Digital Museum of Retinal Ganglion Cells with Dense Anatomy and Physiology.

When 3D electron microscopy and calcium imaging are used to investigate the structure and function of neural circuits, the resulting datasets pose new challenges of visualization and interpretation. Here, we present a new kind of digital resource that encompasses almost 400 ganglion cells from a single patch of mouse retina. An online "museum" provides a 3D interactive view of each cell's anatomy, as well as graphs of its visual responses. The resource reveals two aspects of the retina's inner plexiform layer: an arbor segregation principle governing structure along the light axis and a density conservation principle governing structure in the tangential plane. Structure is related to visual function; ganglion cells with arbors near the layer of ganglion cell somas are more sustained in their visual responses on average. Our methods are potentially applicable to dense maps of neuronal anatomy and physiology in other parts of the nervous system.

Machine-learning-optimized Cas12a barcoding enables the recovery of single-cell lineages and transcriptional profiles.

The development of CRISPR-based barcoding methods creates an exciting opportunity to understand cellular phylogenies. We present a compact, tunable, high-capacity Cas12a barcoding system called dual acting inverted site array (DAISY). We combined high-throughput screening and machine learning to predict and optimize the 60-bp DAISY barcode sequences. After optimization, top-performing barcodes had ∼10-fold increased capacity relative to the best random-screened designs and performed reliably across diverse cell types. DAISY barcode arrays generated ∼12 bits of entropy and ∼66,000 unique barcodes. Thus, DAISY barcodes-at a fraction of the size of Cas9 barcodes-achieved high-capacity barcoding. We coupled DAISY barcoding with single-cell RNA-seq to recover lineages and gene expression profiles from ∼47,000 human melanoma cells. A single DAISY barcode recovered up to ∼700 lineages from one parental cell. This analysis revealed heritable single-cell gene expression and potential epigenetic modulation of memory gene transcription. Overall, Cas12a DAISY barcoding is an efficient tool for investigating cell-state dynamics.

Exposing omitted moderators: Explaining why effect sizes differ in the social sciences.

Policymakers increasingly rely on behavioral science in response to global challenges, such as climate change or global health crises. But applications of behavioral science face an important problem: Interventions often exert substantially different effects across contexts and individuals. We examine this heterogeneity for different paradigms that underlie many behavioral interventions. We study the paradigms in a series of five preregistered studies across one in-person and 10 online panels, with over 11,000 respondents in total. We find substantial heterogeneity across settings and paradigms, apply techniques for modeling the heterogeneity, and introduce a framework that measures typically omitted moderators. The framework's factors (Fluid Intelligence, Attentiveness, Crystallized Intelligence, and Experience) affect the effectiveness of many text-based interventions, producing different observed effect sizes and explaining variations across samples. Moderators are associated with effect sizes through two paths, with the intensity of the manipulation and with the effect of the manipulation directly. Our results motivate observing these moderators and provide a theoretical and empirical framework for understanding and predicting varying effect sizes in the social sciences.

The distorting effects of producer strategies: Why engagement does not reveal consumer preferences for misinformation.

A great deal of empirical research has examined who falls for misinformation and why. Here, we introduce a formal game-theoretic model of engagement with news stories that captures the strategic interplay between (mis)information consumers and producers. A key insight from the model is that observed patterns of engagement do not necessarily reflect the preferences of consumers. This is because producers seeking to promote misinformation can use strategies that lead moderately inattentive readers to engage more with false stories than true ones-even when readers prefer more accurate over less accurate information. We then empirically test people's preferences for accuracy in the news. In three studies, we find that people strongly prefer to click and share news they perceive as more accurate-both in a general population sample, and in a sample of users recruited through Twitter who had actually shared links to misinformation sites online. Despite this preference for accurate news-and consistent with the predictions of our model-we find markedly different engagement patterns for articles from misinformation versus mainstream news sites. Using 1,000 headlines from 20 misinformation and 20 mainstream news sites, we compare Facebook engagement data with 20,000 accuracy ratings collected in a survey experiment. Engagement with a headline is negatively correlated with perceived accuracy for misinformation sites, but positively correlated with perceived accuracy for mainstream sites. Taken together, these theoretical and empirical results suggest that consumer preferences cannot be straightforwardly inferred from empirical patterns of engagement.

Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence.

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out a dynamic analysis of coordinated behavior. To reach our goal, we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. We find that i) coordinated communities (CCs) feature variable degrees of temporal instability; ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; iii) some users exhibit distinct archetypal behaviors that have important practical implications; iv) content and network characteristics contribute to explaining why users leave and join CCs. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of CCs, and on the patterns of online influence.

