Unique Interaction Pattern Research Articles

River stabilization reshaped human-nature interactions in the Lower Yellow River Floodplain

Floodplains are crucial agricultural and populated areas worldwide. Rivers typically shape human activities within floodplains through water supply and flood risk, forming unique human-nature interaction patterns. Given that river systems have undergone significant transformations globally, understanding the response of these interactions to hydrological changes is elementary. Here, using the Lower Yellow River Floodplain (LYRF) as a case, where continuous levees distinctly outline the river-influenced floodplain from a homogenous cultivated plain and the river's hydrology has undergone dramatic alterations since the 1990s, we analyzed how the human-nature interactions respond to river hydrological changes. This study found that the flood-prone nature of the Lower Yellow River has weakened from 1986 to 2021 as shown by decreased surface water extent and inundated extent of cropland. The intensity of agriculture inside the LYRF has been lower but experienced faster development than outside, minimizing the disparity between them. Meanwhile, human activities in the floodplain have moved closer to the river. These changes in human-nature interactions in the LYRF can be explained by the river stabilization caused by upstream regulation, underscoring the significance of integrated river governance in managing human-nature system in floodplain areas.

Identity Construction in Fan Community: An Analysis of Online Discourse among (G)I-DLE Fans in Mainland China on Sina Weibo

Fandom has become a social and cultural phenomenon reflecting practical activities in the fan community. The prevalence of social media and online fan communities has facilitated the transnational industries of Korean popular culture (K-pop) in mainland China. Fans are enthusiastic about creating posts dedicated to their idol(s) or music bands to broaden the influence of the celebrities and establish personal connections with them. The unique interaction patterns in the K-pop fan community reflect the process of fans’ identity identification and construction. This article explores how mainland fans employ discursive strategies to construct their identity and legitimize their behaviors on social media. A total of 80 posts in (G)I-DLE Super Topic made by mainland China netizens have been collected from Sina Weibo. The findings illustrate the construction of both individual and collective identities among fans, which are shaped by various contexts and different communicative purposes. Further analysis explores the potential impact of fans' identities within online communities on the K-pop market in mainland China.

Identity Construction in Fan Community: An Analysis of Online Discourse among (G)I-DLE Fans in Mainland China on Sina Weibo

Fandom has become a social and cultural phenomenon reflecting practical activities in the fan community. The prevalence of social media and online fan communities has facilitated the transnational industries of Korean popular culture (K-pop) in mainland China. Fans are enthusiastic about creating posts dedicated to their idol(s) or music bands to broaden the influence of the celebrities and establish personal connections with them. The unique interaction patterns in the K-pop fan community reflect the process of fans’ identity identification and construction. This article explores how mainland fans employ discursive strategies to construct their identity and legitimize their behaviors on social media. A total of 80 posts in (G)I-DLE Super Topic made by mainland China netizens have been collected from Sina Weibo. The findings illustrate the construction of both individual and collective identities among fans, which are shaped by various contexts and different communicative purposes. Further analysis explores the potential impact of fans' identities within online communities on the K-pop market in mainland China.

Synthesis, design, biological activity, DFT study and molecular docking of new 1,2,4-triazine and 1,2,4-triazol derivatives bearing the phthalazine moiety

The present work aims to synthesise a series of new compounds with a combination of 1,2,4-triazine and 1,2,4-triazole with a phthalazine components that have been created from compound (M4). The structural formula of these new compounds was confirmed by FT-IR, 1HNMR , 13CNMR and Mass spectra. Density functional theory (DFT) simulations utilising the Gausean-9 programme were used to rationalise the mechanism of all novel compounds. They demonstrations that the energy gap between compounds (M8–M10) is 0.09951 a.u., 0.108133 a.u. and 0.9857 a.u., respectively, between HOMO and LOMO. The molecular docking analysis unveiled the existence of several derivatives exhibiting significant binding affinities and unique interaction patterns with the target proteins. Derivatives M8–10 have been identified as the most favourable candidates, demonstrating the highest binding energies and a diverse range of interaction types, such as hydrogen bonding and pi interactions, across various distances. Derivative (M8–10) exhibits the highest binding energy at S = -8.15, -8.18, or -8.28 Kcal/mol with the 2VAM receptor and -8.52, -8.78 and -9.04 Kcal/mol with the 2G1H receptor. The analysis of the inhibition of the growth values in different concentrations against both Escherichia coli and Bacillus subtilis bacteria compared with amoxicillin. The presented research obviously demonstrates that compounds (M8–10) can serve as promising candidates for alternatives to new medications. The empirical findings were consistent with the anticipated outcomes derived from the molecular docking analysis.

