Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel ALDH1A1 Inhibitor Blocks Platinum-Induced Senescence and Stemness in Ovarian Cancer

Ovarian cancer is a deadly disease attributed to late-stage detection as well as recurrence and development of chemoresistance. Ovarian cancer stem cells (OCSCs) are hypothesized to be largely responsible for emergence of chemoresistant tumors. Although chemotherapy may initially succeed at decreasing the size and number of tumors, it leaves behind residual malignant OCSCs. In this study, we demonstrate that Aldehyde dehydrogenase 1A1 (ALDH1A1) is essential for the survival of OCSCs. We identified a first in class ALDH1A1 inhibitor, compound 974, and used 974 as a tool to decipher the mechanism of stemness regulation by ALDH1A1. Treatment of OCSCs with 974 significantly inhibited ALDH activity, expression of stemness genes, spheroid, and colony formation. In vivo limiting dilution assay demonstrated that 974 significantly inhibited CSC frequency. Transcriptomic sequencing of cells treated with 974 revealed significant downregulation of genes related to stemness and chemoresistance as well as senescence and senescence associated secretory phenotype (SASP). We confirmed that 974 inhibited senescence and stemness induced by platinum-based chemotherapy in functional assays. Overall, these data establish that ALDH1A1 is essential for OCSCs survival and ALDH1A1 inhibition suppresses chemotherapy induced senescence and stemness. Targeting ALDH1A1 using small molecule inhibitors in combination with chemotherapy therefore presents a promising strategy to prevent ovarian cancer recurrence and has potential for clinical translation.
Multi-color live-cell STED nanoscopy of mitochondria with a gentle inner membrane stain

Significance Mitochondrial nanoscopic imaging has developed from proof-of-principle demonstrations to a viable approach for structural and functional research. The next technological leap requires a palette of orthogonal dyes to label various mitochondrial components and unveil their interactive dynamics in 3D space and extended time, at sub-100 nm resolution. We report a mitochondrial inner membrane fluorescent marker, PK Mito Orange (PKMO), featuring markedly reduced phototoxicity for time-lapse imaging, and compatibility with not only commercial stimulated emission depletion (STED) nanoscopes, but also green and far-red fluorophores such as the genetically encoded calcium indicator (GCaMP) and (silicon rhodamine) SiR. Gentle PKMO labeling enables 3D reconstruction of crista structures in live mitochondria, analyzing crista morphology in live primary cells and genetically edited cell lines, and multiplexed recording of mitochondrial dynamics and interactions.
CHALLENGES CONFRONTING CONSTRUCTION PROJECT MANAGEMENTSYSTEM FOR SUSTAINABLE CONSTRUCTION IN DEVELOPING COUNTRIES:PROFESSIONALS PERSPECTIVES (A CASE STUDY OF NIGERIA)

The success of the Nigerian construction industry can be aligned with the use of construction project management systems, although, the industry has been maligned by issues such as building collapse, incessant delays, abandonment and cost overrun. It is therefore imperative to examine the challenges confronting construction project management system in Nigeria. In this study the descriptive survey method was adopted and data were obtained by means of inquiries using questionnaires. A sample size of fifty nine (59) construction professionals was used for the study. The study reveals that location of a project majorly influences Project Manager’s decision making on project planning. It asserted the crucial importance of Management skills required in practicing construction project management. In conclusion, the result identified that passive participation from Project Manager, lack of client involvement in making decisions, provision of substandard materials, design error, lack of effective communication and poor treatment of workforce are challenges hampering the use of construction project management. The study recommends the institutionalization of construction project management practice, compulsion of adequate training and skill modification programs for construction professionals to aid the sustainability of construction project management systems in Nigeria.
Cascades and Overexposure in Social Networks: The Budgeted Case

Influence maximization (IM) has now been a widely studied topic, but only in recent years have studies considered overexposure. Overexposure is usually measured as the negative cost associated with reaching unintended recipients during an information cascade. A polynomial-time algorithm is known for cascades with overex-posure when we can seed as many nodes as we want. This paper focuses on overexposure for the budgeted case of seeding, which has received little to no attention. We show that the problem is NP-hard even for restricted cases. For various special cases, we devise provable approximation algorithms, dynamic programming solutions, linear programming solutions, and heuristics. For the general case, we provide a linear programming solution and several fast and effective heuristics, mostly of the greedy flavor. We perform an extensive experimental study using synthetic and real-world networks. We investigate how network properties and model parameters impact our algorithms. It brings out interesting findings like why a low-quality product needs a smarter algorithm, and why certain algorithms do well on some networks but not others.
Towards Anomaly Detection in Reinforcement Learning