Causally estimating the effect of YouTube's recommender system using counterfactual bots.

In recent years, critics of online platforms have raised concerns about the ability of recommendation algorithms to amplify problematic content, with potentially radicalizing consequences. However, attempts to evaluate the effect of recommenders have suffered from a lack of appropriate counterfactuals-what a user would have viewed in the absence of algorithmic recommendations-and hence cannot disentangle the effects of the algorithm from a user's intentions. Here we propose a method that we call "counterfactual bots" to causally estimate the role of algorithmic recommendations on the consumption of highly partisan content on YouTube. By comparing bots that replicate real users' consumption patterns with "counterfactual" bots that follow rule-based trajectories, we show that, on average, relying exclusively on the YouTube recommender results in less partisan consumption, where the effect is most pronounced for heavy partisan consumers. Following a similar method, we also show that if partisan consumers switch to moderate content, YouTube's sidebar recommender "forgets" their partisan preference within roughly 30 videos regardless of their prior history, while homepage recommendations shift more gradually toward moderate content. Overall, our findings indicate that, at least since the algorithm changes that YouTube implemented in 2019, individual consumption patterns mostly reflect individual preferences, where algorithmic recommendations play, if anything, a moderating role.

January 6 arrests and media coverage do not remobilize conservatives on social media.

Social media's pivotal role in catalyzing social movements is widely acknowledged across scientific disciplines. Past research has predominantly explored social media's ability to instigate initial mobilization while leaving the question of its capacity to sustain these movements relatively uncharted. This study investigates the persistence of movement activity on Twitter and Gab following a substantial on-the-ground mobilization event catalyzed by social media-the StoptheSteal movement culminating in the January 6th Capitol attack. Our findings indicate that the online communities active in the January 6 mobilization did not display substantial remobilization in the subsequent year. These results highlight the fact that further exploration is needed to understand the factors shaping how and when movements are sustained by social media. In this regard, our study provides valuable insights for scientists across diverse disciplines, on how certain social media platforms may contribute to the evolving dynamics of collective action.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

Unproctored online exams provide meaningful assessment of student learning.

In the United States, the onset of COVID-19 triggered a nationwide lockdown, which forced many universities to move their primary assessments from invigilated in-person exams to unproctored online exams. This abrupt change occurred midway through the Spring 2020 semester, providing an unprecedented opportunity to investigate whether online exams can provide meaningful assessments of learning relative to in-person exams on a per-student basis. Here, we present data from nearly 2,000 students across 18 courses at a large Midwestern University. Using a meta-analytic approach in which we treated each course as a separate study, we showed that online exams produced scores that highly resembled those from in-person exams at an individual level despite the online exams being unproctored-as demonstrated by a robust correlation between online and in-person exam scores. Moreover, our data showed that cheating was either not widespread or ineffective at boosting scores, and the strong assessment value of online exams was observed regardless of the type of questions asked on the exam, the course level, academic discipline, or class size. We conclude that online exams, even when unproctored, are a viable assessment tool.

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.

Directing smartphone use through the self-nudge app one sec.

Research suggests various associations of smartphone use with a range of physical, psychological, and performance dimensions. Here, we test one sec, a self-nudging app that is installed by the user in order to reduce the mindless use of selected target apps on the smartphone. When users attempt to open a target app of their choice, one sec interferes with a pop-up, which combines a deliberation message, friction by a short waiting time, and the option to dismiss opening the target app. In a field-experiment, we collected behavioral user data from 280 participants over 6 wk, and conducted two surveys before and after the intervention span. one sec reduced the usage of target apps in two ways. First, on average 36% of the times participants attempted opening a target app, they closed that app again after one sec interfered. Second, over the course of 6 wk, users attempted to open target apps 37% less than in the first week. In sum, one sec decreased users' actual opening of target apps by 57% after six consecutive weeks. Afterward, participants also reported spending less time with their apps and indicated increased satisfaction with their consumption. To disentangle one sec's effects, we tested its three psychological features in a preregistered online experiment (N = 500) that measured the consumption of real and viral social media video clips. We found that providing the additional option to dismiss the consumption attempt had the strongest effect. While the friction by time delay also reduced consumption instances, the deliberation message was not effective.

Resolving content moderation dilemmas between free speech and harmful misinformation.

