Aspergillus niger. Ochratoxinase-like enzymes from ochratoxin-producing fungi showed more favorable substrate-binding pockets to accommodate ochratoxins A and B. (4) Conclusions: filamentous fungi are an interesting and rich source of hydrolases potentially capable of degrading ochratoxins, and could be used for the detoxification of diverse food commodities.(1) Background: ochratoxins are mycotoxins produced by filamentous fungi with important implications in the food manufacturing industry due to their toxicity. Decontamination by specific ochratoxin-degrading enzymes has become an interesting alternative for the treatment of contaminated food commodities. (2) Methods: using a structure-based approach based on homology modeling, blind molecular docking of substrates and characterization of low-frequency protein motions, we performed a proteome mining in filamentous fungi to characterize new enzymes with potential ochratoxinase activity. (3) Results: the proteome mining results demonstrated the ubiquitous presence of fungal binuclear zinc-dependent amido-hydrolases with a high degree of structural homology to the already characterized ochratoxinase from  Aspergillus ochraceus . Ea. Ea26].  enzymes . ConsideA. niger ochratoxinase (UniprotKB reference: A2R2V4) in fungal proteomes were selected by database searching using the BLASTP algorithm [p-values and query coverage, considering only the non-redundant protein homologs with p-value < 10−15 and query coverage > 75%. The final list of selected fungal putative ochratoxinases comprised 83 elements.Sequence homologs of lgorithm . The hitThe atomic structure of the putative selected ochratoxinases was predicted by homology modeling and threading using the Phyre2 algorithm . The strAtomic models of ochratoxins A, B and C were manually built with Maestro software v.12.3 . Hydrogen atoms were automatically added following the riding model and the structures were optimized by 100 cycles of energy minimization. Molecular docking of ochratoxins A and B on the surface of the protein models was performed by applying a blind method with a previous cavity detection and flexible docking using CB-Dock and Autodock Vina . This meA. niger in apo-form and complexed with OTA was studied by molecular dynamics simulations with the GROMACS suite v. 5.1.2. using a full-atom model strategy and the CHARMM27 force field parameters [The structure and stability of the ochratoxinase enzyme from rameters . The toprameters . Brieflyrameters .A. niger ochratoxinase. Among all the available protocols for the characterization of protein–ligand interactions, we selected the molecular mechanics Poisson–Boltzmann surface area method (MM-PBSA), due to its efficiency and reliability in small- to medium-sized molecular systems [The molecular dynamics trajectories obtained in 2.3 were used to characterize and model the interactions between OTA and the  systems . Followi systems . Separat systems ,40.A. niger ochratoxinase complexed with OTA were analyzed by the g_mmpbsa package [The molecular dynamics trajectory files from  package  to deter package .The refined atomic models of putative ochratoxinase enzymes were superposed in 3D space by the Caretta software, a recent protein-structure-alignment algorithm that combines dynamic time warping together with progressive pairwise alignment to superpose multiple protein structures in a very efficient manner . The samA. niger ochratoxinase (PDB code: 4C5Y) by Consurf 2016 software [The degree of evolutionary conservation of the amino acids across ochratoxinase structure models was mapped over the coordinates of software .The electrostatic surface properties of protein models were determined by the Adaptive Poisson–Boltzmann Solver (APBS)  and reprLarge-scale protein movements were studied by low-frequency normal mode analysis using a C-alpha force field . This stAdditional graphical representations of atomic coordinates and their properties were performed with the Protein Imager software .A. niger (PDB code: 4C5Y) was used as a structural reference for the characterization of putative ochratoxinase-hydrolyzing enzymes in other fungi [A. niger ochratoxinase is a binuclear Zn-dependent hydrolase, containing a conserved arrangement of catalytic residues also present in other hydrolases and esterases [2+ ion is coordinated by two histidine (His111 and His113) and one aspartate (Asp378) residues, whereas the second Zn2+ has partial coordination ensured by His287 and His307. The remaining coordination position is used by the enzyme to stabilize the chemical intermediate prior to the hydrolysis of the substrate. Both Zn2+ ions are immobilized by a covalently modified acetyl-Lysine residue (Ac-Lys246), which is essential for the proper functionality of the enzyme.The already described catalytic center of the ochratoxinase from er fungi . A. nigesterases . The firTo characterize the complete substrate-binding pocket of ochratoxinase, we performed a blind docking experiment using OTA and OTB as ligands . Our resA. niger, we applied a classical molecular dynamics simulation in a controlled aqueous environment combined with molecular mechanics Poisson–Boltzmann surface area analysis (MM-PBSA). The obtained results are depicted in the Molecular docking protocols combined with molecular dynamics simulations have been extensively used in computer-aided drug design due to their flexibility and easy implementation; however, their use in the characterization of enzyme–substrate complexes is quantitatively less extended . To studA. niger ochratoxinase also induces a folding transition of the enzyme, represented by an increase in the average number of residues located in coiled regions and a decrease of the residues in beta-sheets and alpha-helices along the molecular dynamics simulation  and the BLASTp searching algorithm [p-value < 10−15 and query coverage > 75%). After the elimination of redundant entries, we built a dataset composed of 83 protein sequences. All the selected protein sequences were used as seeds for homology modeling with the automated algorithm implemented in Phyre2 [A. niger ochratoxinase in 77% of the sequences (PDB codes: 4C65 and 4C5Y), and two amido-hydrolases of unknown origin characterized from a metagenomic project in 23% of the cases (PDB codes: 3MKV and 3FEQ). The detailed results of the Phyre2 homology modeling process are included in The fungal proteomes deposited at the NCBI databases were scanned for ochratoxinase homologs by using the sequence of lgorithm . Since tn Phyre2 . FilteriAspergillus genus, whereas the third one also includes species of Penicillium and Byssochlamys genera. The structural conservation of residues in specific spatial positions was further analyzed by considering the results of the structure-based sequence alignment of the selected enzymes and using the ConSurf algorithm [A. niger ochratoxinase as a working reference, the normalized conservation score plot along its sequence showed the presence of different conserved subdomains in the analyzed enzymes, which comprise the catalytic site and the protein segment that connects both enzyme domains . The matrix represented in A. niger, a Zn-dependent amido-hydrolase that is able to degrade OTA and OTB [Bioremediation of ochratoxins from food commodities by the use of enzymes is an interesting alternative to the chemical or physical treatments that can alter their nutritional composition and organoleptic properties . Among a and OTB . However and OTB ,58.High-throughput screening and data mining strategies are ideal for the discovery of new ochratoxin-degrading enzymes. Due to the intrinsic conservation of the protease and amido-hydrolase family of enzymes in all living kingdoms, the sequence-based search for ochratoxin-hydrolyzing enzymes is limited by the abundance of sequence homologs in all the proteomes. In this work, we proposed the application of a structure-based mining protocol based on the generation of homology models to filter out the potential ochratoxin-degrading enzymes and to characterize their molecular features that could be related with their catalytic activities. Similar structural-based approaches have been recently applied for the screening of ochratoxin-binding domains and proteins in structural databases .A. niger [A. niger enzyme, using molecular models of OTA and OTB. Our model suggests that the configuration of the catalytic pocket resembles the observed in bacterial arginine amido-hydrolases, where the substrate-binding is ensured by complementary histidine residues different from those involved in the coordination of the Zn-binuclear center [A. niger is enriched in hydrophobic amino acids and contains two residues (His191 and Lys351) able to establish cation–pi interactions with ochratoxin [A. niger enzyme.Our starting point was the work by Dobritzsch and coworkers, where the authors described the only atomic structure of a Zn-dependent amido-hydrolase able to degrade ochratoxin and isolated from A. niger . Despiter center . In conthratoxin . Cation–hratoxin , as wellhratoxin . Our datA. niger ochratoxinase catalytic center by molecular dynamics allowed us to determine the substrate-binding characteristics of the enzyme. Our results are compatible with the existence of two different regions in the catalytic pocket of the enzyme that contributes to the stabilization of the enzyme–substrate complex and further hydrolysis (Additionally, the study of the short-range behavior of the drolysis . The resAspergillus or Penicillium genera [Using a combined approach that joins sequence homology with the construction of atomic models, we selected a dataset of 83 different fungal proteins with ochratoxinase-like characteristics. Structure superposition and phylogenetic analysis of the dataset of fungal proteins pointed to the possibility of the existence of distinct molecular characteristics that could differentiate the ochratoxinase-like enzymes belonging to the proteomes of ochratoxin-producers from other fungal enzymes . At thism genera . Howeverm genera . In thism genera . Interesm genera .Substrate recognition by enzymes is often driven by adaptable protein segments that can accommodate different chemical compounds . This enIn summary, our data suggested that ochratoxin-producing fungi are a potential source of ochratoxin-degrading enzymes that harbor enhanced catalytic properties to act over the mycotoxin, demonstrated by the configuration and properties of the catalytic center and the flexibility of the surrounding residues to accommodate the substrate. These enzymes are interesting starting points for the development of new enzyme-based bioremediation strategies.