Identifying datapoints that substantially differ from normality is the task of anomaly detection (AD). While AD has gained wide-spread attention in rich data domains such as images, videos, audio and text, it has has been studied less frequently in the context of reinforcement learning (RL). This is due to the additional layer of complexity that RL introduces through sequential decision making. Developing suitable anomaly detectors for RL is of particular importance in safety-critical scenarios where acting on anomalous data could result in hazardous situations. In this work, we address the question of what AD means in the context of RL. We found that current research trains and evaluates on overly simplistic and unrealistic scenarios which reduce to classic pattern recognition tasks. We link AD in RL to various fields in RL such as lifelong RL and generalization. We discuss their similarities, differences, and how the fields can benefit from each other. Moreover, we identify non-stationarity to be one of the key drivers for future research on AD in RL and make a first step towards a more formal treatment of the problem by framing it in terms of the recently introduced block contextual Markov decision process. Finally, we define a list of practical desiderata for future problems.
Relationship between Surface and Indwelling EMG Spike Shape Measures

Indwelling electromyography (EMG) has great diagnostic value but its invasive and often painful characteristics make it inappropriate for monitoring human movement. Spike shape analysis of the surface electromyographic signal responds to the call for noninvasive EMG measures for monitoring human movement and detecting neuromuscular disorders. The present study analyzed the relationship between surface and indwelling EMG interference patterns. Twenty four males and twenty four females performed three isometric dorsiflexion contractions at five force levels from 20% to maximal force. The amplitude measures increased differently between electrode types, attributed to the electrode sensitivity. The frequency measures were different between traditional and spike shape measures due to different noise rejection criteria. These measures were also different between surface and indwelling EMG due to the low-pass tissue filtering effect. The spike shape measures, thought to collectively function as a means to differentiate between motor unit characteristics, changed independent of one another.
Factorial Agent Markov Model: Modeling Other Agents' Behavior in presence of Dynamic Latent Decision Factors

Autonomous agents operating in the real world often need to interact with other agents to accomplish their tasks. For such agents, the ability to model behavior of other agents – both human and artificial – without complete knowledge of their decision factors is essential. Towards realizing this ability, we present Factorial Agent Markov Model (FAMM), a model to represent behavior of other agents performing sequential tasks. In contrast with most existing models, FAMM allows for behavior of other agents to depend on multiple , time-varying latent decision factors and does not assume rationality. To enable learning of FAMM parameters by observing behavior of other agents, we provide a set of variational inference algorithms for the unsupervised, semi-supervised, and supervised settings. These Bayesian learning algorithms for the FAMM enable agents to model other agents using execution traces and domain-specific priors. We demonstrate the utility of FAMM and corresponding learning algorithms using three synthetic domains and benchmark them against existing algorithms for modeling agent behavior. Our numerical experiments demonstrate that, despite the presence of multiple and time-varying latent states, our approach is capable of learning predictive models of other agents with semi-supervision.
Task Generalisation in Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning agents are typically trained in a single environment. As a consequence, they overfit to the training environment which results in sensitivity to perturbations and inability to generalise to similar environments. For multi-agent reinforcement learning approaches to be applicable in real-world scenarios, generalisation and robustness need to be addressed. However, unlike in supervised learning, generalisation lacks a clear definition in multi-agent reinforcement learning. We discuss the problem of task generalisation and demonstrate the difficulty of zero-shot generalisation and finetuning at the example of multi-robot warehouse coordination with preliminary results. Lastly, we discuss promising directions of research working towards generalisation of multi-agent reinforcement learning.
Be Considerate: Avoiding Negative Side Effects in Reinforcement Learning

In sequential decision making – whether it’s realized with or without the benefit of a model – objectives are often underspecified or incomplete. This gives discretion to the acting agent to realize the stated objective in ways that may result in undesirable outcomes, including inadvertently creating an unsafe environment or indirectly impacting the agency of humans or other agents that typically operate in the environment. In this paper, we explore how to build a reinforcement learning (RL) agent that contemplates the impact of its actions on the wellbeing and agency of others in the environment, most notably humans. We endow RL agents with the ability to contemplate such impact by augmenting their reward based on expectation of future return by others in the environment, providing different criteria for characterizing impact. We further endow these agents with the ability to differentially factor this impact into their decision making, manifesting behaviour that ranges from self-centred to self-less, as demonstrated by experiments in gridworld environments.
Learning Efficient Diverse Communication for Cooperative Heterogeneous Teaming