Employing advanced computational drug discovery techniques to identify novel inhibitors against ML2640c protein: a potential therapeutic approach for combatting leprosy.

Leprosy, caused by Mycobacterium leprae, remains a significant global health challenge, necessitating innovative approaches to therapeutic intervention. This study employs advanced computational drug discovery techniques to identify potential inhibitors against the ML2640c protein, a key factor in the bacterium's ability to infect and persist within host cells. Utilizing a comprehensive methodology, including virtual screening, re-docking, molecular dynamics simulations, and free energy calculations, we screened a library of compounds for their interaction with ML2640c. Four compounds (24349836, 26616083, 26648979, and 26651264) demonstrated promising inhibitory potential, each exhibiting unique binding energies and interaction patterns that suggest a strong likelihood of disrupting the protein function. The study highlights the efficacy of computational methods in identifying potential therapeutic candidates, presenting compound 26616083 as a notably potent inhibitor due to its excellent binding affinity and stability. Our findings offer a foundation for future experimental validation and optimization, marking a significant step forward in the development of new treatments for leprosy. This research not only advances the fight against leprosy but also showcases the broader applicability of computational drug discovery in tackling infectious diseases.

A data-driven exploration of the complexity of the phosphorus-centered pnictogen bonds

Phosphorus-centered pnictogen bonds, similar to well-studied halogen bonds, play a vital role in molecular recognition and assembly. This study aims to explain how pnictogen bonds interact with other noncovalent interactions. A model system, phosphines (PH3), which is also present in phosphorus cycles and reduces planetary atmospheres, is examined. The investigation of the phosphorus trifluoride (PF3) molecule explores its substituent effects. To generate a broad spectrum of molecular configurations, a specially tailored protocol for sampling and optimisation was implemented. The configurations were refined to uncover primary interaction patterns, and a clustering algorithm revealed unique interaction patterns. This report presents the energy stability and distribution of all the clusters. Our findings verify the prevalent presence of pnictogen bonds, identified by their geometric characteristics and co-occurrence with hydrogen bonds, which show an almost linear correlation. Another significant discovery is the correlation between various energy decomposition elements, especially regarding the electrostatic energy and overall binding energy. These results are anticipated to significantly contribute towards our comprehension of non-covalent interactions among phosphorus-containing molecules and the formulation of empirical models having physical interpretations.

Exploring the anti-inflammatory potential of the polyphenolic compounds in Moringa oleifera leaf: in silico molecular docking

ABSTRACT A recent study conducted an in silico model to investigate the potential of Moringa oleifera’s ethanolic leaf extract as a natural anti-inflammatory agent. The HPLC analysis identified 7 phenolic acids (benzoic acid 28%, syringic acid 23%, ellagic acid 19%, chlorogenic acid 12%, catechol 11%, gallic acid 5%, and caffeic acid 2%) and 5 flavonoids (naringenin 31%, kaempferol 30%, apigenin 14%, rutin 13%, and quercetin 12%) that are known to have anti-inflammatory activities in vitro tested on human interleukin-1β (IL-1β) and interleukin-6 (IL-6) in a previously prepared study (Hamdy, 2023) The Moringa polyphenol compounds were docked against IL-1β, IL-6, and the human interleukin-1 receptor (IL1RA) using Auto-Dock Vina. Caffeic acid, chlorogenic acid, and kaempferol interacted with amino acid residues in IL-1β by creating hydrogen bonds and hydrophobic interactions. The co-crystalized ligand attached to IL-1β displayed an affinity score of −8.96 kcal/mol. Caffeic acid, chlorogenic acid, quercetin, rutin, and syringic acid had unique interaction patterns with IL-6, forming hydrogen bonds and being hydrophobic. The interactions’ binding energies ranged from −5.97 to 5.26 kcal/mol. Moringa’s polyphenolic compounds also docked with IL1RA, where rutin and chlorogenic acid exhibited binding energies of −8.96 kcal/mol and −7.12 kcal/mol, respectively, and created hydrophobic interactions and hydrogen bonds. In conclusion, Moringa oleifera polyphenols can interact with IL-1β, IL-6, and IL-1RA to reduce inflammation, making it a potential treatment strategy for improving recovery.

