Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Labor Transparency in Situated Computational Systems Impact Research

Researchers seeking to examine and prevent technology-mediated harms have emphasized the importance of directly engaging with community stakeholders through participatory approaches to computational systems research. However, recent transformations in strategies of corporate capture within the tech industry pose significant challenges to established participatory practices. In this paper we extend existing critical participatory design scholarship to highlight the exploitative potential of labor relationships in community collaborations between researchers and participants. Drawing on a reflexive approach to our own experiences conducting agonistic participatory research on emerging technologies at a large technology company, we highlight the limitations of doing participatory work within such contexts by empirically illustrating how and when these relationships threaten to appropriate and alienate participant labor. We argue that a labor-conscious approach to computational systems impact research is critical for countering the commodification of inclusion and invite fellow researchers to more actively investigate such dynamics. To this end, we provide (1) a framework for documenting divisions of labor within participatory research, design, and data practices, and (2) a series of short provocations that help locate and inventory sites of extraction within participatory engagements.
An Adversarial Attack Considering Effectiveness and Concealment on Faster R-CNN

A well-designed adversarial attack method can expose the security vulnerabilities of the deep neural network models, thus providing support examples for defense strategies such as adversarial training. This paper investigates the adversarial attack against the object detection model Faster R-CNN. First, this work takes Faster R-CNN as a target model and formulates the adversarial attack as a multi-objective optimization problem. Second, a constrain considering perturbation magnitude, class label scores and bounding boxes coordinates is introduced to guarantee effectiveness and concealment of the attack. Finally, the proposed method is verified on two benchmark datasets for object detection. The experimental results show that the generated adversarial examples can reduce the @[. 5,. 95] of Faster R-CNN from 39.9% to 0.8% and 35.0% to 0.1% on MSCOCO2017 and TT100K, respectively. In addition, the generated perturbation achieves considerable concealment, where the average of perturbation magnitude in L1 norm only reaches 13.99 and 0.71 on the two benchmark datasets.
Fairer Together: Mitigating Disparate Exposure in Kemeny Rank Aggregation

In social choice, traditional Kemeny rank aggregation combines the preferences of voters, expressed as rankings, into a single consensus ranking without consideration for how this ranking may unfairly affect marginalized groups (i.e., racial or gender). Developing fair rank aggregation methods is critical due to their societal influence in applications prioritizing job applicants, funding proposals, and scheduling medical patients. In this work, we introduce the Fair Exposure Kemeny Aggregation Problem (FairExp-kap) for combining vast and diverse voter preferences into a single ranking that is not only a suitable consensus, but ensures opportunities are not withheld from marginalized groups. In formalizing FairExp-kap, we extend the fairness of exposure notion from information retrieval to the rank aggregation context and present a complimentary metric for voter preference representation. We design algorithms for solving FairExp-kap that explicitly account for position bias, a common ranking-based concern that end-users pay more attention to higher ranked candidates. epik solves FairExp-kap exactly by incorporating non-pairwise fairness of exposure into the pairwise Kemeny optimization; while the approximate epira is a candidate swapping algorithm, that guarantees ranked candidate fairness. Utilizing comprehensive synthetic simulations and six real-world datasets, we show the efficacy of our approach illustrating that we succeed in mitigating disparate group exposure unfairness in consensus rankings, while maximally representing voter preferences.
Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking

For applications where multiple stakeholders provide recommendations, a fair consensus ranking must not only ensure that the preferences of rankers are well represented, but must also mitigate disadvantages among socio-demographic groups in the final result. However, there is little empirical guidance on the value or challenges of visualizing and integrating fairness metrics and algorithms into human-in-the-loop systems to aid decision-makers. In this work, we design a study to analyze the effectiveness of integrating such fairness metrics-based visualization and algorithms. We explore this through a task-based crowdsourced experiment comparing an interactive visualization system for constructing consensus rankings, ConsensusFuse, with a similar system that includes visual encodings of fairness metrics and fair-rank generation algorithms, FairFuse. We analyze the measure of fairness, agreement of rankers’ decisions, and user interactions in constructing the fair consensus ranking across these two systems. In our study with 200 participants, results suggest that providing these fairness-oriented support features nudges users to align their decision with the fairness metrics while minimizing the tedious process of manually having to amend the consensus ranking. We discuss the implications of these results for the design of next-generation fairness oriented-systems and along with emerging directions for future research.
Understanding Disengagement in Just-in-Time Mobile Health Interventions