Predicting stable polymorphs of molecular crystals remains one of the grand challenges of computational science. Current methods invoke approximations to electronic structure and statistical mechanics and thus fail to consistently reproduce the delicate balance of physical effects determining thermodynamic stability. We compute the rigorous ab initio Gibbs free energies for competing polymorphs of paradigmatic compounds, using machine learning to mitigate costs. The accurate description of electronic structure and full treatment of quantum statistical mechanics allow us to predict the experimentally observed phase behavior. This constitutes a key step toward the first-principles design of functional materials for applications from photovoltaics to pharmaceuticals. general molecular compounds, which simultaneously account for all effects, have hitherto not been computationally viable. Here we present an efficient “end to end” framework that seamlessly combines state-of-the art electronic structure calculations, machine-learning potentials, and advanced free energy methods to calculate ab initio Gibbs free energies for general organic molecular materials. The facile generation of machine-learning potentials for a diverse set of polymorphic compounds—benzene, glycine, and succinic acid—and predictions of thermodynamic stabilities in qualitative and quantitative agreement with experiments highlight that predictive thermodynamic studies of industrially relevant molecular materials are no longer a daunting task.Predictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differences are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations for  Molecular crystals are ubiquitous in the pharmaceutical industry  and showThe pharmaceutical industry therefore spends considerable resources on high-throughput crystallization experiments to screen for polymorphs , into whDespite the demonstrable value of CSP for many classes of materials –21, and Recent implementations of advanced path-integral (PI) approaches , 28 alloIn this work we present an efficient framework for ranking candidate structures of arbitrary compounds using rigorous ab initio Gibbs free energy calculations, based on the streamlined development of MLPs and their integration with PI methods. Our approach builds upon our previous work on combining PI approaches with MLPs for ice polymorphs , 33, butThe reliability and general applicability of our approach are showcased by the rapid development of MLPs and correct stability predictions for crystal polymorphs of three prototypical compounds: benzene, glycine, and succinic acid. These bear the hallmarks of more complex biomolecular systems—molecular flexibility, competing polymorphs, and intermolecular interactions ranging from weak dispersive to hydrogen bonded and ionic. Importantly, the relative stability of their polymorphs is well established –38. We fOur work complements state-of-the-art CSP methods, which efficiently survey structural space to extract small sets of promising candidate structures using ab initio calculations and/or MLPs , 35, butα-glycine   theory fThe subsequent training of the MLPs hinges on identifying the most important “features” of the configurations, fed to the MLPs as input. These features are usually abstract functions quantifying the local density of atoms and require the careful tuning of multiple parameters . Here, w48SI Appendix.In a third step, we exploit the orders-of-magnitude lower cost of the resultant MLPs compared to the ab initio reference method, to compute Gibbs free energies for much larger simulation supercells using QTI . We accoAs an exposé of the universal applicability of this scheme, we predict the relative stabilities of a set of prototypical systems, whose small number and size belie how representative they are of general organic molecular crystals: Benzene is the archetypal rigid, van der Waals bonded molecular crystal, while succinic acid represents general hydrogen-bonded systems, and glycine prototypes are flexible zwitter-ionic systems. This small, “irreducible” set of prototypical systems covers not only the three different types of bonding, but also the chemical space that includes pharmaceuticals such as aspirin and paracetamol. Moreover, molecular flexibility and the large-amplitude curvilinear motion of the amide group in glycine trigger the same pathologies of approximate free energy methods as more complex systems exhibiting free rotation of molecular units , 29 and α- and β-succinic acid .For each compound we compute the free energy differences between the stable ambient-pressure polymorph and its closest experimentally established competitor(s): We consider forms I and II of benzene  and α- anic acid  at 100 K-glycine  at 300 K-glycine , 55. Theβ- over α-succinic acid, the metastability of β-glycine, and the near degeneracy of α- and γ-glycine , which is in good agreement with the experimentally determined transition pressure of 1.5 GPa . Similarbehavior . While io 23 GPa .α form and thermal expansion differentiates the two polymorphs. In glycine NQEs and thermal expansion differently affect the stability of the α- and β-polymorphs with respect to the γ form, and neglecting any of the three effects would lead to large errors on the scale of the experimental free energy differences.By comparing rigorous free energies with estimates that exclude nuclear quantum effects (NQEs), anharmonicity, and cell expansion and flexibility, we are able to understand the extent to which these effects and their interplay contribute toward the stability of molecular crystals. Crucially, as shown in Meanwhile, the MLP-based stability predictions  energies on the basis of a single-point hybrid-functional DFT calculation for the structure relaxed using semilocal DFT . Thermalk-points . Although these MLPs have been trained on DFTB data as a proof of concept and consequently lack chemical accuracy, they remain robust and capture the molecular flexibility of these systems. Given that dynamic disorder, thermal expansion, conformational relaxation of the molecular units, and potential (dynamic) instabilities of candidate polymorphs are automatically accounted for within the QTI approach, we expect stability predictions to be very robust with respect to the nature of the candidate polymorphs and thus directly applicable to said pharmaceutical and blind-test systems.Our protocol easily extends to stability predictions for other complex molecular crystals, as its data-driven nature accelerates MLP development, irrespective of the material under consideration. As a proof of this, we have developed MLPs for polymorphs of three complex pharmaceuticals—aspirin, paracetamol, and XXIII, the most complicated system  from theIn applications involving large numbers of polymorphs or polymorphs with large unit cells, suitable sets of reference configurations can be generated based on configurations of liquid or amorphous states at different pressures . This exFinally, the empiricism involved in selecting the exchange-correlation functional and dispersion correction used in the DFT calculations can be removed by using PESs evaluated using beyond-DFT electronic structure theory. Crucially, our scheme extends naturally to predictions of Gibbs free energies based on quantum-chemical electronic structure methods , 69 suchp-T phase diagrams in a reliable and computationally efficient manner, paving the way for guiding experimental synthesis of such materials. The protocol and the scripts provided in SI Appendix permit its application practically out of the box. Determining the relative stability of generic polymorphic compounds is a recurrent problem across different domains of science and engineering—from nucleation theory to the practical design of pharmaceuticals—and we hope that the robust and easy-to-use nature of our end-to-end protocol will facilitate reliable, accurate free energy calculations beyond those of the computational chemistry community.In conclusion, marrying state-of-the-art electronic structure, free energy, and machine-learning methods in a widely applicable framework enables rigorous, predictive free energy calculations for complex (organic) molecular crystals at general thermodynamic conditions. The unprecedented accuracy of our approach sets the stage for future studies of kinetic effects as well as full We have constructed Behler–Parinello-type neural network potentials  for benzOur data are based on Langevin-thermostated PI NVT simulations at 300 K, performed using the i-Pi force engine  coupled 77To minimize the computational cost of the reference calculations the MLPs are composed of a baseline potential trained to reproduce energies and forces from more affordable PBE-DFT  calculatPBE0+MBD calculations were performed using FHI-aims –85 with β, accounting for anharmonic quantum nuclear motion and anisotropic cell fluctuations. The difference between the Gibbs and Helmholtz free energies computed from an MLP-based PI NPT simulation based on its average cell isV at external pressure β. A standard Kirkwood construction  volumes of polymorphs can directly be observed in PI NPT simulations. Meanwhile entropic differences can be computed fromSupplementary File