149 Dynamical Network State Sequences for Human Language Production

INTRODUCTION: Speech requires the selection of a conceptual representation, the construction of a word form, and the execution of a complex articulatory plan. Investigating this global system requires high-resolution recordings with an analytic approach to resolve discrete cognitive states. We integrated autoregressive hidden Markov models to resolve trial-by-trial state transition sequences in distributed networks derived from a large-scale electrocorticographic dataset with complete coverage of language-dominant cortex. METHODS: Intracranial electrodes (n = 25810, 134 patients), including both surface grids and stereotactic depths, were implanted for the evaluation of epilepsy. Patients performed picture naming of common objects and underwent systematic cortical stimulation mapping of language function. We used cross-validated autoregressive hidden Markov models – combining the interpretability of multivariate autoregressive analysis with the nonlinear embedding of the switching Markov characteristic – to distinguish cognitive states defined by causal interactional motifs of distributed cortical substrates and to simulate in silico lesions of dynamical network structures. RESULTS: We created a detailed spatiotemporal atlas of word production spanning the entire language-dominant cortex. From this map, we resolved network dynamics comprising sequences of state space transitions for each word articulated. This identified five essential states for speech production, each defined by a unique pattern of directed interactions within the language network. We then derived a computational lesion model for state dynamics and compared its predictions with causal perturbation by direct cortical stimulation. We assembled a global dynamical state model for language production and resolved the fine-scale interregional dynamics of conceptualization and lexical access. CONCLUSIONS: The distributed network dynamics detailed within this comprehensive interactional map of speech production advance our understanding of how both local and disconnection injuries yield complex neurological deficits, facilitating the development of novel therapeutic approaches for aphasia.

Representing and discovering heterogeneous interactions for financial risk assessment of SMEs

Small and medium-sized enterprises (SMEs) generally have weak risk management abilities, yet their financial risk assessment faces unique challenges due to limited information about those firms. The emergence of network data constructed from the interactions between the SMEs and other stakeholders createsan opportunity to enhance the assessment of SMEs’ financial risks. While existing studies have extracted features from thenetwork data to utilize structural information, they overlook the heterogeneous interactions among the focal firms and other entities in the network. This research aims to fill the above gaps by proposing a represent-then-discover framework based on graph neural networks. The framework comprises an entity-importance based graph contrastive model (EIGC) that represents firms as the embeddings of their heterogeneous interactions and a novel subgraph-distillation based frequent interaction mining model (SDIM) that discovers heterogeneous interactions that signal firms with or without financial risks. The firm embeddings are first trained following an unsupervised paradigm and thenused to build models for predicting financial risks. Our empirical evaluation results not only demonstrate the superior performance of EIGC over state-of-the-art methods but also discover unique interaction patterns that distinguish normal SMEs compared to financial risk SMEs. This study contributes an effective framework for assessing and explaining SMEs’ financial risk in the fintech era. The proposed EIGC provides a promising avenue for constructing predictive features by leveraging network data and graph neural networks.

Polymorphism-driven Distinct Nanomechanical, Optical, Photophysical, and Conducting Properties in a Benzothiophene-quinoline.