Just-in-time (JIT) intervention aims to proactively detect a user's problematic behaviors and deliver interventions at an opportune moment to facilitate target behaviors. However, prior studies have shown that JIT intervention may suffer from user disengagement, a phenomenon in which a user's level of engagement with intervention apps and target behaviors declines over time. In this study, we aimed to deepen our understanding of disengagement in a mobile JIT intervention system. As a case study, we conducted a user study with college students (n = 54) for eight weeks to understand how disengagement appears over time and what factors influence user disengagement. Our findings reveal that personal traits, such as boredom proneness and self-control issues, are closely related to disengagement, with key factors including 1) boredom and habituation related to repetitive and monotonous JIT interventions, 2) inopportune alarm, 3) distrust for the JIT feedback mechanism, and 4) a lack of motivation due to low rewards. We provide theoretical and practical design guidelines for follow-up studies on JIT intervention system design.
Exploring Smart Standing Desks to Foster a Healthier Workplace

Sedentary behavior is endemic in modern workplaces, contributing to negative physical and mental health outcomes. Although adjustable standing desks are increasing in popularity, people still avoid standing. We developed an open-source plug-and-play system to remotely control standing desks and investigated three system modes with a three-week in-the-wild user study (N=15). Interval mode forces users to stand once per hour, causing frustration. Adaptive mode nudges users to stand every hour unless the user has stood already. Smart mode, which raises the desk during breaks, was the best rated, contributing to increased standing time with the most positive qualitative feedback. However, non-computer activities need to be accounted for in the future. Therefore, our results indicate that a smart standing desk that shifts modes at opportune times has the most potential to reduce sedentary behavior in the workplace. We contribute our open-source system and insights for future intelligent workplace well-being systems.
A Systematic Review of Ethics Disclosures in Predictive Mental Health Research

Applied machine learning (ML) has not yet coalesced on standard practices for research ethics. For ML that predicts mental illness using social media data, ambiguous ethical standards can impact peoples’ lives because of the area’s sensitivity and material consequences on health. Transparency of current ethics practices in research is important to document decision-making and improve research practice. We present a systematic literature review of 129 studies that predict mental illness using social media data and ML, and the ethics disclosures they make in research publications. Rates of disclosure are going up over time, but this trend is slow moving – it will take another eight years for the average paper to have coverage on 75% of studied ethics categories. Certain practices are more readily adopted, or "stickier", over time, though we found prioritization of data-driven disclosures rather than human-centered. These inconsistently reported ethical considerations indicate a gap between what ML ethicists believe ought to be and what actually is done. We advocate for closing this gap through increased transparency of practice and formal mechanisms to support disclosure.
A Deep Reinforcement Learning Technique for PNPSC Net Player Strategies

Petri Nets with Players, Strategies, and Costs (PNPSC) is an extension of Petri nets specifically designed to model cyberattacks. The PNPSC formalism includes a representation of the strategies for the competing "players," i.e., the attacker and defender. Developing well-performing strategies for players in PNPSC nets is challenging for both game tree and reinforcement learning algorithms. This paper presents a method of modeling the PNPSC net player strategies as a game tree and using a combination of Monte Carlo Tree Search (MCTS) and deep reinforcement learning to effectively improve the players' strategies. The performance of this combination method is compared with standard action selection with the deep Q-learning algorithm.
An R package for ensemble learning stacking

Summary We developed an R package for stacking, which is an ensemble approach to supervised learning. Using this package, training and prediction of stacking can be conducted using one-row scripts. Availability and implementation The R package stacking is available at the GitHub (https://github.com/Onogi/stacking). Contact onogiakio@gmail.com Supplementary information This manuscript has no supplementary information.
Modeling the 3D structure and conformational dynamics of very large RNAs using coarse-grained molecular simulations

We describe a computational approach to building and simulating realistic 3D models of very large RNA molecules (>1000 nucleotides) at a resolution of one “bead” per nucleotide. The method starts with a predicted secondary structure and uses several stages of energy minimization and Brownian dynamics (BD) simulation to build 3D models. A key step in the protocol is the temporary addition of a 4th spatial dimension that allows all predicted helical elements to become disentangled from each other in an effectively automated way. We then use the resulting 3D models as input to Brownian dynamics simulations that include hydrodynamic interactions (HIs) that allow the diffusive properties of the RNA to be modelled as well as enabling its conformational dynamics to be simulated. To validate the dynamics part of the method, we first show that when applied to small RNAs with known 3D structures the BD-HI simulation models accurately reproduce their experimental hydrodynamic radii (Rh). We then apply the modelling and simulation protocol to a variety of RNAs for which experimental Rh values have been reported ranging in size from 85 to 3569 nucleotides. We show that the 3D models, when used in BD-HI simulations, produce hydrodynamic radii that are usually in good agreement with experimental estimates for RNAs that do not contain tertiary contacts that persist even under very low salt conditions. Finally, we show that sampling of the conformational dynamics of large RNAs on timescales of 100 µs is computationally feasible with BD-HI simulations.
Iron Oxide Nanoparticles Coated with Biodegradable Block-Copolymer PDMAEMA-b-PMPC and Functionalized with Aptamer for HER2 Breast Cancer Cell Identification

