An AsCas12f-based compact genome-editing tool derived by deep mutational scanning and structural analysis.

SpCas9 and AsCas12a are widely utilized as genome-editing tools in human cells. However, their relatively large size poses a limitation for delivery by cargo-size-limited adeno-associated virus (AAV) vectors. The type V-F Cas12f from Acidibacillus sulfuroxidans is exceptionally compact (422 amino acids) and has been harnessed as a compact genome-editing tool. Here, we developed an approach, combining deep mutational scanning and structure-informed design, to successfully generate two AsCas12f activity-enhanced (enAsCas12f) variants. Remarkably, the enAsCas12f variants exhibited genome-editing activities in human cells comparable with those of SpCas9 and AsCas12a. The cryoelectron microscopy (cryo-EM) structures revealed that the mutations stabilize the dimer formation and reinforce interactions with nucleic acids to enhance their DNA cleavage activities. Moreover, enAsCas12f packaged with partner genes in an all-in-one AAV vector exhibited efficient knock-in/knock-out activities and transcriptional activation in mice. Taken together, enAsCas12f variants could offer a minimal genome-editing platform for in vivo gene therapy.

Enhancer-promoter interactions are reconfigured through the formation of long-range multiway hubs as mouse ES cells exit pluripotency.

Enhancers bind transcription factors, chromatin regulators, and non-coding transcripts to modulate the expression of target genes. Here, we report 3D genome structures of single mouse ES cells as they are induced to exit pluripotency and transition through a formative stage prior to undergoing neuroectodermal differentiation. We find that there is a remarkable reorganization of 3D genome structure where inter-chromosomal intermingling increases dramatically in the formative state. This intermingling is associated with the formation of a large number of multiway hubs that bring together enhancers and promoters with similar chromatin states from typically 5-8 distant chromosomal sites that are often separated by many Mb from each other. In the formative state, genes important for pluripotency exit establish contacts with emerging enhancers within these multiway hubs, suggesting that the structural changes we have observed may play an important role in modulating transcription and establishing new cell identities.

Zoom out: An intervention on the virtual learning environment improves minority students' grades in two field experiments in Israel.

Closing the achievement gap for minority students in higher education requires addressing the lack of belonging these students experience. This paper introduces a psychological intervention that strategically targets key elements within the learning environment to foster the success of minority students. The intervention sought to enhance Palestinian minority student's sense of belonging by increasing the presence of their native language. We tested the effectiveness of the intervention in two field experiments in Israel (n > 20,000), at the height of the COVID-19 pandemic when all classes were held via Zoom. Lecturers in the experimental condition added a transcript of their names in Arabic to their default display (English/Hebrew only). Our findings revealed a substantial and positive impact on Palestinian student's sense of belonging, class participation, and overall grades. In experiment 1, Palestinian student's average grade increased by 10 points. In experiment 2, there was an average increase of 4 points among Palestinian students' semester grade. Our intervention demonstrates that small institutional changes when carefully crafted can have a significant impact on minority populations. These results have significant implications for addressing educational disparities and fostering inclusive learning environment.

Self-assembled active actomyosin gels spontaneously curve and wrinkle similar to biological cells and tissues.

Living systems adopt a diversity of curved and highly dynamic shapes. These diverse morphologies appear on many length scales, from cells to tissues and organismal scales. The common driving force for these dynamic shape changes are contractile stresses generated by myosin motors in the cell cytoskeleton, that converts chemical energy into mechanical work. A good understanding of how contractile stresses in the cytoskeleton arise into different three-dimensional (3D) shapes and what are the shape selection rules that determine their final configurations is still lacking. To obtain insight into the relevant physical mechanisms, we recreate the actomyosin cytoskeleton in vitro, with precisely controlled composition and initial geometry. A set of actomyosin gel discs, intrinsically identical but of variable initial geometry, dynamically self-organize into a family of 3D shapes, such as domes and wrinkled shapes, without the need for specific preprogramming or additional regulation. Shape deformation is driven by the spontaneous emergence of stress gradients driven by myosin and is encoded in the initial disc radius to thickness aspect ratio, which may indicate shaping scalability. Our results suggest that while the dynamical pathways may depend on the detailed interactions between the different microscopic components within the gel, the final selected shapes obey the general theory of elastic deformations of thin sheets. Altogether, our results emphasize the importance for the emergence of active stress gradients for buckling-driven shape deformations and provide insights on the mechanically induced spontaneous shape transitions in contractile active matter, revealing potential shared mechanisms with living systems across scales.

General theory for localizing the where and when of entropy production meets single-molecule experiments.