Polymorphic forms of organic conjugated small molecules, with their unique molecular shapes, packing arrangements, and interaction patterns, provide an excellent opportunity to uncover how their microstructures influence their observable properties. Ethyl-2-(1-benzothiophene-2-yl)quinoline-4-carboxylate (BZQ) exists as dimorphs with distinct crystal habits - blocks (BZB) and needles (BZN). The crystal forms differ in their molecular arrangements - BZB has a slip-stacked column-like structure in contrast to a zig-zag crystal packing with limited π-overlap in BZN. The BZB crystals characterized by extended π-stacking along [100] demonstrated semiconductor behavior, whereas the BZN, with its zig-zag crystal packing and limited stacking characteristics, was reckoned as an insulator. Monotropically related crystal forms also differ in their nanomechanical properties, with BZB crystals being considerably softer than BZN crystals. This discrepancy in mechanical behavior can be attributed to the distinct molecular arrangements adopted by each crystal form, resulting in unique mechanisms to relieve the strain generated during nanoindentation experiments. Waveguiding experiments on the acicular crystals of BZN revealed the passive waveguiding properties. Excitation of these crystals using a 532 nm laser confirmed the propagation of elastically scattered photons (green) and the subsequent generation of inelastically scattered (orange) photons by the crystals. Further, the dimorphs display dissimilar photoluminescence properties; they are both blue-emissive, but BZN displays twice the quantum yield of BZB. The study underscores the integral role of polymorphism in modulating the mechanical, photophysical, and conducting properties of functional molecular materials. Importantly, our findings reveal the existence of light-emitting crystal polymorphs with varying electric conductivity, a relatively scarce phenomenon in the literature.

Bias-force guided simulations combined with experimental validations towards GPR17 modulators identification

Glioblastoma Multiforme (GBM) is known to be by far the most aggressive brain tumor to affect adults. The median survival rate of GBM patient’s is < 15 months, while the GBM cells aggressively develop resistance to chemo- and radiotherapy with their self-renewal capacity which suggests the pressing need to develop novel preventative measures. We have recently proved that GPR17 —an orphan G protein-coupled receptor— is highly expressed on the GBM cell surface and it has a vital role to play in the disease progression. Despite the progress made on GBM downregulation, there still remain difficulties in developing a promising modulator for GPR17, till date. Here, we have performed robust virtual screening combined with biased-force pulling molecular dynamic (MD) simulations to predict high-affinity GPR17 modulators followed by experimental validation. Initially, the database containing 1379 FDA-approved drugs were screened against the orthosteric binding pocket of the GPR17. The external bias-potentials were then applied to the screened hits during the MD simulations which enabled to predict a spectrum of rupture peak force values that were used to select four approved drugs –ZINC000003792417 (Sacubitril), ZINC000014210457 (Victrelis), ZINC000001536109 (Pralatrexate) and ZINC000003925861 (Vorapaxar)– as top hits. The hits selected turns out to demonstrate unique dissociation pathways, interaction pattern, and change in polar network over time. Subsequently the selected hits with GPR17 were measured by inhibiting the forskolin-stimulated cAMP accumulation in GBM cell lines, LN229 and SNB19. The ex vivo validations shows that Sacubitril drug can act as a full agonist, while Vorapaxar functions as a partial agonist for GPR17. The pEC50 of Sacubitril was identified as 4.841 and 4.661 for LN229 and SNB19, respectively. Small interference of the RNA (siRNA)– silenced the GPR17 to further validate the targeted binding of Sacubitril with GPR17. In the current investigation, we have identified new repurposable GPR17 specific drugs which are likely to increase the opportunity to treat orphan deadly diseases.

Binding of human ACE2 and RBD of Omicron enhanced by unique interaction patterns among SARS-CoV-2 variants of concern.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, has continued to mutate and spread worldwide despite global vaccination efforts. In particular, the Omicron variant, first identified in South Africa in late November 2021, has become the dominant strain worldwide. Compared to the original strain identified in Wuhan, Omicron features 50 genetic mutations, with 15 mutations in the receptor-binding domain (RBD) of the spike protein, which binds to the human angiotensin-converting enzyme 2 (ACE2) receptor for viral entry. However, it is not completely understood how these mutations alter the interaction and binding strength between the Omicron RBD and ACE2. In this study, we used a combined steered molecular dynamics (SMD) simulation and experimental microscale thermophoresis (MST) approach to quantify the interaction between Omicron RBD and ACE2. We report that the Omicron brings an enhanced RBD-ACE2 interface through N501Y, Q498R, and T478K mutations; the changes further lead to unique interaction patterns, reminiscing the features of previously dominated variants, Alpha (N501Y) and Delta (L452R and T478K). Among the Q493K and Q493R, we report that Q493R shows stronger binding to ACE2 than Q493K due to increased interactions. Our MST data confirmed that the Omicron mutations in RBD are associated with a five-fold higher binding affinity to ACE2 compared to the RBD of the original strain. In conclusion, our results could help explain the Omicron variant's prevalence in human populations, as higher interaction forces or affinity for ACE2 likely promote greater viral binding and internalization, leading to increased infectivity.