Do students experience a person-centred experiential (PCE) approach to learning in a university context differently to transmitted knowledge learning from prior education, and if so, how?The person-centred approach, as defined and developed by Carl Ransom Rogers, remains on the margins of practice in the UK. The approach sustains a non-medical stance. All of the Improving Access to Psychological Therapies Person Centred Experiential Counselling (APT PCEC) workforce require a qualification in person-centred experiential counselling. In order to attune to Roger's hypothesis regarding the conditions required in order to facilitate psychological growth, person-centred learning is a principle stance.Researching experiences of PCE learning through anonymous feedback from students attending different levels of training  License) is an initial test of the hypothesis .Counselling education in the UK is increasingly highly standardised and driven by competency frameworks. This work begins to uncover person-centred students’ evaluation of undertaking person-centred qualifications. Modules and continuing professional practice were constructed to facilitate a person-centred learning environment wherein the curriculum was designed by students or the experiential aspect of the learning drove the agendaThe sample was made up of (N = 30) students. 8 students were studying for a Master's degree in person-centred experiential psychotherapy, 10 students were studying a BA in humanistic psychotherapy, 12 students were attending a mandatory IAPT Continuous Professional Development (CPD) training in PCE therapy. The evaluation responses were subject to a thematic analysis.The emerging themes parallel each other and indicate that degree students were very aware of the difference from their previous learning experience in education.68% of MA Students experienced psychological maturation through the process of training.83% of BA students became more agentic in their approach to learning.83% IAPT therapists noticed the nurturing, compassion and humane approach to the learning, despite the mandatory nature of the offer and empowered them in regards to their non-medical stance within an NHS context.Our findings point to the significance and impact of person-centred learning for person-centred psychotherapists’ development during and post-qualification. Implications can be drawn in regards to engaging with person-centred learning in public sector and health contexts.Person centred approaches to learning hold a potential for a mature depth of understanding and engagement as opposed to the traditional ‘transmission of knowledge’ approach to learning.


Antibiotics are freqeuently used in the livestock sector in low- and middle-income countries for treatment, prophylaxis, and growth promotion. However, there is limited information into the zoonotic prevalence and dissemination patterns of antimicrobial resistance (AMR) within these environments. In this study we used pig farming in Thailand as a model to explore AMR; 156 pig farms were included, comprising of small-sized (<50 sows) and medium-sized (≥100 sows) farms, where bacterial isolates were selectively cultured from animal rectal and human fecal samples. Bacterial isolates were subjected to antimicrobial susceptibility testing (AST), and whole-genome sequencing. Our results indicate extensive zoonotic sharing of antibiotic resistance genes (ARGs) by horizontal gene transfer. Resistance to multiple antibiotics was observed with higher prevalence in medium-scale farms. Zoonotic transmission of colistin resistance in small-scale farms had a dissemination gradient from pigs to handlers to non-livestock contacts. We highly recommend reducing the antimicrobial use in animals’ feeds and medications, especially the last resort drug colistin. Escherichia coli (E. coli), isolated from livestock pigs (P), contact handlers (C), and non-livestock contact individuals (NC) in the farm’s proximity. To understand which antibiotic drugs pose AMR risks and assess zoonotic AMR transfer events, we utilized antimicrobial susceptibility testing (AST) and whole genome sequencing (WGS) methods in a One Health multi-factorial model consisting of different meat production intensities and subjects within the farm environment.Understanding zoonotic interactions is critical in controlling the plethora of infectious agents that affect our health as highlighted by COVID-19. One important zoonotic concern is the global challenge of antimicrobial resistance (AMR), which has been exacerbated by the overuse and misuse of antibiotic drugs both by the implementation in humans and domestic animals. It has been estimated that 60% of human pathogens are acquired from other animal species but quantification of transfer from humans to animals has been understudied and remain relatively unknown . TherefoTraditional antimicrobial usage (AMU) in the livestock sector aims to treat bacterial infections, provide prophylactic treatment to prevent infectious manifestations , and as E. coli from chicken and pig farms; isolates were resistant to several antibiotics: ampicillin , ciprofloxacin (73.3 and 21.1%), gentamicin (42.2 and 35.6%), and colistin (22.2 and 24.4%). Whilst ivestock .We wanted to implement the principles of One Health by performing a muliti-displinary study in a LMIC model country where AMR knowledge is often scarce due to limited resources and the few studies published are often limited in scope for potential risk factors that may be pivotal in reducing morbidity and mortality. Therefore, we used as Thailand is a LMIC livestock model to provide much needed evidence-based recommendations on AMU in LMICs in order to save the clinical effect of important antibiotics.This study was conducted in the Khon Kaen province of North-East Thailand during September-December 2018 in 166 pig farms. Farms were separated on production scale, all farms in this study were considered either small- or medium-scale farms. In this study small-scale farms were defined as having ≤50 sows (often family owned), whilst medium-scale farms were defined as having ≥100 and ≤500 sows and (company owned). In total there was 115 small-scale and 51 medium-scale farms. At each farm we tried to obtain a fecal samples from a contact human and a non-contact human and up-to 10 pigs (depending on the number of pigs on the farm) that were pooled together. Overall we collect 143 pig, 90 contact human and 54 non-contact human fecal sample and stored in accordance to the methods described by n ≤ 10) were pooled in 25 mL of buffered peptone water for enrichment, information regarding numbers of pooled pig rectal swabs from specific farms is provided in TM mSuperCARBATM, and CHROMagarTM COL-APSE . Different morphologic types of colonies were collected and identified by matrix-assisted laser desorption ionization time of flight . All isolates were re-streaked from frozen stocks onto blood-agar plates, incubated at 37°C 18 h and checked for potential contamination before AST. All AST were performed using the Sensititre EUVSEC AST microdilution plates using Sensititre Mueller Hinton Broth tubes and the Sensititre AIM pipetting robot . The AST was done in accordance to the Sensititre EU Surveillance Salmonella/E. coli EUVSEC Plate workflow . All processed samples had a minimum DNA concentration of 30 ng/μL verified using the dsDNA HS Assay kits on a Qubit Fluorometer  and OD260/280 in the range of 1.8–2.0 verified by the NanoDrop spectrophotometer , United States). Samples were shipped on dry ice to the Novogene sequencing facility  and sequenced on the Novoseq Illumina platform  and produced ∼5 GB of 150 bp pair-end sequencing reads per isolate.de novo assembly with Unicycler with minimum contigs of 400 bp produced . Informed consent for each human subject was obtained after an explanation of the experimental procedures. Pig samples were collected at the farm with the farm owners permission of the pig herd.PRJEB38313. All sequence data from computation workflow is compiled in Raw sequence data can be obtained from the European Nucleotide Archive (ENA) under the project accession number E. coli isolates we examined 156 farms in Khon Kaen province of North-East Thailand, utilizing our livestock and human participant One Health