The laws of thermodynamics apply to biophysical systems on the nanoscale as described by the framework of stochastic thermodynamics. This theory provides universal, exact relations for quantities like work, which have been verified in experiments where a fully resolved description allows direct access to such quantities. Complementary studies consider partially hidden, coarse-grained descriptions, in which the mean entropy production typically is not directly accessible but can be bounded in terms of observable quantities. Going beyond the mean, we introduce a fluctuating entropy production that applies to individual trajectories in a coarse-grained description under time-dependent driving. Thus, this concept is applicable to the broad and experimentally significant class of driven systems in which not all relevant states can be resolved. We provide a paradigmatic example by studying an experimentally verified protein unfolding process. As a consequence, the entire distribution of the coarse-grained entropy production rather than merely its mean retains spatial and temporal information about the microscopic process. In particular, we obtain a bound on the distribution of the physical entropy production of individual unfolding events.

An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability.

Traditionally, scientists have placed more emphasis on communicating inferential uncertainty (i.e., the precision of statistical estimates) compared to outcome variability (i.e., the predictability of individual outcomes). Here, we show that this can lead to sizable misperceptions about the implications of scientific results. Specifically, we present three preregistered, randomized experiments where participants saw the same scientific findings visualized as showing only inferential uncertainty, only outcome variability, or both and answered questions about the size and importance of findings they were shown. Our results, composed of responses from medical professionals, professional data scientists, and tenure-track faculty, show that the prevalent form of visualizing only inferential uncertainty can lead to significant overestimates of treatment effects, even among highly trained experts. In contrast, we find that depicting both inferential uncertainty and outcome variability leads to more accurate perceptions of results while appearing to leave other subjective impressions of the results unchanged, on average.

Educating for inclusion: Diversity education programs can reduce prejudice toward outgroups in Israel.

Intergroup prejudice is pervasive in many contexts worldwide, leading to discrimination and conflict. Existing research suggests that prejudice is acquired at an early age and that durably improving intergroup relations is extremely challenging, often requiring intense interventions. Building on existing research in social psychology and inspired by the Israeli TV series "You Can't Ask That," which depicts charismatic children from minority groups broaching sensitive topics at the core of intergroup relations, we develop a month-long diversity education program. Our program exposed students to the TV series and facilitated follow-up classroom discussions in which students constructively addressed various sensitive topics at the core of intergroup relations and learned about intergroup similarities, intragroup heterogeneity, and the value of taking others' perspectives. Through two field experiments implemented in Israeli schools, we show that integrating our intervention into school curricula improved Jewish students' attitudes toward minorities and increased some pro-diversity behavior up to 13 wk posttreatment. We further provide suggestive evidence that the intervention was effective by encouraging students to take their outgroups' perspectives and address an element of scalability by delegating implementation responsibilities to classroom teachers in our second study. Our findings suggest that theoretically informed intensive education programs are a promising route to reducing prejudice at a young age.

System-, teacher-, and student-level interventions for improving participation in online learning at scale in high schools.

Many school systems across the globe turned to online education during the COVID-19 pandemic. This context differs significantly from the prepandemic situation in which massive open online courses attracted large numbers of voluntary learners who struggled with completion. Students who are provided online courses by their high schools also have their behavior determined by actions of their teachers and school system. We conducted experiments to improve participation in online learning before, during, and right after the COVID-19 outbreak, with 1,151 schools covering more than 45,000 students in their final years of high school in Ecuador. These experiments tested light-touch interventions at scale, motivated by behavioral science, and were carried out at three levels: that of the system, teacher, and student. We find the largest impacts come from intervening at the system level. A cheap, online learning management system for centralized monitoring increased participation by 0.21 SD and subject knowledge by 0.13 SD relative to decentralized management. Centralized management is particularly effective for underperforming schools. Teacher-level nudges in the form of benchmarking emails, encouragement messages, and administrative reminders did not improve student participation. There was no significant impact of encouragement messages to students, or in having them plan and team-up with peers. Small financial incentives in the form of lottery prizes for finishing lessons did increase study time, but was less cost-effective, and had no significant impact on knowledge. The results show the difficulty in incentivizing online learning at scale, and a key role for central monitoring.

Emergence and collapse of reciprocity in semiautomatic driving coordination experiments with humans.

