Removal of perfluoroalkyl acids (PFAAs) from aqueous solution by water hyacinth (Eichhornia crassipes): Uptake, accumulation, and translocation.

Although Eichhornia crassipes, commonly known as water hyacinth, has been widely used in wastewater treatment, further investigations are still needed to explore the removal efficiency of perfluoroalkyl acids (PFAAs) from the aqueous environment using this floating aquatic plant. In this study, a hydroponic experiment was conducted to assess accumulation, bioconcentration factors (BCFs), translocation factors (TFs), and removal rates of eight PFAAs by water hyacinth. The obtained results indicated that all PFAAs, including five perfluoroalkyl carboxylic acids (PFCAs) with chain lengths C4-C8 and three perfluoroalkyl sulfonic acids (PFSAs) with C4, C6, and C8, were readily accumulated in water hyacinth. Throughout the duration of the experiment, there was a noticeable increase in PFAA concentrations and BCF values for different plant parts. For the root, PFAAs with more carbon numbers showed a higher uptake than the shorter homologues, with PFSAs being more readily accumulated compared to PFCAs with the same carbon number in the molecules. In contrast, the levels of long-chain PFAAs were comparatively lower than those of short-chain substances in the stem and leaf. Notably, PFAAs with less carbon numbers, like PFPeA, PFBA, and PFBS, showed a remarkable translocation from the root to the stem and leaf with TFs >1. For the whole plant, no significant correlation was found between BCFs and organic carbon-water partition coefficients (Koc), octanol-water partition coefficients (Kow), membrane-water distribution coefficients (Dmw), or protein-water distribution coefficients (Dpw). The removal rates of PFAAs ranged from 40.3 to 63.5 % throughout the three weeks of the experiment while the removal efficiencies varied from 48.9 % for PFHxS to 82.6 % for PFPeA in the last week.

Minimap: An interactive dynamic decision making game for search and rescue missions.

Many aspects of humans' dynamic decision-making (DDM) behaviors have been studied with computer-simulated games called microworlds. However, most microworlds only emphasize specific elements of DDM and are inflexible in generating a variety of environments and experimental designs. Moreover, despite the ubiquity of gridworld games for Artificial Intelligence (AI) research, only some tools exist to aid in the development of browser-based gridworld environments for studying the dynamics of human decision-making behavior. To address these issues, we introduce Minimap, a dynamic interactive game to examine DDM in search and rescue missions, which incorporates all the essential characteristics of DDM and offers a wide range of flexibility regarding experimental setups and the creation of experimental scenarios. Minimap specifically allows customization of dynamics, complexity, opaqueness, and dynamic complexity when designing a DDM task. Minimap also enables researchers to visualize and replay recorded human trajectories for the analysis of human behavior. To demonstrate the utility of Minimap, we present a behavioral experiment that examines the impact of different degrees of structural complexity coupled with the opaqueness of the environment on human decision-making performance under time constraints. We discuss the potential applications of Minimap in improving productivity and transparent replications of human behavior and human-AI teaming research. We made Minimap an open-source tool, freely available at  https://github.com/DDM-Lab/MinimapInteractiveDDMGame .

Fostering Collective Intelligence in Human-AI Collaboration: Laying the Groundwork for COHUMAIN.

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human-machine interactions, is exhibiting collective intelligence? Research on human-machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain-Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human-AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance-based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human-machine intelligence.

Postural sway is not affected by estrogen fluctuations during the menstrual cycle.

When people stand still, they exhibit a phenomenon called postural sway, or spontaneous movement of the body's center of pressure, which is related to balance control. In general females show less sway than males, but this difference only begins to appear around puberty, pointing to different levels of sex hormones as one potential mechanism for sway sex differences. In this study, we followed cohorts of young females using oral contraceptives (n = 32) and not using oral contraceptives (n = 19), to investigate associations between estrogen availability and postural sway. All participants visited the lab four times over the putative 28-day menstrual cycle. At each visit, we performed blood draws to measure plasma estrogen (estradiol) levels, and tests of postural sway using a force plate. During late follicular and mid-luteal phase, estradiol levels were lower in participants using oral contraceptives (mean differences [95% CI], respectively: -231.33; [-800.44, 337.87]; -613.26; [-1333.60, 107.07] pmol/L; main effect p < 0.001), reflecting expected consequences of oral contraceptive use. Despite these differences, postural sway was not significantly different between participants who were using oral contraceptives and participants who were not (mean difference: 2.09 cm; 95% CI = [-1.05, 5.22]; p = 0.132). Overall, we found no significant effects of the estimated menstrual cycle phase-or absolute levels of estradiol-on postural sway.