In online content moderation, two key values may come into conflict: protecting freedom of expression and preventing harm. Robust rules based in part on how citizens think about these moral dilemmas are necessary to deal with this conflict in a principled way, yet little is known about people's judgments and preferences around content moderation. We examined such moral dilemmas in a conjoint survey experiment where US respondents (N = 2, 564) indicated whether they would remove problematic social media posts on election denial, antivaccination, Holocaust denial, and climate change denial and whether they would take punitive action against the accounts. Respondents were shown key information about the user and their post as well as the consequences of the misinformation. The majority preferred quashing harmful misinformation over protecting free speech. Respondents were more reluctant to suspend accounts than to remove posts and more likely to do either if the harmful consequences of the misinformation were severe or if sharing it was a repeated offense. Features related to the account itself (the person behind the account, their partisanship, and number of followers) had little to no effect on respondents' decisions. Content moderation of harmful misinformation was a partisan issue: Across all four scenarios, Republicans were consistently less willing than Democrats or independents to remove posts or penalize the accounts that posted them. Our results can inform the design of transparent rules for content moderation of harmful misinformation.

Meet the Metaorganism: A web-based learning app for undergraduate and graduate biology students.

Meet the Metaorganism is a web-based learning app that combines three fundamental biological concepts (coevolution, community dynamics, and immune system) with latest scientific findings using the metaorganism as a central case study. In a transdisciplinary team of scientists, information designers, programmers, science communicators, and educators, we conceptualized and developed the app according to the latest didactic and scientific findings and aimed at setting new standards in visual design, digital knowledge transfer, and online education. A content management system allows continuous integration of new findings, which enables us to expand the app with the dynamics of the research field. Students can thus gain a close insight and connection to current research, and at the same time learn that knowledge is not static but grows dynamically. Especially in the realm of the easily accessible metaorganism research, visualization plays an essential role to keep complex processes understandable and memorable. Meet the Metaorganism is freely available online and can be accessed here: www.metaorganism.app.

Detecting fake-review buyers using network structure: Direct evidence from Amazon.

Online reviews significantly impact consumers' decision-making process and firms' economic outcomes and are widely seen as crucial to the success of online markets. Firms, therefore, have a strong incentive to manipulate ratings using fake reviews. This presents a problem that academic researchers have tried to solve for over two decades and on which platforms expend a large amount of resources. Nevertheless, the prevalence of fake reviews is arguably higher than ever. To combat this, we collect a dataset of reviews for thousands of Amazon products and develop a general and highly accurate method for detecting fake reviews. A unique difference between previous datasets and ours is that we directly observe which sellers buy fake reviews. Thus, while prior research has trained models using laboratory-generated reviews or proxies for fake reviews, we are able to train a model using actual fake reviews. We show that products that buy fake reviews are highly clustered in the product reviewer network. Therefore, features constructed from this network are highly predictive of which products buy fake reviews. We show that our network-based approach is also successful at detecting fake review buyers even without ground truth data, as unsupervised clustering methods can accurately identify fake review buyers by identifying clusters of products that are closely connected in the network. While text or metadata can be manipulated to evade detection, network-based features are more costly to manipulate because these features result directly from the inherent limitations of buying reviews from online review marketplaces, making our detection approach more robust to manipulation.

The surprising underperformance of East Asians in US law and business schools: The liability of low assertiveness and the ameliorative potential of online classrooms.

SignificanceTo date, researchers and practitioners have focused on the academic challenges of underrepresented ethnic groups in the United States. In comparison, Asians have received limited attention, as they are commonly assumed to excel across all educational stages. Six large studies challenge this assumption by revealing that East Asians (but not South Asians) underperform in US law schools and business schools. This is not because East Asians are less academically motivated or less proficient in English but because their low verbal assertiveness is culturally incongruent with the assertiveness prized by US law and business schools. Online classes (via Zoom) mitigated East Asians' underperformance in courses emphasizing assertiveness and class participation. Educators should reexamine pedagogical practices to create a culturally inclusive classroom.

Preventing soft skill decay among early-career women in STEM during COVID-19: Evidence from a longitudinal intervention.

As the workforce shifts to being predominantly hybrid and remote, how can companies help employees-particularly early-career women in science, technology, engineering, and mathematics (STEM) fields-develop greater confidence in their soft skills, shown to improve organizational retention? We evaluate the effects of an online longitudinal intervention to develop soft skills among early-career women employees at a North American biotechnology company during the height of the COVID-19 pandemic. Controlling for baseline levels collected immediately prior to nationwide lockdowns, we find that a 6-month online intervention increased early-career women's assessments of their soft skills at work by an average of 9% (P < 0.001), compared with a decrease of about 3.5% for a matched control group (P < 0.05), resulting in an average treatment effect of nearly 13% on the treated group. Furthermore, we find evidence that the intervention led to an increase in manager-assessed performance for early-career women relative to employees not in the intervention, and that overall, increased self-assessments of soft skill competencies were associated with greater odds of retention. Results show how employee soft skill development was affected by the pandemic and provide insights for a feasible and cost-effective method to train and engage a hybrid or fully remote workforce.