Forms of both simple and complex machine intelligence are increasingly acting within human groups in order to affect collective outcomes. Considering the nature of collective action problems, however, such involvement could paradoxically and unintentionally suppress existing beneficial social norms in humans, such as those involving cooperation. Here, we test theoretical predictions about such an effect using a unique cyber-physical lab experiment where online participants (N = 300 in 150 dyads) drive robotic vehicles remotely in a coordination game. We show that autobraking assistance increases human altruism, such as giving way to others, and that communication helps people to make mutual concessions. On the other hand, autosteering assistance completely inhibits the emergence of reciprocity between people in favor of self-interest maximization. The negative social repercussions persist even after the assistance system is deactivated. Furthermore, adding communication capabilities does not relieve this inhibition of reciprocity because people rarely communicate in the presence of autosteering assistance. Our findings suggest that active safety assistance (a form of simple AI support) can alter the dynamics of social coordination between people, including by affecting the trade-off between individual safety and social reciprocity. The difference between autobraking and autosteering assistance appears to relate to whether the assistive technology supports or replaces human agency in social coordination dilemmas. Humans have developed norms of reciprocity to address collective challenges, but such tacit understandings could break down in situations where machine intelligence is involved in human decision-making without having any normative commitments.

How Can People Become Happier? A Systematic Review of Preregistered Experiments.

Can happiness be reliably increased? Thousands of studies speak to this question. However, many of them were conducted during a period in which researchers commonly "p-hacked," creating uncertainty about how many discoveries might be false positives. To prevent p-hacking, happiness researchers increasingly preregister their studies, committing to analysis plans before analyzing data. We conducted a systematic literature search to identify preregistered experiments testing strategies for increasing happiness. We found surprisingly little support for many widely recommended strategies (e.g., performing random acts of kindness). However, our review suggests that other strategies-such as being more sociable-may reliably promote happiness. We also found strong evidence that governments and organizations can improve happiness by providing underprivileged individuals with financial support. We conclude that happiness research stands on the brink of an exciting new era, in which modern best practices will be applied to develop theoretically grounded strategies that can produce lasting gains in life satisfaction.

Impression management attenuates the effect of ability on trust in economic exchange.

Are competent actors still trusted when they promote themselves? The answer to this question could have far-reaching implications for understanding trust production in a variety of economic exchange settings in which ability and impression management play vital roles, from succeeding in one's job to excelling in the sales of goods and services. Much social science research assumes an unconditional positive impact of an actor's ability on the trust placed in that actor: in other words, competence breeds trust. In this report, however, we challenge this assumption. Across a series of experiments, we manipulated both the ability and the self-promotion of a trustee and measured the level of trust received. Employing both online laboratory studies (n = 5,606) and a field experiment (n = 101,520), we find that impression management tactics (i.e., self-promotion and intimidation) can substantially backfire, at least for those with high ability. An explanation for this effect is encapsuled in attribution theory, which argues that capable actors are held to higher standards in terms of how kind and honest they are expected to be. Consistent with our social attribution account, mediation analyses show that competence combined with self-promotion decreases the trustee's perceived benevolence and integrity and, in turn, the level of trust placed in that actor.

Human HELB is a processive motor protein that catalyzes RPA clearance from single-stranded DNA.

Human DNA helicase B (HELB) is a poorly characterized helicase suggested to play both positive and negative regulatory roles in DNA replication and recombination. In this work, we used bulk and single-molecule approaches to characterize the biochemical activities of HELB protein with a particular focus on its interactions with Replication Protein A (RPA) and RPA­single-stranded DNA (ssDNA) filaments. HELB is a monomeric protein that binds tightly to ssDNA with a site size of ∼20 nucleotides. It couples ATP hydrolysis to translocation along ssDNA in the 5' to 3' direction accompanied by the formation of DNA loops. HELB also displays classical helicase activity, but this is very weak in the absence of an assisting force. HELB binds specifically to human RPA, which enhances its ATPase and ssDNA translocase activities but inhibits DNA unwinding. Direct observation of HELB on RPA nucleoprotein filaments shows that translocating HELB concomitantly clears RPA from ssDNA. This activity, which can allow other proteins access to ssDNA intermediates despite their shielding by RPA, may underpin the diverse roles of HELB in cellular DNA transactions.

Net effect of environmental fluctuations in multiple global-change drivers across the tree of life.