Hybrid nanoparticles have shown promise in biomedical applications; however, their seamless integration into clinical settings remains challenging. Here, we introduce a novel metal oxide polymer hybrid nanoparticle (NP) with a high affinity for nucleic acids. Iron oxide nanoparticles (IONP) were initially synthesized via the co-precipitation method and subjected to comprehensive characterization. Subsequently, block copolymers were synthesized using the Reversible Addition−Fragmentation Chain Transfer (RAFT) technique, employing the zwitterionic PMPC (Poly (2 Methacryloyloxyethyl Phosphorylcholine)) and the cationic PDMAEMA (Poly(2 (Dimethylamino) Ethyl Methacrylate)) with varying degrees of polymerization. In vitro cytotoxicity studies demonstrated the biocompatibility of the synthesized nanoparticles, with no observed toxicity up to a concentration of 150 µg/mL. The cationic polymer PDMAEMA facilitated the facile coating of IONP, forming the IONPP complex, consisting of a 13.27 metal core and a 3.1 nm block-copolymer coating. Subsequently, the IONPP complex was functionalized with a DNA aptamer specifically targeting the human epidermal growth factor receptor 2 (HER2) in breast cancer, forming IONPPP. The block-copolymer exhibited an EC50 of 7.07 µg/mL and demonstrated enhanced recognition efficiency in HER2-amplified SKBR3 cells. Our study presents a comprehensive IONPPP characterization capable of binding short DNA sequences and targeting proteins such as HER2. This newly developed nanoparticle holds significant potential for cancer cell identification and isolation, offering promising prospects in cancer research and clinical applications. 1. Statement of significance Despite recent advancements in biomedical research, developing sensitive and specific tools for recognizing biological motifs, such as cell receptors and proteins in complex biological solutions, remains a challenge. Furthermore, current approaches often rely on complex biological derivatives like antibodies, lacking a cost-effective delivery strategy. Our study proposes creating and characterizing a novel hybrid metal oxide polymer nanoparticle named IONPPP, functionalized with a DNA aptamer designed to recognize HER2-positive cells. HER2 is a clinically actionable marker for gastric, gastroesophageal, and, particularly, breast cancers. This unique combination of a metal core with an external polymeric structure offers the potential for identification, isolation, and even theragnostic applications, benefiting from its low toxicity and high specificity. 2. Graphical Abstract
A dual-fluorescent recombinant for live observation of Herpes simplex-type 1 infection outcomes

Critical stages of lytic Herpes simplex type 1 (HSV-1) replication are marked by the sequential expression of immediate early (IE) to early (E), then late (L) viral genes. HSV-1 also persists in neuronal tissues via a non-replicative, transcriptionally repressed infection called latency. Understanding the regulation of lytic and latent transcriptional profiles provides focused insight into HSV-1 infection and disease. We sought a fluorescence-based approach to observe temporal progression of HSV-1 infection at the single-cell level. We constructed and characterized a novel HSV-1 recombinant that reports IE and L gene expression by fluorescent protein detection. The dual-reporter HSV-1 visualizes IE gene expression by a CMV promotor-driven YFP, and L gene expression by an endogenous mCherry-VP26 fusion. We confirmed that viral gene expression, replication and spread of infection in epithelial cells is not altered by the incorporation of the fluorescent reporters. Interference with viral DNA polymerase activity abolishes VP26-mCherry detection late in HSV-1 infection, visually reporting the failure to complete viral replication. Viral replication in primary neurons is not altered, but retrograde-directed inoculation of the dual-reporter HSV-1 exhibits a modest reduction in titer, compared to unlabeled HSV-1. Low-dose axonal inoculation in the presence of small molecule modulation of neuronal signaling results in divergent outcomes of YFP and mCherry detection, suggesting different states of latent and lytic replication. Rigorous characterization of this dual-reporter HSV-1 recombinant has demonstrated the utility of temporal observation of HSV-1 replication in live cells and the potential for further insight into the dynamics of infection. Importance Herpes simplex virus-type 1 (HSV-1) is a prevalent human pathogen that infects approximately 67% of the global population. HSV-1 invades the peripheral nervous system, where latent HSV-1 infection persists within the host for life. Immunological evasion, viral persistence, and herpetic pathologies are determined by regulation of HSV-1 gene expression. Studying HSV-1 gene expression during neuronal infection is challenging but essential for the development of antiviral therapeutics and interventions. We constructed and characterized a dual-fluorescent HSV-1 recombinant that enables visualization of IE and L gene expression. The recombinant HSV-1 is used to observe the progression and outcome of infection. We demonstrate that drug treatments targeting cellular pathways can manipulate latent HSV-1 infection in neurons to achieve divergent outcomes of infection.
Brewing COFFEE: A sequence-specific coarse-grained energy function for simulations of DNA-protein complexes