study model. A total of 143 pooled pig samples, 90 C and 54 NC human fecal samples and were collected and processed. We generated an isolate collection of 492 E. coli isolates derived from culturing fecal material on agar plates selective for ESBL-producing, carbapenemase-producing, or colistin-resistant gram-negative bacteria. From the 107 small-scale farms (SSF) 174 isolates were from livestock pigs (P), 99 isolates from livestock producers (C), and 57 isolates were from and non-livestock contact individuals (NC). From the 49 medium-scale farms (MSF), 94 isolates were from Ps, 44 isolates from Cs, and 24 isolates were from NCs. Several studies in farms in South-East Asia have looked at E. coli isolates from different livestock and farm workers  and known chromosomal mutations present. Within our data we saw higher diversity of ARGs in the SSFs compared to the MSFs , consistivestock . We did gyrA S83L . Then we looked for the most frequent ST in each group   . In the g media) . We alsoblaCTX-M-14 accession number AF25622; 43 other ESBL genes were also identified. Whilst the most common colistin resistance gene was mcr-1.1 accession number KP347127, whereas 12 other colistin resistance genes were also detected in our data set. The colistin ARG detection frequencies had the same zoonotic spill-over indication in SSFs as the AST data (P > C > NC), therefore providing a plausible molecular mechanism for the phenotypic observation seen. From the WGS data we in silco analyzed MLST of each isolate; in farms where the same MLST was detected in different sample groups, additional variant analysis was performed. We observed only one case of suspected clonality between human and pig isolates (same ST with less than 25 variants)  , indicatcarriage , but no E. coli isolates therefore follow-up studies could be warranted looking at the resistome in all these groups by metagenomic sequencing enabling a broader picture.Strengths of this study include providing a large isolate collection that has AST data coupled with extensive molecular data on sequence types, ARGs and clonal similarities between isolates. Furthermore, a One Health, multi-disciplinary approach was used, and concurrent samples from humans and animals were obtained. From our approach we could select isolates that exhibited the ESBL, colistin and carbapenem resistant phenotypes and detect any other AMR genotypic traits that can hitch-hike with these phenotypes by our WGS analysis. We observed both farm-scale difference where more MDR was detected in MSFs and XDR in the MSF NC cohort, and we also observed the zoonotic spill-over effect in the SSF from P to C to NC. Whilst limitations of the study include skewed data due to the antibiotic selection used to obtain isolates from the raw fecal samples and that pig samples were pooled whereas human samples were not. Additionally our study was limited to This study presents a snap shot of resistant isolates that contribute to ESBL and colistin-resistant phenotypes as well as ARGs that can hitch-hike with these AMR traits. Our AMR surveillance data highlights the AMR/AMU problem, whilst publications based on questionnaire and interviews revealed that farmers, human and animal health professionals and the general public have little awareness of the problem of AMR or knowledge to counter-act the AMR effects . EspeciaNDM–1bla and mcr resistance genes are prime examples, where their extenence on mobile genetic elements with other resistance can confer multiple-drug resistance that can easily be shared by a multitude of Gram-negative bacteria. Currently, we present the data of ARGs and known chromosomal mutations that are present in our isolate collection from the present-day versions of the databases available. However, on the discovery of novel ARGs or chromosomal mutations we encourage fellow researchers to use our data to see if these novel ARGs or mutations are also present in our study conducted in 2018. Furthermore, we would like to highlight the importance of combining interventions to reduce AMU with surveillance to reduce the resistance to antibiotic drugs such as colistin, ciprofloxacin and gentamicin and maintain the potency of drugs such as meropenem. This could be done by utilizing phenotypic AST microbroth plate testing and genotypic WGS analysis like our study or by traditional techniques such as AST disc diffusion testing and PCR used in other studies in Thailand  prolific multi-drug resistance that was more prevelent on medium-scale farms and (ii) likely zoonotic transmission of colistin resistance in small-scale farms as there was a dissemination gradient from pigs to handlers to non-livestock contacts. We highly recommend reducing the antimicrobial use in animals’ feeds and medications, especially the last resort drug colistin.Using our One Health multi-factorial model to compare PRJEB38313. All sequence data from computation workflow is compiled in Raw sequence data can be obtained from the European Nucleotide Archive (ENA) under the project accession number This study was performed in accordance to the Helsinki declaration for the human subjects and the EU Directive 2010/63/EU for animal experiments; the protocol involving human participants and animals was approved by the Khon Kaen University Ethics Committee . Informed consent for each human subject was obtained after an explanation of the experimental procedures. The patients/participants provided their written informed consent to participate in this study. Pig samples were collected at the farm with the farm owners permission of the pig herd. Written informed consent was obtained from the owners for the participation of their animals in this study.RH, TL, UM, MS, and JDJ designed the study. KL, JJ, SA, and MS collected the original samples. RH, TL, and KL did the Laboratory work. RH analyzed the data and prepared figures. RH and JDJ interpreted the data. RH wrote the manuscript with critical revisions by JDJ. All authors revised and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Regulatory T cells (Tregs) are key mediators of peripheral self-tolerance and alterations in their frequencies, stability, and function have been linked to autoimmunity. The antigen-specific induction of Tregs is a long-envisioned goal for the treatment of autoimmune diseases given reduced side effects compared to general immunosuppressive therapies. However, the translation of antigen-specific Treg inducing therapies for the treatment or prevention of autoimmune diseases into the clinic remains challenging. In this mini review, we will discuss promising results for antigen-specific Treg therapies in allergy and specific challenges for such therapies in autoimmune diseases, with a focus on type 1 diabetes (T1D). We will furthermore discuss opportunities for antigen-specific Treg therapies in T1D, including combinatorial strategies and tissue-specific Treg targeting. Specifically, we will highlight recent advances in miRNA-targeting as a means to foster Tregs in autoimmunity. Additionally, we will discuss advances and perspectives of computational strategies for the detailed analysis of tissue-specific Tregs on the single-cell level. Foxp3 gene. Tregs develop in the thymus, referring to thymic Tregs (tTregs), and harbor a TCR repertoire that is skewed towards self-antigens. Additionally, Tregs can likewise be induced in the periphery in an antigen-specific manner, so called peripheral Tregs (pTregs), with a TCR repertoire different from their tTreg counterparts . Outside of the thymus, peripheral tolerance is maintained by specialized cells, including so-called regulatory T cells (Tregs). Tregs are characterized by the high expression of the interleukin-2-receptor-aplpha chain CD25) and the transcription factor Foxp3, which is the master regulator of Tregs phenotype and function –4. The c and the terparts . Considece Tregs –11. Herevia direct inhibition of effector T cells with the same specificity but also via modulation of antigen-presenting cell (APCs), a process called bystander suppression, makes Tregs an important target for tolerizing therapies  6 and 12 months after the treatment started  which affects their migration and function, but they also vary in their activation state and functionality. This is especially evident in the divergent expression of CD45RA, with CD45RA- Tregs being antigen-experienced and having a higher suppressive activity .