Energetics of the OH radical H-abstraction reactions from simple aldehydes and their geminal diol forms.

CONTEXT: Carbonyl compounds, especially aldehydes, emitted to the atmosphere, may suffer hydration in aerosols or water droplets in clouds. At the same time, they can react with hydroxyl radicals which may add or abstract hydrogen atoms from these species. The interplay between hydration and hydrogen abstraction is studied using density functional and quantum composite theoretical methods, both in the gas phase and in simulated bulk water. The H-abstraction from the aldehydic and geminal diol forms of formaldehyde, acetaldehyde, glycolaldehyde, glyoxal, methylglyoxal, and acrolein is studied to determine whether the substituent has any noticeable effect in the preference for the abstraction of one form or another. It is found that abstraction of the H-atom adjacent to the carbonyl group gives a more stable radical than same abstraction from the geminal diol in the case of formaldehyde, acetaldehyde, and glycolaldehyde. The presence of a delocalizing group in the Cα (a carbonyl group in glyoxal and methylglyoxal, and a vinyl group in acrolein), reverts this trend, and now the abstraction of the H-atom from the geminal diol gives more stable radicals. A further study was conducted abstracting hydrogen atoms from the other different positions in the species considered, both in the aldehydic and geminal diol forms. Only in the case of glycolaldehyde, the radical formed by H-abstraction from the -CH2OH group is more stable than any of the other radical species. Abstraction of the hydrogen atom in one of the hydroxyl groups in the geminal diol is equivalent to the addition of the •OH radical to the aldehyde. It leads, in some cases, to decomposition into a smaller radical and a neutral molecule. In these cases, some interesting theoretical differences are observed between the results in gas phase and (simulated) bulk solvent, as well as with respect to the method of calculation chosen. METHODS: DFT (M06-2X, B2PLYP, PW6B95), CCSD(T), and composite (CBS-QB3, jun-ChS, SCVECV-f12) methods using Dunning basis sets and extrapolation to the CBS limit were used to study the energetics of closed shell aldehydes in their keto and geminal-diol forms, as well as the radical derived from them by hydrogen abstraction. Both gas phase and simulated bulk solvent calculations were performed, in the last case using the Polarizable Continuum Model.

Manipulated overlapping reactivation of multiple memories promotes explicit gist abstraction.

Sleep is considered to promote gist abstraction on the basis of spontaneous memory reactivation. As speculated in the theory of 'information overlap to abstract (iOtA)', 'overlap' between reactivated memories, beyond reactivation, is crucial to gist abstraction. Yet so far, empirical research has not tested this theory by manipulating the factor of 'overlap'. In the current study, 'overlap' itself was manipulated by targeted memory reactivation (TMR), through simultaneously reactivating multiple memories that either contain or do not contain spatially overlapped gist information, to investigate the effect of overlapping reactivation on gist abstraction. This study had a factorial design of 2 factors with 2 levels respectively (spatial overlap/no spatial overlap, TMR/no-TMR). Accordingly, 82 healthy college students (aged 19 âˆ¼ 25, 57 females) were randomized into four groups. After learning 16 pictures, paired with 4 auditory cues (4 pictures - 1 cue) according to the grouping, participants were given a 90-minute nap opportunity. Then TMR cueing was conducted during N2 and slow wave sleep of the nap. Performance in memory task was used to measure gist abstraction. The results showed a significant main effect of TMR on both implicit and explicit gist abstraction, and a marginally significant interaction effect on explicit gist abstraction. Further analyses showed that explicit gist abstraction in the spatial overlap & TMR group was significantly better than in the control group. Moreover, explicit gist abstraction was positively correlated with spindle density. The current study thus indicates that TMR facilitates gist abstraction, and explicit gist abstraction may benefit more from overlapping reactivation.

Expecting to integrate additional information improves averaging of experience.

Humans learn both directly, from own experience, and via social communication, from the experience of others. They also often integrate these two sources of knowledge to make predictions and choices. We hypothesized that when faced with the need to integrate communicated information into personal experience, people would represent the average of experienced exemplars with greater accuracy. In two experiments, Mturk users estimated the mean of consecutively and rapidly presented number sequences that represented bonuses ostensibly paid by different providers on a crowdsource platform. Participants who expected integrating these values with verbal information about possible change in bonuses were more accurate in extracting the means of the values compared to participants who did not have such expectation. While our study focused on socially communicated information, the observed effect may potentially extend to other forms of information integration. We suggest that expected integration of experience with additional information facilitates an abstract representation of personal experiences.

Temporally extended successor feature neural episodic control.

