizing antibody prevalence.

Development of directed and random exploration in children.

Are young children just random explorers who learn serendipitously? Or are even young children guided by uncertainty-directed sampling, seeking to explore in a systematic fashion? We study how children between the ages of 4 and 9 search in an explore-exploit task with spatially correlated rewards, where exhaustive exploration is infeasible and not all options can be experienced. By combining behavioral data with a computational model that decomposes search into similarity-based generalization, uncertainty-directed exploration, and random exploration, we map out developmental trajectories of generalization and exploration. The behavioral data show strong developmental differences in children's capability to exploit environmental structure, with performance and adaptiveness of sampling decisions increasing with age. Through model-based analyses, we disentangle different forms of exploration, finding signature of both uncertainty-directed and random exploration. The amount of random exploration strongly decreases as children get older, supporting the notion of a developmental "cooling off" process that modulates the randomness in sampling. However, even at the youngest age range, children do not solely rely on random exploration. Even as random exploration begins to taper off, children are actively seeking out options with high uncertainty in a goal-directed fashion, and using inductive inferences to generalize their experience to novel options. Our findings provide critical insights into the behavioral and computational principles underlying the developmental trajectory of learning and exploration.