+ insulin-specific TFH cell frequencies to be increased during onset of islet autoimmunity, which was directly correlated with miRNA92a-3p expression. Importantly, miRNA92a-3p targets negative regulators of T cell activation  and thereby simultaneously reduces Treg induction. Hence, inhibition of miRNA92a-3p enhanced while a miRNA92a-3p mimic reduced Treg induction  to show, that miRNA142-3p directly targets the methylcytosine deoxygenase Tet2. Importantly, TET proteins catalyze the first step of DNA demethylation and can thereby impact the epigenetic landscape . We werein vivo in NOD mice with ongoing islet autoimmunity resulted in enhanced frequencies of Tregs accompanied by a reduction in the clinical disease score of the mice -residing Tregs by generating a mouse line transgenic for the TCR of an expanded VAT Treg clone . AdditioHowever, the identification of TCR sequences is only one side of the coin and a remaining bottleneck for T cell biology is the identification of the peptide-MHC ligands recognized by the identified TCRs. Here, recent advances have been made for experimental identification of epitopes recognized by orphan TCRs in a high-throughput screening of highly complex peptide-encoding oligo pools presented by bar-coded T cell-cytokine capturing APCs . AdditioSequence-based computational methods for TCR specificity analysis can be grouped into two categories: comparison and prediction. TCR comparison approaches impute antigen specificities by either allocating unknown TCRs to T-cell clusters or by assigning pairwise distance scores to TCR sequences with known antigen specificity. When several TCRs specific to the antigens of interest are known, these methods can be used to identify T cells with similar sequences likely to bind to the same antigen. The second category applies machine learning models to directly predict TCR binding to specific epitopes. Since these methods often additionally analyze the epitope sequence, they allow to predict specificity towards previously unknown antigens.TCR sequences with common epitope specificity carry statistically enriched motifs , 79. MetWhile comparison-based methods can serve as a proxy for determining TCR-specificity, such methods fail for novel epitopes without known corresponding TCRs. Machine learning methods can alleviate these issues by learning general rules that guide the T-cell epitope interaction. De Neuter et al. provided a proof of concept by predicting specificity towards one of two B*08 restricted HIV-1 epitopes based on the TCR CDR3β sequence . Jurtz eThese tools will potentially enable the identification of Tregs associated with disease-relevant antigens by predicting the specificity for large libraries of sequenced T cells. By limiting the number of candidates, for which specificity needs to be tested, the time and cost for identifying disease-relevant Tregs will be significantly reduced. However, due to different evaluation methodologies and different datasets, these methods often cannot directly be compared. Therefore, it remains yet to be determined, which model to choose, and to what degree computational tools can be already used for the development of targeted immunotherapies. It is apparent though, that the use of multi-omics techniques for the deep characterization of tissue-specific Tregs can critically contribute to the development and advancement of Treg-based immunotherapies. TCR transgenic Tregs migrate to the site of immune activation and therefore will facilitate the development of effective and safe therapies. Additionally, identification of surface markers specific to tissue-residing Tregs will enable targeted delivery of therapeutics, e.g., miRNA inhibitors or mimics, to foster Tregs specifically at the site of the autoimmune attack.While advances have been made for antigen-specific Treg inducing therapies e.g. to treat patients with severe peanut allergies, the success of such therapies in autoimmune T1D is still limited. A broad impairment in Treg induction in children during onset of islet autoimmunity highlights the necessity of combinatorial strategies to foster Tregs in order to open the window of opportunity for antigen-specific Treg therapies. miRNA-targeting offers the opportunity to improve Treg induction and stability in T1D. However new strategies to specifically modify miRNAs in specific cell types are needed. Identifying key signatures and characteristics of Tregs residing in the pancreas, the target organ of the disease, will be important to target therapies more specifically to those cells that are directly involved in the disease development and progression. Major advances in the use of single-cell multi-omics integration together with machine learning approaches for TCR specificity prediction have paved the way for a detailed description of individual cells from different tissues and will therefore help to bring antigen-specific Treg therapy to the next level.IS and FD reviewed the literature and wrote the manuscript. CD and BS reviewed the literature and contributed to the conceptualization of the manuscript. All authors contributed to the article and approved the submitted version.https://www.helmholtz-muenchen.de/fellows/index.html). IS is supported by a Research Grant of the Deutsche Forschungsgemeinschaft . FD is supported by the Helmholtz Association under the joint research school “Munich School for Data Science - MUDS”. CD holds a professorship grant from the Excellence Program for Outstanding Female Scientists from the Helmholtz Association, is supported by a Research Group at Helmholtz Zentrum München, the German Center for Diabetes Research (DZD), through a membership in the CRC1054 of the Deutsche Forschungsgemeinschaft (B11), and through an award of the EFSD/JDRF/Lilly Programme on Type 1 Diabetes Research 2020.BS acknowledges financial support by the Postdoctoral Fellowship Program of the Helmholtz Zentrum München (The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mindfulness meditation smartphone apps may improve mental health but lack evidence-based behavioral strategies to encourage their regular use for attaining mental health benefits. In October 2019, the Calm mindfulness meditation app introduced a mood check-in feature, but its effects on participation in meditation have yet to be tested.The objective of this study was to investigate how a mood check-in feature impacts meditation behavior in Calm app subscribers.This was a retrospective longitudinal analysis of mobile app usage data from a random sample of first-time subscribers to the Calm app (n=2600) who joined in summer 2018 or summer 2019. The mood check-in feature allows users to rate their mood using an emoji after completing a meditation session and displays a monthly calendar of their past mood check-ins. Regression analyses were used to compare the rate of change in meditation behavior before and after the introduction of mood check-ins and to estimate how usage of mood check-ins was associated with individuals’ future meditation behavior . Additional regression models examined the heterogenous effect of mood check-ins between subscribers who were active or inactive users prior to the introduction to mood check-ins . In order to confirm the specific associations between mood check-ins and meditation engagement, we modeled the direct relationship between the use of mood check-ins in previous weeks and subsequent meditation behavior .During the first 9 months of their subscription, the 2019 cohort completed an average of 0.482 more sessions per week (95% CI 0.309 to 0.655) than the 2018 cohort; however, across both cohorts, average weekly meditation declined . Controlled for trends in meditation before mood check-ins and aggregate differences between the 2018 and 2019 samples, the time trend in the number of weekly meditation sessions increased by 0.045 sessions among the 2019 cohort after the introduction of mood check-ins (95% CI 0.039 to 0.052). This increase in meditation was most pronounced among the inactive subscribers . When controlled for past-week meditation, use of mood check-ins during the previous week was positively associated with the likelihood of meditating the following week ; however, these associations were not sustained beyond 1 week.Using mood check-ins increases meditation participation in Calm app subscribers and may be especially beneficial for inactive subscribers. Mobile apps should consider incorporating mood check-ins to help better engage a wider range of users in app-based meditation, but more research is warranted. There are an estimated 20,000 mental health smartphone apps currently available for download . MindfulLack of engagement is a well-documented barrier confronting mobile app interventions. A recent systematic review and meta-analysis  reportedSelf-tracking app usage is a common component of existing mental health apps . In geneIn October 2019, the Calm mindfulness meditation app introduced a mood check-in feature that is based on constructs of social cognitive theory ,14. TherWe used an event-study design (or difference-in-differences model) to