Investigating the allosteric response of the PICK1 PDZ domain to different ligands with all-atom simulations.

The PDZ family is comprised of small modular domains that play critical roles in the allosteric modulation of many cellular signaling processes by binding to the C-terminal tail of different proteins. As dominant modular proteins that interact with a diverse set of peptides, it is of particular interest to explore how different binding partners induce different allosteric effects on the same PDZ domain. Because the PICK1 PDZ domain can bind different types of ligands, it is an ideal test case to answer this question and explore the network of interactions that give rise to dynamic allostery. Here, we use all-atom molecular dynamics simulations to explore dynamic allostery in the PICK1 PDZ domain by modeling two PICK1 PDZ systems: PICK1 PDZ-DAT and PICK1 PDZ-GluR2. Our results suggest that ligand binding to the PICK1 PDZ domain induces dynamic allostery at the αA helix that is similar to what has been observed in other PDZ domains. We found that the PICK1 PDZ-ligand distance is directly correlated with both dynamic changes of the αA helix and the distance between the αA helix and βB strand. Furthermore, our work identifies a hydrophobic core between DAT/GluR2 and I35 as a key interaction in inducing such dynamic allostery. Finally, the unique interaction patterns between different binding partners and the PICK1 PDZ domain can induce unique dynamic changes to the PICK1 PDZ domain. We suspect that unique allosteric coupling patterns with different ligands may play a critical role in how PICK1 performs its biological functions in various signaling networks.

Conceptualizing Health Communication and its Impact on Patient Outcomes in Oncology Outpatient Settings: A Mixed-Methods Study

ObjectivesInformed by the dual process theory of supportive message, the aim of this study is to systematically describe symptom communication, including its relationship with patient outcomes. Data SourcesThis is a mixed-methods study with an exploratory design. By examining symptom communication that occurred in oncology and hospice outpatient clinics, the qualitative phase employed conversation analysis to validate a typology of interaction patterns. The subsequent quantitative phase examined the relationship between interaction patterns and patient outcomes. ConclusionA total of 52 cancer patients’ outpatient communications with their health care providers were included in the analysis. Ten unique interaction patterns were identified and defined. Among the 10 interaction patterns, some patterns are significantly associated with critical patient outcomes, including satisfaction, health communication self-efficacy, and symptom agreement between patients and their health care providers. This study represents one of the few mixed-methods studies to examine the patterns of real outpatient symptom communications and link them to concrete patient outcomes. Implications for Nursing PracticeOur results present various interaction patterns that are commonly used in medical encounters and suggest that using some patterns affects critical patient outcomes.

TAAR1 dependent and independent actions of the potential antipsychotic and dual TAAR1/5-HT1A receptor agonist SEP-363856

SEP-363856 (SEP-856) is a novel antipsychotic under clinical development. It displays a unique pattern of receptor interaction, with only weak (partial agonist) activity at dopamine D2 receptors, yet more potent agonist activity at the trace amine associated receptor (TAAR1) and 5-hydroxytryptamine 1 A receptor (5-HT1A). Nonetheless, these observations await independent confirmation and more detailed characterization of the in vitro and in vivo actions of SEP-856 at TAAR1 and 5-HT1A receptors would be instructive. Herein, we employed luminescence complementation technology in heterologous live cell systems, confocal microscopy, voltage clamp electrophysiology, behavioral readouts and TAAR1 knockout (KO) mice to study SEP-856 in further detail. We provide evidence for the ability of SEP-856 to activate TAAR1 at the surface plasma membrane, and show that this interaction results in Gαs recruitment (pEC50: 6.08 ± 0.22 EMAX: 96.41% ± 15.26) and by extension, to G-protein inwardly rectifying potassium (GIRK) channel activation. Using TAAR1-KO mice, we find TAAR1 to be indispensable for SEP-856 control of body temperature, baseline locomotion reduction and for “antipsychotic-like” efficacy as characterized by a reversal of dizocilipine (MK-801) mediated disruption of pre-pulse inhibition. Conversely, the inhibition by SEP-856 of MK-801 induced locomotion was unaffected in TAAR1 KO mice. SEP-856 behaved as a low-potency, partial agonist at the 5-HT1A receptor, while it partially inhibited recruitment of D2 receptor-coupled Gα and GIRK by DA and acted as a weak partial agonist with low potency at the same receptor when applied alone. Our findings corroborate and extend previous observations on the molecular substrates engaged by this unique, dual TAAR1/5-HT1A receptor agonist and potential antipsychotic that could prove to have major advantages in the treatment of schizophrenia and other psychotic disorders.