One of the long-term goals of reinforcement learning is to build intelligent agents capable of rapidly learning and flexibly transferring skills, similar to humans and animals. In this paper, we introduce an episodic control framework based on the temporal expansion of subsequent features to achieve these goals, which we refer to as Temporally Extended Successor Feature Neural Episodic Control (TESFNEC). This method has shown impressive results in significantly improving sample efficiency and elegantly reusing previously learned strategies. Crucially, this model enhances agent training by incorporating episodic memory, significantly reducing the number of iterations required to learn the optimal policy. Furthermore, we adopt the temporal expansion of successor features a technique to capture the expected state transition dynamics of actions. This form of temporal abstraction does not entail learning a top-down hierarchy of task structures but focuses on the bottom-up combination of actions and action repetitions. Thus, our approach directly considers the temporal scope of sequences of temporally extended actions without requiring predefined or domain-specific options. Experimental results in the two-dimensional object collection environment demonstrate that the method proposed in this paper optimizes learning policies faster than baseline reinforcement learning approaches, leading to higher average returns.

Efficient Holes Abstraction by Precisely Decorating Ruthenium Single Atoms and RuOx Clusters on ZnIn2S4 for Photocatalytic Pure Water Splitting.

Developing efficient photocatalysts for two-electron water splitting with simultaneous H2O2 and H2 generation shows great promise for practical application. Currently, the efficiency of two-electron water splitting is still restricted by the low utilization of photogenerated charges, especially holes, of which the transfer rate is much slower than that of electrons. Herein, Ru single atoms and RuOx clusters are co-decorated on ZnIn2S4 (RuOx/Ru-ZIS) to employ as multifunctional sites for efficient photocatalytic pure water splitting. Doping of Ru single atoms in the ZIS basal plane enhances holes abstraction from bulk ZIS by regulating the electronic structure, and RuOx clusters offer a strong interfacial electric field to remarkably promote the out-of-plane migration of holes from ZIS. Moreover, Ru single atoms and RuOx clusters also serve as active sites for boosting surface water oxidation. As a result, an excellent H2 and H2O2 evolution rates of 581.9 µmol g-1 h-1 and 464.4 µmol g-1 h-1 is achieved over RuOx/Ru-ZIS under visible light irradiation, respectively, with an apparent quantum efficiency (AQE) of 4.36% at 400 nm. This work paves a new way to increase charge utilization by manipulating photocatalyst using single atom and clusters.

NewAbstractConcepts: A Database of 42 Normed Abstract Concepts and Exemplars.

Recently, researchers have expressed challenges in conducting word-learning experiments in adult populations due to limited availability of normed stimulus materials. This constraint often prompts the use of low-frequency or low-prevalence words, introducing the potential influence of prior knowledge or direct translation to familiar words. In response, we developed novel abstract concepts devoid of word referents, providing better control over prior knowledge. These new concepts describe situations encountered in various settings for which there is no existing word in English. The resulting database comprises 42 normed New Abstract Concepts, offering unique materials structured through scenarios, each containing similar and dissimilar exemplars. These materials underwent meticulous norming for relatability and similarity levels across a series of studies. The success of our approach was demonstrated in a word-learning experiment examining the effects of similarity and diversity. The database serves as a valuable resource for selecting stimuli in experiments exploring the learning of abstract semantic concepts, particularly investigating the role of similarity versus diversity in concept learning. The database is available on OSF (https://osf.io/svm2p/).

Hydrogen Atom Abstraction and Reduction Study of 21-Thiaporphyrin and 21,23-Dithiaporphyrin.

The metal-free porphyrins protonation has gained interest over five decades because its structure modification and hardly monoacid intermediate isolation. Here, upon the hydrogen atom abstraction processes, one step diproptonated H3STTP(BF4)2 (STTP = 5,10,15,20-tetraphenyl-21-thiaporphyrin) (3) and stepwise protonated HS2TTPSbCl6 (5) and diprotonated H2S2TTP(BF4)2 (6) (S2TTP = 5,10,15,20-tetraphenyl-21,23-thiaporphyrin) compounds were obtained using HSTTP and S2TTP with oxidants. The closed-shell protonated compounds were fully characterized using XRD, UV-vis, IR and NMR spectra. In addition, the reduced 19π compounds [K(2,2,2)]HSTTP (2) and [K(2,2,2)]S2TTP (7) were synthesized by the ligands with reductant KC8 in THF solution. These two open-shell compounds were characterized with UV-vis, IR and EPR spectroscopies. The semiempirical ZINDO/S method was employed to analyze the HOMO/LUMO gap lever and identify the electronic transitions of the UV-vis spectra of the closed- and open-shell porphyrin compounds.