measure the change in meditation use after the introduction of mood check-ins among a random sample of users who had subscribed 3 months before mood check-ins were introduced, and compared that difference to the change in meditation use among a random sample of users who subscribed in the same month the year prior . We also used this framework to examine whether mood check-ins had a heterogeneous effect on meditation engagement across subscribers in our sample who were active and inactive users prior to the introduction of mood check-ins . To confirm that it was specifically mood check-ins that increased engagement, we modeled the direct relationship between use of mood check-ins during the previous weeks and subsequent meditation behavior . These findings may help to design future behavioral interventions for promoting mindfulness meditation app engagement and thus improving subscribers’ mental health outcomes.This was a retrospective longitudinal analysis of mobile app usage data and was approved by the Arizona State University institutional review board (STUDY 00011292). Consent for use of data was provided by subscribers who agreed to the Calm privacy policy, which states that user information may be used for research and shared with third parties in anonymous or aggregate form.Usage data were compiled from a random sample of 2600 first-time Calm app subscribers who joined in June or July 2018 (n=1300) or in June or July 2019 (n=1300). Subscribers were eligible for selection based only on the date of their initial paid subscription to the Calm app and were chosen using a randomization function in SQL. The Calm app introduced the mood check-in feature in October 2019; therefore, users who joined in 2018 had not been exposed to mood check-ins over the first 15 to 16 months of their subscription , whereas those who subscribed in 2019 were introduced to mood check-ins between 3 and 4 months after subscribing. No other major changes were introduced in the app during these time periods. Data for both groups were restricted to the first 9 months of app use to avoid analyzing usage patterns during the COVID-19 pandemic (February 2020) among the group that joined in 2019.The Calm app is a meditation app that is commercially available. Calm provides general guided meditation  grounded in mindfulness-based stress reduction  and VipaCalm recently added a mood check-in feature which allows participants to rate their mood using an emoji after completing a meditation session; users then receive preset feedback messages related to their mood selection  and a monthly calendar of past mood check-ins that is displayed after checking-in. This feature is consistent with behavioral strategies  thatweeks×2019 cohort×mood check-ins. All analyses were conducted using Stata software .App usage data  were aggregated by week over the first 9 months of users’ subscriptions. To estimate the overall effect of mood check-ins on meditation behavior over time, we used linear regression analyses. We compared the rate of change in meditation behavior measures in the 2019 cohort before and after the introduction of mood check-ins with those of the 2018 cohort during the corresponding time period in the previous year. We used a difference-in-differences approach, in which estimations of changes in meditation behavior were allowed to differ linearly for the periods before and after the mood check-in feature was introduced to determine the intent-to-treat effect. Outcomes were regressed on to a linear measure of weeks since starting to use the app, an indicator variable equal to 1 for the 2019 sample, and an indicator variable equal to 1 for the weeks after the mood check-in feature became available in 2019 , as well as all 2-way and 3-way interactions between the three variables. The model coefficient of interest was the estimated parameter for the change in weekly time trend after mood check-ins became available to the 2019 cohort—the variable To investigate whether the effects of mood check-ins were similar for active versus inactive users, we created separate models for active and inactive subscribers. Active and inactive status was defined by the number of weeks in which a subscriber completed at least 1 meditation session during the first 17 weeks of their subscription (corresponding to the period before mood check-ins were introduced in 2019). Users were classified as active subscribers if they were above the median number of weeks with any meditation within their cohort or as inactive subscribers if they were below the median number of meditation weeks threshold within their cohort. Thresholds were 2 and 3 weeks, respectively, for the 2018 and 2019 cohorts.To confirm the specific direct relationship between using mood check-ins and meditation engagement (treatment on the treated effect), we examined associations between an individual’s use of mood check-ins during the previous week and that person’s future meditation behavior by creating a second set of linear models regressing (1) the likelihood of any meditation during the week and (2) the weekly number of meditation sessions on the number of mood check-ins during the prior week . For these analyses, weekly app usage data were assessed for a 16-week period—8 weeks prior to and following the release of the mood check-in feature in October 2019. We created models for the entire 2019 cohort, active subscribers, and inactive subscribers. To control for differences in aggregate meditation behavior between subscribers who did and did not use the mood check-in feature, the models controlled for estimated meditation behavior during the prior week. To examine the duration of associations between using mood check-ins and future meditation behavior, we created models predicting subsequent weekly meditation using mood check-ins and meditation behavior measured over the previous 7 weeks.P<.001), meditated for an average of 0.587 more sessions per week (P<.001), and meditated for approximately 7.761 more minutes per week (P<.001). Similar patterns were observed among the active and inactive subscribers . The estimated effect of mood check-ins on weekly meditation sessions is attenuated but remains positive and statistically significant . SpecifiTo examine the association between using mood check-ins and future meditation behavior, we estimated the relationship between using mood check-ins during the prior week and the likelihood of any meditation during the subsequent week for the 8 weeks after mood check-ins were introduced. The results are presented in To examine the long-term relationship between past mood check-ins and subsequent meditation behavior, we estimated associations between 7 weeks of lagged mood check-ins and future meditation engagement  and that this effect was stronger for inactive subscribers than for active subscribers. Additionally, we found that use of mood check-ins specifically increased the likelihood of future meditation engagement, even when controlling for past meditation behavior and aggregate trends in meditation patterns over time. Use of mood check-ins during the previous week was positively associated with likelihood of meditating the following week; however, use of mood check-ins beyond 1 week prior was not predictive of future meditation.The 2019 cohort had an estimated cumulative increase in meditation of approximately 1 session (or 5 minutes), 8 weeks after the introduction of mood check-ins, compared to that of the 2018 cohort. Additionally, subscribers who used mood check-ins (compared to those who did not use mood check-ins) were more likely to meditate, completed more meditation sessions, and spent more time meditating on a given week. Importantly, these associations were strongest among previously inactive users. These data highlight the potential for mood check-ins to help subscribers participate in meditation after a period of low engagement. Existing literature suggests mood self-tracking helps app users increase their awareness of their mood patterns and, as such, helps with self-regulation and emotional well-being . AdditioThe effect of mood check-ins was greater for inactive subscribers  than it was for active subscribers . To our knowledge, there is no research evaluating app-based meditation behaviors and factors that influence continued engagement. According to the Transtheoretical Model and the Stages of Change, there is a distinction between people in the precontemplation or contemplation stages and those in the action or maintenance stages . Those iWe were not able to determine whether the benefits of mood check-ins come from individuals consciously deciding to meditate based on their mood or if this effect occurs on a subconscious level. If occurring subconsciously, the