Three Binding Conformations of BIO124 in the Pocket of the PICK1 PDZ Domain.

The PDZ family has drawn attention as possible drug targets because of the domains’ wide ranges of function and highly conserved binding pockets. The PICK1 PDZ domain has been proposed as a possible drug target because the interactions between the PICK1 PDZ domain and the GluA2 subunit of the AMPA receptor have been shown to progress neurodegenerative diseases. BIO124 has been identified as a sub µM inhibitor of the PICK1–GluA2 interaction. Here, we use all-atom molecular dynamics simulations to reveal the atomic-level interaction pattern between the PICK1 PDZ domain and BIO124. Our simulations reveal three unique binding conformations of BIO124 in the PICK1 PDZ binding pocket, referred to here as state 0, state 1, and state 2. Each conformation is defined by a unique hydrogen bonding network and a unique pattern of hydrophobic interactions between BIO124 and the PICK1 PDZ domain. Interestingly, each conformation of BIO124 results in different dynamic changes to the PICK1 PDZ domain. Unlike states 1 and 2, state 0 induces dynamic coupling between BIO124 and the αA helix. Notably, this dynamic coupling with the αA helix is similar to what has been observed in other PDZ–ligand complexes. Our analysis indicates that the interactions formed between BIO124 and I35 may be the key to inducing dynamic coupling with the αA helix. Lastly, we suspect that the conformational shifts observed in our simulations may affect the stability and thus the overall effectiveness of BIO124. We propose that a physically larger inhibitor may be necessary to ensure sufficient interactions that permit stable binding between a drug and the PICK1 PDZ domain.

Let's teach Kibot: Discovering discussion patterns between student groups and two conversational agent designs

AbstractConversational agents can deepen reasoning and encourage students to build on others' knowledge in collaborative learning. Embedding agents in group work, however, presents challenges where groups may ignore the agents, and this calls for designs where students perceive agents as learning partners. This study examines group interactions with two text‐based agents (ie, chatbots) that posed as an expert and a less knowledgeable peer in a high school marine biology lesson. Student messages (N = 1764) from 18 groups (52 students ages 14–15) received codes for reasoning, building on prior ideas, and responsiveness to the agents. Results indicate no differences between agents in how often each discussion move occurred. Interestingly, sequential pattern mining suggests that the less‐knowledgeable‐peer agent prompted groups to show questioning and building on others' ideas, similar to how students may act as peer tutors to the agent. Meanwhile, sequences with the expert agent resembled student‐teacher exchange, where groups responded to the agent's nudges and then provided reasoning. Findings illustrate the affordances of embedding humanized features in technology designs to promote discussion. Practitioner notesWhat is already known about this topic Conversational agents can facilitate group discussions, but can get abused or ignored by student groups. To engage students, agent designs can simulate the characteristics of familiar classroom figures, such as peers or teachers. There are limited explorations of how student groups adapt their interactions to different agent designs in collaborative settings. What the paper adds Illustrations of the utilities of diversifying agent designs in collaborative learning. Insights into the unique interaction patterns that student groups displayed to different designs, such as questioning and building on others' ideas with a less‐knowledgeable‐peer agent. Implications for practice and/or policy Embedding signs of humanness in classroom facilitation and technology design can foster responsiveness among users. Pedagogy can consider adaptive designs to promote interaction sequences that contribute to learning (eg, questioning, expanding on prior ideas) at opportune moments.