System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.

Monomeric Fe(III)-Hydroxo and Fe(III)-Aqua Complexes Display Oxidative Asynchronous Hydrogen Atom Abstraction Reactivity.

This paper presents the synthesis and characterization of a series of novel monomeric aqua-ligated iron(III) complexes, [FeIII(L5R)(OH2)]2+ (R = OMe, H, Cl, NO2), supported by an amide-containing pentadentate N5 donor ligand, L5R [HL5R = 2-(((1-methyl-1H-imidazol-2-yl)methyl)(pyridin-2-yl-methyl)amino)-N-(5-R-quinolin-8-yl)acetamide]. The complexes were characterized by various spectroscopic and analytical techniques, including electrochemistry and magnetic measurements. The Fe(III)-hydroxo complexes, [FeIII(L5R)(OH)]1+, were generated in situ by deprotonating the corresponding aqua complexes in a pH ~7 aqueous medium. In another way, adding one equivalent of a base to a methanolic solution of the Fe(III)-aqua complexes also produced the Fe(III)-hydroxo complexes. The study uses linoleic fatty acid as a substrate to explore the hydrogen atom abstraction (HAA) reactivity of both hydroxo- and aqua-complexes. The investigation highlights the substitution effect of the L5R ligand on reactivity, revealing a higher rate when an electron-withdrawing group is present. Hammett analyses and(or) determination of the asynchronicity factor (η) suggest an oxidative asynchronous concerted proton-electron transfer (CPET) pathway for the HAA reactions. Aqua complexes exhibited a higher asynchronicity in CPET, resulting in higher reaction rates than their hydroxo analogues. Overall, the work provides insights into the beneficial role of a higher imbalance in electron-transfer-proton-transfer (ET-PT) contributions in HAA reactivity.

A Phone in a Basket Looks Like a Knife in a Cup: Role-Filler Independence in Visual Processing.

When a piece of fruit is in a bowl, and the bowl is on a table, we appreciate not only the individual objects and their features, but also the relations containment and support, which abstract away from the particular objects involved. Independent representation of roles (e.g., containers vs. supporters) and "fillers" of those roles (e.g., bowls vs. cups, tables vs. chairs) is a core principle of language and higher-level reasoning. But does such role-filler independence also arise in automatic visual processing? Here, we show that it does, by exploring a surprising error that such independence can produce. In four experiments, participants saw a stream of images containing different objects arranged in force-dynamic relations-e.g., a phone contained in a basket, a marker resting on a garbage can, or a knife sitting in a cup. Participants had to respond to a single target image (e.g., a phone in a basket) within a stream of distractors presented under time constraints. Surprisingly, even though participants completed this task quickly and accurately, they false-alarmed more often to images matching the target's relational category than to those that did not-even when those images involved completely different objects. In other words, participants searching for a phone in a basket were more likely to mistakenly respond to a knife in a cup than to a marker on a garbage can. Follow-up experiments ruled out strategic responses and also controlled for various confounding image features. We suggest that visual processing represents relations abstractly, in ways that separate roles from fillers.

Red-Light Triggered H-Abstraction Photoinitiators for the Efficient Oxygen-Independent Therapy of Hypoxic Tumors.

The clinical application of photodynamic therapy (PDT) is limited by oxygen-dependence and side effects caused by photosensitizer residues. Photoinitiators based on the H-abstraction reaction can address these challenges because they can generate alkyl radical-killing cells independently of oxygen and undergo rapid bleaching following H-abstraction. Nonetheless, the development of photoinitiators for PDT has been impeded by the absence of effective design strategies. Herein, we have developed aryl-ketone substituted cyanine (ACy-R), the first red-light triggered H-abstraction photoinitiators for hypoxic cancer therapy. These ACy-R molecules inherited the near-infrared absorption of cyanine dye, and aryl-ketone modification imparted H-abstraction capability. Experimental and quantum calculations revealed that modifying the electron-withdrawing groups of the aryl (e.g., ACy-5F) improved the contribution of the O atom to the photon excitation process promoting intersystem crossing and H-abstraction ability. Particularly, ACy-5F rapidly penetrated cells and enriched in the endoplasmic reticulum. Even under severe hypoxia, ACy-5F initiated red-light induced H-abstraction with intracellular biomolecules, inducing necroptosis and ferroptosis. Moreover, ACy-5F was degraded after H-abstraction, thus avoiding the side effects of long-term phototoxicity after therapy. This study not only provides a crucial molecular tool for hypoxic tumors therapy, but also presents a promising strategy for the development of multifunctional photosensitizers and photoinitiators.