SpeedyIBL: A comprehensive, precise, and fast implementation of instance-based learning theory.

Instance-based learning theory (IBLT) is a comprehensive account of how humans make decisions from experience during dynamic tasks. Since it was first proposed almost two decades ago, multiple computational models have been constructed based on IBLT (i.e., IBL models). These models have been demonstrated to be very successful in explaining and predicting human decisions in multiple decision-making contexts. However, as IBLT has evolved, the initial description of the theory has become less precise, and it is unclear how its demonstration can be expanded to more complex, dynamic, and multi-agent environments. This paper presents an updated version of the current theoretical components of IBLT in a comprehensive and precise form. It also provides an advanced implementation of the full set of theoretical mechanisms, SpeedyIBL, to unlock the capabilities of IBLT to handle a diverse taxonomy of individual and multi-agent decision-making problems. SpeedyIBL addresses a practical computational issue in past implementations of IBL models, the curse of exponential growth, that emerges from memory-based tabular computations. When more observations accumulate over time, there is an exponential growth of the memory of instances that leads directly to an exponential slowdown of the computational time. Thus, SpeedyIBL leverages parallel computation with vectorization to speed up the execution time of IBL models. We evaluate the robustness of SpeedyIBL over an existing implementation of IBLT in decision games of increased complexity. The results not only demonstrate the applicability of IBLT through a wide range of decision-making tasks, but also highlight the improvement of SpeedyIBL over its prior implementation as the complexity of decision features the of agents increase. The library is open sourced for the use of the broad research community.

Theory of Mind From Observation in Cognitive Models and Humans.

A major challenge for research in artificial intelligence is to develop systems that can infer the goals, beliefs, and intentions of others (i.e., systems that have theory of mind, ToM). In this research, we propose a cognitive ToM framework that uses a well-known theory of decisions from experience to construct a computational representation of ToM. Instance-based learning theory (IBLT) is used to construct a cognitive model that generates ToM from the observation of other agents' behavior. The IBL model of the observer distinguishes itself from previous models of ToM that make unreasonable assumptions about human cognition, are hand-crafted for particular settings, complex, or unable to explain a cognitive development of ToM compared to human's ToM. The IBL model learns from the observation of goal-directed agents' behavior in a gridworld navigation task, and it infers and predicts the behaviors of the agents in new gridworlds across different degrees of decision complexity in similar ways to the way human observers do. We provide evidence for the alignment of the IBL observer's predictions under various levels of decision complexity. We also advance the demonstration of the IBL predictions using a classic test of false beliefs (the Sally-Anne test), which is commonly used to test ToM in humans. We discuss our results and the potential of the IBL observer model to improve human-machine interactions.

Clinical Epidemiological Characteristics and Risk Factors for Severe Bronchiolitis Caused by Respiratory Syncytial Virus in Vietnamese Children.

INTRODUCTION: Bronchiolitis is the most prevalent cause of hospitalization in infants under the age of 12 months. The disease is caused by respiratory syncytial virus (RSV) infection, which can cause breathing difficulties and respiratory failure. Therefore, it is necessary to discover the risk factors of severe bronchiolitis to diagnose and treat promptly. This study is aimed at describing the epidemiological characteristics and clinical features of acute bronchiolitis caused by RSV and assessing the related factors to severe acute bronchiolitis in studied patients. METHODS: A descriptive cross-sectional study was carried out in Haiphong Children's Hospital, Haiphong, Vietnam, for one year, from October 1, 2016, to September 30, 2017. All bronchiolitis admissions < 2 years were included. RESULTS: 377 children were evaluated, including 261 boys and 116 girls; children under 6 months accounted for the highest proportion (57%), and 47 (12.5%) of all patients had severe disease. Wheezing was the main reason to be taken to the hospital 261 (69.2%). Clinical symptoms of acute bronchiolitis such as cough, tachypnea, and runny nose were found in all patients. Bronchiolitis cases increased in the winter-spring season, and the highest registered number of patients was 42 in March. Image of bronchiolitis on chest X-ray was found in all patients, and air trapping lung was found in 124 (32.9%) patients. The risk factors included age (≤6 months), low birth weight, preterm birth, nonbreastfeeding for the first six months, early weaning, and exposition to cigarette smoke increased the severe disease (p < 0.05). CONCLUSION: The number of hospitalized infants with bronchiolitis caused by RSV has an upward trend during the winter-spring season (from October to March). This study confirms that age, preterm birth, breastfeeding under 6 months, history of exposure to cigarette smoking, low birth weight, having sibling(s) under five years old going to kindergarten, history of undergoing cesarean section, history of mechanical ventilation, poor living condition, and maternal education are 10 risk factors of severe bronchiolitis caused by RSV.