High-performing teams learn intelligent and efficient communication and coordination strategies to maximize their joint utility. These teams implicitly understand the different roles of heterogeneous team members and adapt their communication protocols accordingly. Multi-Agent Reinforcement Learning (MARL) seeks to develop computational methods for synthesizing such coordination strategies, but formulating models for heterogeneous teams with different state, action, and observation spaces has remained an open problem. Without properly modeling agent heterogeneity, as in prior MARL work that leverages homogeneous graph networks, communication becomes less helpful and can even deteriorate the cooperativity and team performance. We propose Heterogeneous Policy Networks (HetNet) to learn efficient and diverse communication models for coordinating cooperative heterogeneous teams. Building on heterogeneous graph-attention networks, we show that HetNet not only facilitates learning heterogeneous collaborative policies per existing agent-class but also enables end-to-end training for learning highly efficient binarized messaging. Our empirical evaluation shows that HetNet sets a new state of the art in learning coordination and communication strategies for heterogeneous multi-agent teams by achieving an 8.1% to 434.7% performance improvement over the next-best baseline across multiple domains while simultaneously achieving a 200 × reduction in the required communication bandwidth.
Financial Inclusion-An Overview

Bank nationalization in India marked a paradigm shift in the focus of banking as it was intended to shift the focus from class banking to mass banking. The banking industry has shown tremendous growth in volume and complexity during the last few decades. Despite making significant improvements in all the areas relating to financial viability, profitability and competitiveness, there are concerns that banks have not been able to include vast segment of the population, especially the underprivileged sections of the society, into the fold of basic banking services. The period 1969 to 1991 saw a huge increase in the branch outreach in India as reflected in the average population covered by a bank branch fell from 64, 000 to 13, 711 numbers. In 1991 along with reforms for liberalizing and opening up of the economy, financial sector reform aimed at deregulation, increased competition and strengthening of the banking sector through recapitalization and adoption of prudential measures.
Cooperation and Learning Dynamics under Risk Diversity and Financial Incentives

In this paper, we investigate the role of risk diversity in groups of agents learning to play collective risk dilemmas (CRDs). We show that risk diversity poses new challenges to cooperation that are not observed in homogeneous groups. While increasing average risk contributes, in general, for agents to cooperate with higher probability, increasing risk diversity significantly reduces a population’s ability to achieve a collective target. Risk diversity leads to asymmetrical changes in agents policies — i.e. the increase in contributions from individuals at high risk is unable to compensate for the decrease in contributions from individuals at low risk — which reduces the total contributions in a population and overall social welfare. At the same time, risk diversity offers novel opportunities to design financial incentives, which, as we show, can improve cooperation, target achievement and global welfare beyond the levels obtained in the absence of diversity. Our results highlight the need to align risk perceptions among agents and implement diversity-based incentive policies in order to improve collectives’ abilities to avoid future catastrophic events.
Exploiting Causal Structure for Transportability in Online, Multi-Agent Environments

Autonomous agents may encounter the transportability problem when they suffer performance deficits from training in an environment that differs in key respects from that in which they are deployed. Although a causal treatment of transportability has been studied in the data sciences, the present work expands its utility into online, multi-agent, reinforcement learning systems in which agents are capable of both experimenting within their own environments and observing the choices of agents in separate, potentially different ones. In order to accelerate learning, agents in these Multi-agent Transport (MAT) problems face the unique challenge of determining which agents are acting in similar environments, and if so, how to incorporate these observations into their policy. We propose and compare several agent policies that exploit local similarities between environments using causal selection diagrams, demonstrating that optimal policies are learned more quickly than in baseline agents that do not. Simulation results support the efficacy of these new agents in a novel variant of the Multi-Armed Bandit problem with MAT environments.
Max-sum with Quadtrees for Continuous DCOPs with Application to Lane-Free Autonomous Driving

In this paper we put forward a novel extension of the classic Max-Sum algorithm to the framework of Continuous Distributed Constrained Optimization Problems (Continuous DCOPs), in which we model the exchanged messages by means of a popular geometric al-gorithm, Quadtrees. As such, the discretization process is dynamic and embedded in the internal Max-Sum operations (addition and marginal maximization). We apply our Max-Sum with Quadtrees approach to Lane-Free Autonomous Driving in a highway populated with vehicles. Our experimental evaluation verifies the efficiency of our approach in this challenging dynamic coordination domain, demonstrating its superior performance with respect to the standard Max-Sum algorithm.
Spatial and Temporal Trends in HIV/AIDS Burden Among Worldwide Regions From 1990 to 2019: A Secondary Analysis of the Global Burden of Disease Study 2019