Toward the Emergence of Intelligent Control: Episodic Generalization and Optimization.

Human cognition is unique in its ability to perform a wide range of tasks and to learn new tasks quickly. Both abilities have long been associated with the acquisition of knowledge that can generalize across tasks and the flexible use of that knowledge to execute goal-directed behavior. We investigate how this emerges in a neural network by describing and testing the Episodic Generalization and Optimization (EGO) framework. The framework consists of an episodic memory module, which rapidly learns relationships between stimuli; a semantic pathway, which more slowly learns how stimuli map to responses; and a recurrent context module, which maintains a representation of task-relevant context information, integrates this over time, and uses it both to recall context-relevant memories (in episodic memory) and to bias processing in favor of context-relevant features and responses (in the semantic pathway). We use the framework to address empirical phenomena across reinforcement learning, event segmentation, and category learning, showing in simulations that the same set of underlying mechanisms accounts for human performance in all three domains. The results demonstrate how the components of the EGO framework can efficiently learn knowledge that can be flexibly generalized across tasks, furthering our understanding of how humans can quickly learn how to perform a wide range of tasks-a capability that is fundamental to human intelligence.

Trace Br- Inhibits Halogenated Byproduct Formation in Saline Wastewater Electrochemical Treatment.

The electrochemical technology provides a practical and viable solution to the global water scarcity issue, but it has an inherent challenge of generating toxic halogenated byproducts in treatment of saline wastewater. Our study reveals an unexpected discovery: the presence of a trace amount of Br- not only enhanced the electrochemical oxidation of organic compounds with electron-rich groups but also significantly reduced the formation of halogenated byproducts. For example, in the presence of 20 µM Br-, the oxidation rate of phenol increased from 0.156 to 0.563 min-1, and the concentration of total organic halogen decreased from 59.2 to 8.6 µM. Through probe experiments, direct electron transfer and HO• were ruled out as major contributors; transient absorption spectroscopy (TAS) and computational kinetic models revealed that trace Br- triggers a shift in the dominant reactive species from Cl2•- to Br2•-, which plays a key role in pollutant removal. Both TAS and electron paramagnetic resonance identified signals unique to the phenoxyl and carbon-centered radicals in the Br2•--dominated system, indicating distinct reaction mechanisms compared to those involving Cl2•-. Kinetic isotope experiments and density functional theory calculations confirmed that the interaction between Br2•- and phenolic pollutants follows a hydrogen atom abstraction pathway, whereas Cl2•- predominantly engages pollutants through radical adduct formation. These insights significantly enhance our understanding of bromine radical-involved oxidation processes and have crucial implications for optimizing electrochemical treatment systems for saline wastewater.

The relational bottleneck as an inductive bias for efficient abstraction.

A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. In that approach, neural networks are constrained via their architecture to focus on relations between perceptual inputs, rather than the attributes of individual inputs. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain.

Salience Interest Option: Temporal abstraction with salience interest functions.

Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions, also known as options. The issue of discovering options has seen a considerable research effort. Most notably, the Interest Option Critic (IOC) algorithm first extends the initial set to the interest function, providing a method for learning options specialized to certain state space regions. This approach offers a specific attention mechanism for action selection. Unfortunately, this method still suffers from the classic issues of poor data efficiency and lack of flexibility in RL when learning options end-to-end through backpropagation. This paper proposes a new approach called Salience Interest Option Critic (SIOC), which chooses subsets of existing initiation sets for RL. Specifically, these subsets are not learned by backpropagation, which is slow and tends to overfit, but through particle filters. This approach enables the rapid and flexible identification of critical subsets using only reward feedback. We conducted experiments in discrete and continuous domains, and our proposed method demonstrate higher efficiency and flexibility than other methods. The generated options are more valuable within a single task and exhibited greater interpretability and reusability in multi-task learning scenarios.

A Mechanistic Spectrum of O-H Bond Cleavage Observed for Reactions of Phenols with a Manganese Superoxo Complex.

Reaction of a rare and well-characterized MnIII-superoxo species, Mn(BDPBrP)(O2⋅) (1, H2BDPBrP=2,6-bis((2-(S)-di(4-bromo)phenylhydroxylmethyl-1-pyrrolidinyl)methyl)pyridine), with 4-dimethylaminophenol at -80 °C proceeds via concerted proton electron transfer (CPET) to produce a MnIII-hydroperoxo complex, Mn(BDPBrP)(OOH) (2), alongside 4-dimethylaminophenoxy radical; whereas, upon treatment with 4-nitrophenol, complex 1 undergoes a proton transfer process to afford a MnIV-hydroperoxo complex, [Mn(BDPBrP)(OOH)]+ (3). Intriguingly, the reactions of 1 with 4-chlorophenol and 4-methoxyphenol follow two routes of CPET and sequential proton and electron transfer to furnish complex 2 in the end. UV-vis and EPR spectroscopic studies coupled with DFT calculations provided support for this wide mechanistic spectrum of activating various phenol O-H bonds by a single MnIII-superoxo complex, 1.