Unveiling the Dynamic Electrocatalytic Activity of Online Synthesized Bimetallic Nanocatalysts via Electrochemiluminescence Microscopy.

Effective bimetallic nanoelectrocatalysis demands precise control of composition, structure, and understanding catalytic mechanisms. To address these challenges, we employ a two-in-one approach, integrating online synthesis with real-time imaging of bimetallic Au@Metal core-shell nanoparticles (Au@M NPs) via electrochemiluminescence microscopy (ECLM). Within 120 s, online electrodeposition and in situ catalytic activity screening alternate. ECLM captures transient faradaic processes during potential switches, visualizes electrochemical processes in real-time, and tracks catalytic activity dynamics at the single-particle level. Analysis using ECL photon flux density eliminates size effects and yields quantitative electrocatalytic activity results. Notably, a nonlinear activity trend corresponding to the shell metal to Au surface atomic ratio is discerned, quantifying the optimal surface component ratio of Au@M NPs. This approach offers a comprehensive understanding of catalytic behavior during the deposition process with high spatiotemporal resolution, which is crucial for tailoring efficient bimetallic nanocatalysts for diverse applications.

Filling the gaps in peptide maps with a platform assay for top-down characterization of purified protein samples.

Liquid chromatography-mass spectrometry (LC-MS) intact mass analysis and LC-MS/MS peptide mapping are decisional assays for developing biological drugs and other commercial protein products. Certain PTM types, such as truncation and oxidation, increase the difficulty of precise proteoform characterization owing to inherent limitations in peptide and intact protein analyses. Top-down MS (TDMS) can resolve this ambiguity via fragmentation of specific proteoforms. We leveraged the strengths of flow-programmed (fp) denaturing online buffer exchange (dOBE) chromatography, including robust automation, relatively high ESI sensitivity, and long MS/MS window time, to support a TDMS platform for industrial protein characterization. We tested data-dependent (DDA) and targeted strategies using 14 different MS/MS scan types featuring combinations of collisional- and electron-based fragmentation as well as proton transfer charge reduction. This large, focused dataset was processed using a new software platform, named TDAcquireX, that improves proteoform characterization through TDMS data aggregation. A DDA-based workflow provided objective identification of αLac truncation proteoforms with a two-termini clipping search. A targeted TDMS workflow facilitated the characterization of αLac oxidation positional isomers. This strategy relied on using sliding window-based fragment ion deconvolution to generate composite proteoform spectral match (cPrSM) results amenable to fragment noise filtering, which is a fundamental enhancement relevant to TDMS applications generally.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

Lipidomics Study of Type 1 Diabetic Rats Using Online Phase Transition Trapping-Supercritical Fluid Extraction-Chromatography Coupled with Quadrupole Time-of-Flight Tandem Mass Spectrometry.

Chromatography-mass spectrometry-based lipidomics represents an essential tool for elucidating lipid dysfunction mechanisms and is extensively employed in investigating disease mechanisms and identifying biomarkers. However, the detection of low-abundance lipids in biological matrices, along with cumbersome operational procedures, complicates comprehensive lipidomic analyses, necessitating the development of highly sensitive, environmentally friendly, and automated methods. In this study, an online phase transition trapping-supercritical fluid extraction-chromatography-mass spectrometry (PTT-SFEC-MS/MS) method was developed and successfully applied to plasma lipidomics analysis in Type 1 diabetes (T1D) rats. The PTT strategy captured entire extracts at the column head by converting CO2 from a supercritical state to a gaseous state, thereby preventing peak spreading, enhancing peak shape for precise quantification, and boosting sensitivity without any sample loss. This method utilized only 5 µL of plasma and accomplished sample extraction, separation, and detection within 27 min. Ultimately, 77 differential lipids were identified, including glycerophospholipids, sphingolipids, and glycerolipids, in T1D rat plasma. The results indicated that the progression of the disease might be linked to alterations in glycerophospholipid and sphingolipid metabolism. Our findings demonstrated a green, highly efficient, and automated method for the lipidomics analysis of biological samples, providing a scientific foundation for understanding the pathogenesis and diagnosis of T1D.