Jensen's inequality predicts that the response of any given system to average constant conditions is different from its average response to varying ones. Environmental fluctuations in abiotic conditions are pervasive on Earth; yet until recently, most ecological research has addressed the effects of multiple environmental drivers by assuming constant conditions. One could thus expect to find significant deviations in the magnitude of their effects on ecosystems when environmental fluctuations are considered. Drawing on experimental studies published during the last 30 years reporting more than 950 response ratios (n = 5,700), we present a comprehensive analysis of the role that environmental fluctuations play across the tree of life. In contrast to the predominance of interactive effects of global-change drivers reported in the literature, our results show that their cumulative effects were additive (58%), synergistic (26%), and antagonistic (16%) when environmental fluctuations were present. However, the dominant type of interaction varied by trophic level (autotrophs: interactive; heterotrophs: additive) and phylogenetic group (additive in Animalia; additive and positive antagonism in Chromista; negative antagonism and synergism in Plantae). In addition, we identify the need to tackle how complex communities respond to fluctuating environments, widening the phylogenetic and biogeographic ranges considered, and to consider other drivers beyond warming and acidification as well as longer timescales. Environmental fluctuations must be taken into account in experimental and modeling studies as well as conservation plans to better predict the nature, magnitude, and direction of the impacts of global change on organisms and ecosystems.

Measuring Electrical Resistivity at the Nanoscale in Phase-Change Materials.

Electrical resistivity is the key parameter in the active regions of many current nanoscale devices, from memristors to resistive random-access memory and phase-change memories. The local resistivity of the materials is engineered on the nanoscale to fit the performance requirements. Phase-change memories, for example, rely on materials whose electrical resistance increases dramatically with a change from a crystalline to an amorphous phase. Electrical characterization methods have been developed to measure the response of individual devices, but they cannot map the local resistance across the active area. Here, we propose a method based on operando electron holography to determine the local resistance within working devices. Upon switching the device, we show that electrical resistance is inhomogeneous on the scale of only a few nanometers.

Modeling relaxation experiments with a mechanistic model of gene expression.

BACKGROUND: In the present work, we aimed at modeling a relaxation experiment which consists in selecting a subfraction of a cell population and observing the speed at which the entire initial distribution for a given marker is reconstituted. METHODS: For this we first proposed a modification of a previously published mechanistic two-state model of gene expression to which we added a state-dependent proliferation term. This results in a system of two partial differential equations. Under the assumption of a linear dependence of the proliferation rate with respect to the marker level, we could derive the asymptotic profile of the solutions of this model. RESULTS: In order to confront our model with experimental data, we generated a relaxation experiment of the CD34 antigen on the surface of TF1-BA cells, starting either from the highest or the lowest CD34 expression levels. We observed in both cases that after approximately 25 days the distribution of CD34 returns to its initial stationary state. Numerical simulations, based on parameter values estimated from the dataset, have shown that the model solutions closely align with the experimental data from the relaxation experiments. CONCLUSION: Altogether our results strongly support the notion that cells should be seen and modeled as probabilistic dynamical systems.

A strategic approach for efficient cryo-EM grid optimization using design of experiments.

In recent years, cryo-electron microscopy (cryo-EM) has become a practical and effective method of determining structures at previously unattainable resolutions due to advances in detection, automation, and data processing. However, sample preparation remains a major bottleneck in the cryo-EM workflow. Even after the arduous process of biochemical sample optimization, it often takes several iterations of grid vitrification and screening to determine the optimal grid freezing parameters that yield suitable ice thickness and particle distribution for data collection. Since a high-quality sample is imperative for high-resolution structure determination, grid optimization is a vital step. For researchers who rely on cryo-EM facilities for grid screening, each iteration of this optimization process may delay research progress by a matter of months. Therefore, a more strategic and efficient approach should be taken to ensure that the grid optimization process can be completed in as few iterations as possible. Here, we present an implementation of Design of Experiments (DOE) to expedite and strategize the grid optimization process. A Fractional Factorial Design (FFD) guides the determination of a limited set of experimental conditions which can model the full parameter space of interest. Grids are frozen with these conditions and screened for particle distribution and ice thickness. Quantitative scores are assigned to each of these grid characteristics based on a qualitative rubric. Input conditions and response scores are used to generate a least-squares regression model of the parameter space in JMP, which is used to determine the conditions which should, in theory, yield optimal grids. Upon testing this approach on apoferritin and L-glutamate dehydrogenase on both the Vitrobot Mark IV and the Leica GP2 plunge freezers, the resulting grid conditions reliably yielded grids with high-quality ice and particle distribution that were suitable for collecting large overnight datasets on a Krios. We conclude that a DOE-based approach is a cost-effective and time-saving tool for cryo-EM grid preparation.

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks.