Classified Dynamic Programming in RNA Structure Analysis.

Analysis of the folding space of RNA generally suffers from its exponential size. With classified Dynamic Programming algorithms, it is possible to alleviate this burden and to analyse the folding space of RNA in great depth. Key to classified DP is that the search space is partitioned into classes based on an on-the-fly computed feature. A class-wise evaluation is then used to compute class-wide properties, such as the lowest free energy structure for each class, or aggregate properties, such as the class' probability. In this paper we describe the well-known shape and hishape abstraction of RNA structures, their power to help better understand RNA function and related methods that are based on these abstractions.

Bridging the Chasm Between Cognitive Representations and Formal Structures of Linguistic Meanings.

This paper aims to show that properties of cognitive/conceptual representations and formal-logical structures of linguistic meaning can be inter-translated, recast, transformed into one another, and so united together, even though cognitive/conceptual representations and formal-logical structures of linguistic meaning are apparently distinct in ontology and divergent in their form or character. While cognitive/conceptual representations are ultimately rooted in sensory-motor systems, formal-logical structures of linguistic meaning are abstractions detached from and independent of the actualized world. This paper sketches out the foundations of how representations of linguistic meaning in terms of cognitive/conceptual structures in Cognitive/Conceptual Semantics can be unified with those in terms of formal-logical structures in Formal Semantics. This is done by recasting cognitive/conceptual representations in terms of formal-logical structures of linguistic meaning and re-encoding formal-logical structures of linguistic meaning in terms of cognitive/conceptual representations. Then, these two types of semantic representations, thus shown representationally equivalent, will be related to a series of derivations across levels in neuronal networks and dynamics. The general discussion on unifying cognitive/conceptual representations of linguistic meaning with formal-logical structures is contextualized within the broader context of theorizing in cognitive science.

Space as a scaffold for rotational generalisation of abstract concepts.

Learning invariances allows us to generalise. In the visual modality, invariant representations allow us to recognise objects despite translations or rotations in physical space. However, how we learn the invariances that allow us to generalise abstract patterns of sensory data ('concepts') is a longstanding puzzle. Here, we study how humans generalise relational patterns in stimulation sequences that are defined by either transitions on a nonspatial two-dimensional feature manifold, or by transitions in physical space. We measure rotational generalisation, i.e., the ability to recognise concepts even when their corresponding transition vectors are rotated. We find that humans naturally generalise to rotated exemplars when stimuli are defined in physical space, but not when they are defined as positions on a nonspatial feature manifold. However, if participants are first pre-trained to map auditory or visual features to spatial locations, then rotational generalisation becomes possible even in nonspatial domains. These results imply that space acts as a scaffold for learning more abstract conceptual invariances.

U.S. Women with Invasive Cervical Cancer: Characteristics and Potential Barriers to Prevention.

Objectives: Although invasive cervical cancer (ICC) rates have declined since the advent of screening, the annual age-adjusted ICC rate in the United States remains 7.5 per 100,000 women. Failure of recommended screening and management often precedes ICC diagnoses. The study aimed to evaluate characteristics of women with incident ICC, including potential barriers to accessing preventive care. Materials and Methods: We abstracted medical records for patients with ICC identified during 2008-2020 in five U.S. population-based surveillance sites covering 1.5 million women. We identified evidence of adverse social and medical conditions, including uninsured/underinsured, language barrier, substance use disorder, incarceration, serious mental illness, severe obesity, or pregnancy at diagnosis. We calculated descriptive frequencies and compared potential barriers by race/ethnicity, and among women with and without symptoms at diagnosis using chi-square tests. Results: Among 1,606 women with ICC (median age: 49 years; non-White: 47.4%; stage I: 54.7%), the majority (68.8%) presented with symptoms. Forty-six percent of women had at least one identified potential barrier; 15% had multiple barriers. The most common potential barriers among all women were being underinsured/uninsured (17.3%), and language (17.1%). Presence of any potential barrier was more frequent among non-White women and women with than without symptoms (p < 0.05). Conclusions: In this population-based descriptive study of women with ICC, we identified adverse circumstances that might have prevented women from seeking screening and treatment to prevent cancer. Interventions to increase appropriate cervical cancer screening and management are critical for reducing cervical cancer rates.