Natural Compounds Inhibit SARS-CoV-2 nsp13 Unwinding and ATPase Enzyme Activities.

SARS-CoV-2 infection is still spreading worldwide, and new antiviral therapies are an urgent need to complement the approved vaccine preparations. SARS-CoV-2 nps13 helicase is a validated drug target participating in the viral replication complex and possessing two associated activities: RNA unwinding and 5′-triphosphatase. In the search of SARS-CoV-2 direct antiviral agents, we established biochemical assays for both SARS-CoV-2 nps13-associated enzyme activities and screened both in silico and in vitro a small in-house library of natural compounds. Myricetin, quercetin, kaempferol, and flavanone were found to inhibit the SARS-CoV-2 nps13 unwinding activity at nanomolar concentrations, while licoflavone C was shown to block both SARS-CoV-2 nps13 activities at micromolar concentrations. Mode of action studies showed that all compounds are nsp13 noncompetitive inhibitors versus ATP, while computational studies suggested that they can bind both nucleotide and 5′-RNA nsp13 binding sites, with licoflavone C showing a unique pattern of interaction with nsp13 amino acid residues. Overall, we report for the first time natural flavonoids as selective inhibitors of SARS-CoV-2 nps13 helicase with low micromolar activity.

Behavioral signatures of structured feature detection during courtship in Drosophila

Many animals detect other individuals effortlessly. In Drosophila, previous studies have examined sensory processing during social interactions using simple blobs as visual stimulation; however, whether and how flies extract higher-order features from conspecifics to guide behavior remains elusive. Arguing that this should be reflected in sensorimotor relations, we developed unbiased machine learning tools for natural behavior quantification and applied these tools, which may prove broadly useful, to study interacting pairs. By transforming motor patterns with female-centered reference frames, we established circling, where heading and traveling directions intersect, as a unique pattern of social interaction during courtship. We found circling to be highly visual, with males exhibiting view-tuned motor patterns. Interestingly, males select specific wing and leg actions based on the positions and motions of the females' heads and tails. Using system identification, we derived visuomotor transformation functions indicating history-dependent action selection, with distance predicting action initiation and angular position predicting wing choices and locomotion directions. Integration of vision with somatosensation further boosts these sensorimotor relations. Essentially comprised of orchestrated wing and leg maneuvers that are more variable in the light, circling induces mutually synchronized conspecific responses stronger than wing extension alone. Finally, we found that actions depend on integrating spatiotemporally structured features with goals. Altogether, we identified a series of sensorimotor relations during circling, implying that during courtship, flies detect complex spatiotemporally structured features of conspecifics, laying the foundation for a mechanistic understanding of conspecific recognition in Drosophila.

A Biometrics-Based Behavioral Trust Framework for Continuous Mobile Crowd Sensing Recruitment

The emergence of mobile crowd sensing (MCS) platforms makes it possible to collect data in a time and cost efficient manner. However, one of the challenges in MCS systems is obtaining reliable information especially in the presence of impersonators who could provide false reports without getting detected. User recruitment mechanisms adopted in MCS systems hire users such that the Quality of Information (QoI) of the submitted sensing reports is maximized. Despite that, there is still a risk of recruiting impersonators to the system. This problem is even more prominent in the case of continuous mobile sensing tasks, since multiple false sensing could be submitted by the impersonator during the sensing period, which can impact the quality of the sensing outcome and hence the performance of the system. Therefore, to ensure the reliability of the continuously submitted data, a more robust recruitment mechanism, which detects and eliminates impersonators during the sensing task, is needed. This work proposes a biometrics-based behavioral trust framework that can support a reliable recruitment process in continuous MCS tasks. Behavioral biometrics are unique behavioral traits that can be used to profile users based on how they naturally perform a specific activity. By leveraging machine learning techniques, these behavioral traits can be used in order to detect impersonators in the system. In this work, a unique model for each MCS worker is built based on their unique interaction patterns with the smartphone&#x2019;s touching screen. The proposed approach integrates the trained machine learning models with a dynamic continuous recruitment system, which continuously monitors the QoI of the submitted sensing reports and changes the recruited participants as needed. Simulation results of the proposed approach show its efficacy in detecting and eliminating impersonators in continuous sensing recruitment.