meditation behavior may be a byproduct of improvements in mental health that are associated with better health behaviors -23. AlteP<.001), but it is unclear whether small increases in meditation over an extended period of time constitute meaningful changes associated with clinically significant health benefits. Given the dearth of literature related to meditation dosage, future research is warranted to determine the frequency and duration of meditation optimally needed to convey benefits to participants and whether effects of this magnitude relate to future behavior and health outcomes.Although this is one of the first studies to assess the effects of mood check-in on participation in meditation, there are important limitations to be noted. First, we did not have any information about subscribers in the current sample beyond their subscription data and app usage patterns. Thus, we did not have information on potentially important covariates that may relate to app usage and meditation behavior and that may modify the effect of mood check-ins on meditation engagement. Future studies are encouraged to collect information on demographics , mental health, and other theoretically important subscriber characteristics to assess the possible confounding impacts of these variables on the relationship between mood check-ins and meditation engagement. Second, we did not assess how users engaged with other behavioral strategies already embedded within the Calm app ; therefore, it is unknown if mood check-ins operate alone or in tandem with these other features when promoting greater meditation behaviors. Future studies should evaluate the interaction of these behavioral strategies with mood check-ins and their combined effect on participation in app-based meditation. Also, this study analyzed changes in app usage during the first 9 months of users’ subscriptions with Calm, but these results may not be generalizable to potential changes in meditation behavior that would occur at other points in a user’s subscription. Finally, the cumulative effect of 8 weeks of mood check-ins was an increase of approximately 1 meditation session or 5 minutes of meditation. These findings were statistically significant (both Using mood check-ins increased meditation participation (in sessions or minutes) and the likelihood of meditation in general, having the greatest benefit for inactive meditators. Mobile apps aiming to encourage better health behaviors should incorporate evidence-based strategies such as mood check-ins to sustain behavior or increase meditation participation, but more research is warranted to examine mechanisms and confirm the findings presented herein. Mobile app developers may also utilize this information to better engage inactive subscribers in an effort to sustain their consumer base.


However, PSS scores were not significantly correlated with RSFC measures . These preliminary results support a relationship between perceived stress and GABAergic functioning in DLPFC, a core node of the CEN, an intrinsic network thought to underlie goal-directed attentional processes. Our findings extend previous work suggesting that functioning in the CEN is related to perceived stress and may inform treatment strategies to improve outcomes in stress-related conditions.Stress exposures and dysregulated responses to stress are implicated in psychiatric disorders of mood, anxiety, and cognition. Perceived stress, an individual's appraisal of experienced stress and ability for coping, relates to dysregulated functioning in resting state brain networks. Alterations in GABAergic function may underlie perceived stress-related functional dysregulation in resting state networks but this has not yet been explored. Therefore, the current study examined the association of perceived stress, via the Perceived Stress Scale (PSS), with prefrontal GABA levels and corresponding resting state functional connectivity (RSFC) alterations. Twelve women and five men, ages 35–61, participated. MR spectroscopy was used to measure brain GABA levels in the anterior cingulate cortex (ACC), left dorsolateral prefrontal cortex (DLPFC), and ventromedial prefrontal cortex (VMPFC). Resting state functional scans acquired at 3 Tesla were used to measure RSFC within and between the default mode (DMN), salience (SN), and central executive networks (CEN), hippocampus and amygdala. We observed significant negative correlations between total PSS scores and left DLPFC GABA levels ( Stress exposures are risk factors for psychiatric disorders including disorders of mood –3, anxieResting state functional magnetic resonance imaging (rfMRI) is a method that involves observing spontaneous brain activity in the absence of a task and has been translated to investigate group differences in brain functioning, develop biomarkers of disease , and ideOne potential mechanism underlying perceived stress and related functional dysregulation in resting state networks and limbic regions is altered GABAergic function. GABA is the primary inhibitory neurotransmitter in the brain and pre-clinical and clinical studies suggest that GABA modulates hypothalamic-pituitary-adrenal (HPA-axis) response to stress  which ha1H-MRS), a non-invasive method for detecting, identifying, and quantifying bulk GABA in the human brain, at 7T where increased spectral dispersion and signal to noise provide more accurate measurements  with the goal of serving as a hypothesis generating study for future research investigating stress and GABAergic functioning. We employed proton magnetic resonance spectroscopy  were recruited at Brigham and Women's Hospital through online advertisements targeted toward healthy participants and participants with mood disorder in order to enrich the sample for a range of emotion regulation in line with the NIMH Research Domain Criteria . InclusiAfter a phone screen, eligible participants were invited to participate in the study. Prior to the first study visit participants provided written informed consent. A psychiatrist or highly trained clinical MA-level coordinator administered the MINI to assess Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) psychiatric research diagnoses. The MADRS and YMRS were administered to assess current depressive and manic symptoms. Participants additionally completed the Hopkins Adult Reading Test  as an inParticipants completed the PSS . The PSS1H-MRS was performed on a clinical 7.0 Tesla MR scanner (Siemens Magnetom Terra) with a 32-channel receiver head coil. Prior to spectroscopy T1-weighted images were acquired with a 3D magnetization-prepared-rapid-gradient-echo (MPRAGE) sequence  and were used for localization of the voxel. Single-voxel MRS utilized a STEAM localization sequence  positioned in the dorsal anterior cingulate cortex , ventromedial prefrontal cortex , and left DLPFC (voxel size = 20 × 20 × 20 mm3) as shown in 1H single voxel spectroscopy spectral fitting using simulated spectra of 19 metabolites as a customized basis set. All spectra were quality controlled by visual inspection of the spectra and according to the following criteria: (1) a FWHM linewidth of the unsuppressed water spectrum <30Hz, (2) signal-to-noise ratio (SNR) <5, and (3) Cramer-Rao Lower Bound (CRLB) of NAA >5. Spectra that did not meet these criteria were re-processed with eddy current and frequency shift correction. If issues remained after re-processing, those spectra were eliminated from further analyses. FWHM, SNR, and CRLB are described in OpenMRSLab was utilized for pre-processing including coil combination, frequency and phase correction as well as eddy current correction and residual water removal . LCModel1H-MRS a second scan for rfMRI was performed on a clinical 3.0 Tesla MR scanner  with a 20-channel receiver head coil. Two participants did not complete this scan and were not included in related analyses. Whole-brain functional imaging was performed using a gradient-echo EPI pulse sequence with z-shim gradients to compensate for magnetic susceptibility-induced signal loss at the base of the brain (On the same day as the scan for he brain –46 with https://www.nitrc.org/projects/conn/) implemented through Matlab Version 2019b for processing structural and functional MRI data. We employed CONN's default preprocessing pipeline for volume-based analyses  which performs the following preprocessing steps via procedures in SPM12: functional realignment and unwarp; slice-timing correction; outlier identification; direct segmentation and normalization; and spatial smoothing. Specifically, functional data were coregistered and resampled to the first scan of each session using b-spline interpolation for the realignment and unwarping procedure. For slice-timing correction, functional data were