DNA-protein interactions are pervasive in a number of biophysical processes ranging from transcription, gene expression, to chromosome folding. To describe the structural and dynamic properties underlying these processes accurately, it is important to create transferable computational models. Toward this end, we introduce Coarse grained force field for energy estimation, COFFEE, a robust framework for simulating DNA-protein complexes. To brew COFFEE, we integrated the energy function in the Self-Organized Polymer model with Side Chains for proteins and the Three Interaction Site model for DNA in a modular fashion, without re-calibrating any of the parameters in the original force-fields. A unique feature of COFFEE is that it describes sequence-specific DNA-protein interactions using a statistical potential (SP) derived from a dataset of high-resolution crystal structures. The only parameter in COFFEE is the strength (λDNAPRO) of the DNA-protein contact potential. For an optimal choice of λDNAPRO, the crystallographic B-factors for DNA-protein complexes, with varying sizes and topologies, are quantitatively reproduced. Without any further readjustments to the force-field parameters, COFFEE predicts the scattering profiles that are in quantitative agreement with SAXS experiments as well as chemical shifts that are consistent with NMR. We also show that COFFEE accurately describes the salt-induced unraveling of nucleosomes. Strikingly, our nucleosome simulations explain the destabilization effect of ARG to LYS mutations, which does not alter the balance of electrostatic interactions, but affects chemical interactions in subtle ways. The range of applications attests to the transferability of COFFEE, and we anticipate that it would be a promising framework for simulating DNA-protein complexes at the molecular length-scale. Graphical TOC Entry
Evaluating the capabilities of the Astral mass analyzer for single-cell proteomics

The complexity of human physiology arises from well-orchestrated interactions between trillions of single cells in the body. While single-cell RNA sequencing (scRNA-seq) has enhanced our understanding of cell diversity, gene expression alone does not fully characterize cell phenotypes. Additional molecular dimensions, such as proteins, are needed to define cellular states accurately. Mass spectrometry (MS)-based proteomics has emerged as a powerful tool for comprehensive protein analysis, including single-cell applications. However, challenges remain in terms of throughput and proteomic depth, in order to maximize the biological impact of single-cell proteomics by Mass Spectrometry (scp-MS) workflows. This study leverages a novel high-resolution, accurate mass (HRAM) instrument platform, consisting of both an Orbitrap and an innovative HRAM Asymmetric Track Lossless (Astral) analyzer. The Astral analyzer offers high sensitivity and resolution through lossless ion transfer and a unique flight track design. We evaluate the performance of the Thermo Scientific Orbitrap Astral MS using Data-Independent Acquisition (DIA) and assess proteome depth and quantitative precision for ultra-low input samples. Optimal DIA method parameters for single-cell proteomics are identified, and we demonstrate the ability of the instrument to study cell cycle dynamics in Human Embryonic Kidney (HEK293) cells, and cancer cell heterogeneity in a primary Acute Myeloid Leukemia (AML) culture model.
Structural insight into CD93 recognition by IGFBP7

The CD93/IGFBP7 axis are key factors expressed in endothelial cells (EC) that mediate EC angiogenesis and migration. Upregulation of them contributes to tumor vascular abnormality and blockade of this interaction promotes a favorable tumor microenvironment for therapeutic interventions. However, how these two proteins associated to each other remains unclear. In this study, we solved the human CD93–IGFBP7 complex structure to elucidate the interaction between the EGF1 domain of CD93 and the IB domain of IGFBP7. Mutagenesis studies confirmed the binding interactions and specificities. Cellular and mouse tumor studies demonstrated the physiological relevance of the CD93–IGFBP7 interaction in EC angiogenesis. Our study provides hints for development of therapeutic agents to precisely disrupt unwanted CD93–IGFBP7 signaling in the tumor microenvironment. Additionally, analysis of the CD93 full-length architecture provides insights into how CD93 protrudes on the cell surface and forms a flexible platform for binding to IGFBP7 and other ligands.