BACKGROUND: In recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process. To address the limitations of experimental methods, such as cost and time, several machine learning-based techniques have been developed. However, these methods encounter certain challenges, including the limited availability of training data, reliance on human intervention for feature selection and engineering, and a lack of validation approaches for robust evaluation in real-life applications. RESULTS: To mitigate these limitations, in this study, we propose a method for drug-target binding affinity prediction based on deep convolutional generative adversarial networks. Additionally, we conducted a series of validation experiments and implemented adversarial control experiments using straw models. These experiments serve to demonstrate the robustness and efficacy of our predictive models. We conducted a comprehensive evaluation of our method by comparing it to baselines and state-of-the-art methods. Two recently updated datasets, namely the BindingDB and PDBBind, were used for this purpose. Our findings indicate that our method outperforms the alternative methods in terms of three performance measures when using warm-start data splitting settings. Moreover, when considering physiochemical-based cold-start data splitting settings, our method demonstrates superior predictive performance, particularly in terms of the concordance index. CONCLUSION: The results of our study affirm the practical value of our method and its superiority over alternative approaches in predicting drug-target binding affinity across multiple validation sets. This highlights the potential of our approach in accelerating drug repurposing efforts, facilitating novel drug discovery, and ultimately enhancing disease treatment. The data and source code for this study were deposited in the GitHub repository, https://github.com/mojtabaze7/DCGAN-DTA . Furthermore, the web server for our method is accessible at https://dcgan.shinyapps.io/bindingaffinity/ .

Cell Survival Enabled by Leakage of a Labile Metabolic Intermediate.

Many metabolites are generated in one step of a biochemical pathway and consumed in a subsequent step. Such metabolic intermediates are often reactive molecules which, if allowed to freely diffuse in the intracellular milieu, could lead to undesirable side reactions and even become toxic to the cell. Therefore, metabolic intermediates are often protected as protein-bound species and directly transferred between enzyme active sites in multi-functional enzymes, multi-enzyme complexes, and metabolons. Sequestration of reactive metabolic intermediates thus contributes to metabolic efficiency. It is not known, however, whether this evolutionary adaptation can be relaxed in response to challenges to organismal survival. Here, we report evolutionary repair experiments on Escherichia coli cells in which an enzyme crucial for the biosynthesis of proline has been deleted. The deletion makes cells unable to grow in a culture medium lacking proline. Remarkably, however, cell growth is efficiently restored by many single mutations (12 at least) in the gene of glutamine synthetase. The mutations cause the leakage to the intracellular milieu of a highly reactive phosphorylated intermediate common to the biosynthetic pathways of glutamine and proline. This intermediate is generally assumed to exist only as a protein-bound species. Nevertheless, its diffusion upon mutation-induced leakage enables a new route to proline biosynthesis. Our results support that leakage of sequestered metabolic intermediates can readily occur and contribute to organismal adaptation in some scenarios. Enhanced availability of reactive molecules may enable the generation of new biochemical pathways and the potential of mutation-induced leakage in metabolic engineering is noted.

Quasi-experimental methods for pharmacoepidemiology: difference-in-differences and synthetic control methods with case studies for vaccine evaluation.

Difference-in-differences and synthetic control methods have become common study designs for evaluating the effects of changes in policies, including health policies. They also have potential for providing real-world effectiveness and safety evidence in pharmacoepidemiology. To effectively add to the toolkit of the field, however, designs-including both their benefits and drawbacks-must be well understood. Quasi-experimental designs provide an opportunity to estimate the average treatment effect on the treated without requiring the measurement of all possible confounding factors, and to assess population-level effects. This requires, however, other key assumptions, including the parallel trends or stable weighting assumptions, a lack of other concurrent events that could alter time trends, and an absence of contamination between exposed and unexposed units. The targeted estimands are also highly specific to the settings of the study, and combining across units or time periods can be challenging. Case studies are presented for 3 vaccine evaluation studies, showcasing some of these challenges and opportunities in a specific field of pharmacoepidemiology. These methods provide feasible and valuable sources of evidence in various pharmacoepidemiologic settings and can be improved through research to identify and weigh the advantages and disadvantages in those settings. This article is part of a Special Collection on Pharmacoepidemiology.

Through an animal's eye: the implications of diverse sensory systems in scientific experimentation.

'Accounting for the sensory abilities of animals is critical in experimental design.' No researcher would disagree with this statement, yet it is often the case that we inadvertently fall for anthropocentric biases and use ourselves as the reference point. This paper discusses the risks of adopting an anthropocentric view when working with non-human animals, and the unintended consequences this has on our experimental designs and results. To this aim, we provide general examples of anthropocentric bias from different fields of animal research, with a particular focus on animal cognition and behaviour, and lay out the potential consequences of adopting a human-based perspective. Knowledge of the sensory abilities, both in terms of similarities to humans and peculiarities of the investigated species, is crucial to ensure solid conclusions. A more careful consideration of the diverse sensory systems of animals would improve many scientific fields and enhance animal welfare in the laboratory.