Determination of Per- and Polyfluoroalkyl Substances in Craft Villages and Industrial Environments of Vietnam.

Per- and polyfluoroalkyl substances (PFASs) have attracted great concern because of their great recalcitrant nature and harmful environmental health effects. Eight PFASs in wastewater from craft villages and industrial environments of Vietnam were analyzed using liquid chromatography triple quadrupole mass spectrometry (LC-MS/MS) with negative electrospray ionization interface. For analysis of PFASs, percent recoveries ranged from 87 to 112, and MQL varied from 0.19 ng/L to 0.49 ng/L. Treated wastewater samples from eight metal-plating and eight textile-dyeing factories were collected for analysis of PFASs. Concentrations of PFOS in wastewater samples obtained from metal-plating factories with decorative plating stage were found at a range of 0.73-18.91 ng/L. For textile-dyeing factories, PFOA and/or PFHxA, which were present in all effluent wastewater samples, varied from 0.37 to 15.96 ng/L and 1.07 to 43.58 ng/L, respectively. Sixty surface water samples in four locations of the textile dyeing craft villages, a recycling plastic village, a paper recycling village, and 10 river water samples in the control area (a rural area without specific waste sources) were collected and analyzed for PFASs. The total concentrations of eight PFASs in surface water samples of craft villages ranged from 0.83 to 58.2 ng/L, which were significantly higher than those in the control area. PFOA, PFHxA, and PFOS are the three most dominant congeners in wastewater taken from craft villages with the highest concentrations of 27.4, 23.8, and 7.36 ng/L, respectively. The environmental risks posed by PFASs in surface water from craft villages were mainly in a range of extremely low to low level, particularly a few points have high ecological risks of PFDoA.

Occurrence, Distribution, and Ecological Risk Assessment of Antibiotics in Selected Urban Lakes of Hanoi, Vietnam.

Residue concentrations of fifteen antibiotics including sulfonamides, quinolones, macrolides, ß-lactams, and trimethoprim in lakes from Hanoi metropolitan area, Vietnam, were analyzed using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC/MS-MS) to elucidate their occurrence and behavior in urban environment. For surface water, the average concentrations of five antibiotic classes decreased in the order: sulfonamides (117.9 ng/L) > ß-lactams (31.28 ng/L) > quinolones (20.19 ng/L) > macrolides (17.74 ng/L) > trimethoprim (8.93 ng/L). While the highest concentration of SMX was detected at 806.5 ng/L in surface water, those obtained in sediment were only at 1.35 ng/g because of their high solubility in water. Quinolones were found at a maximal concentration of 158.7 ng/L for OFL in water phase whereas those in sediment phase were 4,017 ng/g due to their great affinity in sediment. These findings revealed the different fate and release mechanisms of each antibiotic group in the environment. The ecological risk assessment implied some targeted compounds, and in particular, OFL and AZM could pose high risks to algae in the aquatic ecosystem.

Optimum fabrication parameters for preparing high performance SERS substrates based on Si pyramid structure and silver nanoparticles.

In this work, we propose simple and inexpensive methods to prepare micro/nano hierarchical Surface-Enhanced Raman Scattering (SERS) substrates, in which pyramid structure is created by using anisotropic wet etching of a silicon wafer and a silver thin film is deposited on these pyramid arrays by thermal evaporation. The ensemble is then annealed at 450 °C for 2 hours to form silver nanoparticles (AgNPs). The sizes and density of the pyramids and AgNPs are optimized mainly by changing the etching temperature (60-80 °C), the thickness of the Ag-film (15-45 nm) and etching time (3-10 min). The ultraviolet visible (UV-Vis) absorbance spectra show that the AgNPs formed with the 30 nm-thick film exhibit the strongest plasmonic effect. Under these conditions, the spherical AgNPs with sizes of 42-48 nm are densely distributed on the silicon micro-pyramid array. The obtained SERS signal is the strongest at the pyramid base-edge size of 7-10 µm. The enhancement factor obtained from the abamectin probe molecules is as high as 1 × 106 and the SERS substrates enable the detection of abamectin concentrations as low as 5.7 × 10-9 M. Therefore, this work provides a novel SERS substrate structure that has a high potential for use in medicine and biotechnology or as a food security sensor.