Purpose HIV/AIDS is a critical public health concern worldwide. This article investigated the spatial and temporal trends in HIV/AIDS burden from 1990 to 2019. Methods Data were extracted from the Global Burden of Disease (GBD) Study 2019. The estimated annual percentage change (EAPC) and the age-standardized rate (ASR) were used to quantify the change in trends at the global, regional, and national levels. Results In terms of temporal trends, during the period 1990–2004, increasing trends in prevalence (EAPC = 7.47, 95% confidence interval [CI] 5.84, 9.12), death (EAPC = 10.85, 95% CI 8.90–12.84), and disability-adjusted life years (DALYs) (EAPC = 10.40, 95% CI 8.47–12.36) of HIV/AIDS were observed. During the period 2005–2019, the global trends in HIV/AIDS incidence, death, and DALYs of HIV/AIDS decreased, with the EAPCs of −2.68 (95% CI−2.82–−2.53), −6.73 (95% CI −6.98–−6.47), and −6.75 (95% CI −6.95–−6.54), respectively. However, the disease prevalence showed a slight increasing trend (EAPC = 0.71, 95% CI 0.54–0.87). In terms of spatial trends, over the past 15 years, trends in HIV/AIDS incidence of HIV/AIDS appeared upward in High-middle and High sociodemographic index (SDI) areas (EAPC = 6.51, 95% CI 5.50–7.53; EAPC = 2.31, 95% CI 2.02–2.60, respectively). Conclusion Decreasing trends in HIV/AIDS incidence, death, and DALYs have been observed worldwide over the past 15 years, especially in death and DALYs rates. However, the global population living with HIV/AIDS is still increasing. It is worth noting that an unfavorable trend emerged in High-middle and High SDI areas. Prevention and control of HIV/AIDS still need to be strengthened to counteract these concerning trends.
COVID-19 Impacts Across Multiple Life Domains of Vulnerable Socio-Demographic Groups Including Migrants: A Descriptive Cross-Sectional Study

Objectives: We assessed the impacts of COVID-19 on multiple life domains across socio-demographic groups in Netherlands. Methods: After the first COVID-19 wave, we distributed online questionnaires among 13,031 participants of the multi-ethnic HELIUS cohort. Questionnaires contained questions on changes in income status, healthy behaviors, mental health, and access to non-COVID-19 health care. We then calculated differences in adjusted proportions of participants that reported negative changes across multiple life domains by migration background, age, sex, education, and occupation. Results: 4,450 individuals (35%) responded, of which 4,294 were included. Older populations and men seemed to be less vulnerable to negative changes in multiple life domains during the COVID-19 pandemic as compared to the pre-pandemic period, while populations with a migration background and lower education/occupation groups seemed to be more vulnerable to negative changes. Conclusion: Not all populations vulnerable to SARS-CoV-2 infection and mortality are also more vulnerable to COVID-19 impacts across multiple other life domains. Targeted interventions are needed in socio-demographic groups that are most impacted by COVID-19 in various life domains to prevent a further increase of their already increased risk of chronic diseases after the pandemic.
Beltanelliformis konovalovi sp. nov. From the Terminal Neoproterozoic of Central Urals: Taphonomic and Ecological Implications

The siliciclastic succession of the Ediacaran Chernyi Kamen Formation in the Central Urals of Russia contains a rich complex of Ediacaran fossils that represent in situ benthic populations buried in life position. The present work reports Beltanelliformis konovalovi sp. nov. found as a paleocommunity of organisms and studied for their morphology and spatial distribution. Unlike the type species (B. brunsae), new fossils are preserved within fine-grained sandstone beds as three-dimensional pancake-like or flattened discoidal single, paired, or occasionally aggregated bodies with an ultra-thin rim. Statistical analysis shows significant predominance of small individuals in the paleocommunity, which in turn manifests different spatial distribution in comparison with larger ones. In addition, the studied paleocommunity exhibits patterns of self-organization in extremely shallow marine environment and adaption to periodic desiccation. These results provide quantitative support for the more complex ecology of Beltanelliformis and also expand the range of taphonomic possibilities of macroscopic life in the terminal Neoproterozoic.
The Effect of Aging on Retinal Function and Retinal Ganglion Cell Morphology Following Intraocular Pressure Elevation

Aging and elevated intraocular pressure (IOP) are two major risk factors for glaucomatous optic neuropathy; a condition characterized by the selective, progressive injury, and subsequent loss of retinal ganglion cells (RGCs). We examined how age modified the capacity for RGCs to functionally recover following a reproducible IOP elevation (50 mmHg for 30 min). We found that RGC functional recovery (measured using electroretinography) was complete by 7 days in 3-month-old mice but was delayed in 12-month-old mice until 14 days. At the 7-day recovery endpoint when RGC function had recovered in young but not older eyes, we examined RGC structural responses to IOP-related stress by analyzing RGC dendritic morphology. ON-RGC cell volume was attenuated following IOP elevation in both young and older mice. We also found that following IOP elevation OFF-RGC dendritic morphology became less complex per cell volume in young mice, an effect that was not observed in older eyes. Our data suggest that adaptations in OFF-RGCs in young eyes were associated with better functional recovery 7 days after IOP elevation. Loss of RGC cellular adaptations may account for delayed functional recovery in older eyes. Graphical Abstract Retinal ganglion cell (RGC) morphological changes 7 days after intraocular pressure (IOP) elevation to 50 mmHg for 30 min.