time-shifted to correct for interleaved slice acquisition and resampled using sinc-interpolation to match the time in the middle of each acquisition time. Potential outlier scans were identified based on the observed global BOLD signal and the amount of subject-motion in the scanner using the “conservative” threshold settings in CONN of 0.5 mm for scan-to-scan frame-wise displacement and 3 s.d. away from the mean global brain activation across the entire volume for each timepoint. Functional and anatomical data were normalized into standard MNI space and segmented into gray matter, white matter, and CSF tissues classes with a resampling of functional data to 2 mm isotropic voxels and structural data to 1 mm isotropic voxels data using 4th order spline interpolation. Last, functional data were smoothed using a spatial convolution with a Gaussian kernel of 8 mm FWHM.We used the CONN Toolbox Version 19b  that includes noise components from cerebral white matter and cerebrospinal areas and includes other noise components of estimated subject-motion parameters (12 components), scrubbing of outlier scans (components vary per subject), and first order linear session effects. In addition, based on recommendations that higher frequencies contain informative resting state signals  and baset-tests were used to test relationships involving dichotomous variables. Given evidence of differential responses to stress by biological sex and age, we assessed these variables along with diagnosis, trauma exposure, symptom ratings, and handedness in relation to PSS scores.Relationships between continuous variables were tested using Pearson's correlations and We tested for correlations between continuous measures of GABA concentrations and total PSS scores using Pearson's correlations. In this hypothesis generating, preliminary analysis we did not correct for multiple testing. When a nominally significant correlation between PSS score and GABA was observed, the voxel was carried forward for functional connectivity analyses.Emergent nominally significant correlations between GABA and PSS scores were used to inform seed region selection for subsequent RSFC analyses. Masks of the MRS-determined voxel region were created for each subject and translated into MNI space for use as subject-specific seed ROIs in RSFC processing. We conducted seed-based correlations between the MRS-determined seed and network nodes in the DMN , CEN , and SN  and with limbic areas  using preconstructed ROIs from the Harvard-Oxford atlas in CONN. Network nodes were chosen based on predefined network assignment in CONN.t-test to determine ROI-to-ROI connectivity from the seed region to each of our pre-defined ROIs. Results were thresholded at an analysis-level false discovery (FDR) rate of p < 0.05 to correct for the total number of connections tested from the seed ROI. Bivariate correlation coefficients were calculated between the average time-courses for the seed to each ROI in our pre-defined set and converted to Fisher-transformed z-scores. Z-scores were extracted for significant RSFC results from the ROI-to-ROI analyses for use in primary analyses where separate correlation analyses were used to test the relationship of RSFC results with PSS scores and with cortical GABA. Results were deemed significant at a nominal threshold of p < 0.05.To examine functional connectivity between the seed region with each ROI we conducted ROI-to-ROI connectivity analyses which produce weighted connectivity matrices computed from a Weighted Least Squares (WLS) linear model to characterize functional connectivity strength. For second-level analyses CONN implements a General Linear Modeling (GLM) framework for testing between-subjects and between-conditions contrasts. We conducted a one-sample r = −0.61, p = 0.021) and mood disorder diagnosis  were significantly related to greater total PSS scores. None of the demographic or clinical variables tested were significantly associated with GABA levels (p > 0.054), but a trend was present for mood disorder diagnosis.In total, of the 17 original subjects, 3 did not complete the PSS, 1 reported falling asleep during the 3T scanning session and was removed from related analyses, and in 3 participants GABA measurements did not meet our quality standards and were removed from their respective analyses . Consequently, for comparisons involving perceived stress and GABA levels as well as comparisons involving perceived stress and RSFC there was complete data for 13 participants and for comparisons involving GABA levels and RSFC, 15 participants. In total 12 participants had complete data across all analyses. Demographic and clinical features of the participants are shown in r = −0.62, p = 0.023; r = −0.46, p = 0.086) and ACC GABA  were not significantly correlated with PSS scores , and a trend in negative association emerged between left DLPFC GABA and FC between the left DLPFC voxel and the left SMG, a node of the SN , dorsal ACC, and left insula make up a stress adaptation response network . RSFC stAlthough we found perceived stress to be related to GABA levels in the left DLPFC but not to RSFC with the left DLPFC voxel seed, we did observe a significant negative relationship between GABA levels in the left DLPFC and RSFC between the left DLPFC voxel and the left LPFC node of the CEN. The LPFC includes the DLPFC, the ventrolateral prefrontal cortex (VLPFC) and frontal eye fields  and GABAa priori atlas-defined ROI and thus do not report on whole-brain seed-to-voxel analyses that might have identified additional regions that are biologically relevant to perceived stress. Such analyses would be of interest in future studies in larger samples, as would formal statistical examination of GABA as a potential modifier or mediator of the relationship between perceived stress and RSFC together with the impact of diagnosis, age, sex, and biological measurements of HPA-axis activity, all of which were beyond the scope of this preliminary study.Findings of the current study should be evaluated with consideration of several limitations. The sample size in this study is small, limiting our ability to detect small effects and to test for effects of potential confounding, mediating and moderating variables. However, this preliminary study aimed to generate hypotheses to be tested in future studies and provides valuable initial data related to the role of GABAergic functioning in perceived stress that warrant further investigation. This study did not include biological measurements of HPA-axis activity or autonomic nervous system function response to stress. The collection of such measures would capture additional facets underlying stress and the perception of stress that may additionally be related to GABAergic functioning and RSFC. Given the large voxel sizes utilized in MRS, biologically important heterogeneity within each voxel may not be captured. For rsfMRI in this preliminary study, we implemented a high-pass filter only in the pre-processing of the data. Though others have recommended this decision the use of band-pass filtering instead is still debated in rsfMRI research. We therefore note that our analyses may be less generalizable to studies using band-pass filtering in the processing pipeline. In addition, we limited our analyses to In the present study, we conducted a preliminary investigation regarding the relationship of perceived stress, brain GABA levels, and RSFC in stress-related networks and limbic regions. Our preliminary results support an association between perceived stress and dysregulated GABAergic functioning in DLPFC, a core node of the CEN, an intrinsic network thought to underlie goal-directed attentional processes. This line of work has potential to shed light on GABAergic and other neurobiological mechanisms that may contribute to increased perception of stress experiences and coping which may in turn aid in the development of prevention and treatment strategies to improve outcomes in stress-related conditions.The original contributions presented in the study are included in the article/The study protocol was reviewed and approved by the Mass General Brigham Human Subjects Committee. The patients/participants provided their written informed consent to participate in this study.PM, AL, JT, and LH contributed to the study design. EC, WZ, and JB performed data analysis. HL and WZ contributed to data collection. All authors contributed to manuscript preparation.This work was supported by grants from the Brigham and Women's Hospital Connors Center for Women's Health and Gender Biology WHISPR Pilot Award (PM) and from the National Institutes of Health R01MH110979 (PM), U54AG062322 (PM) and T32MH017119 (JB).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Fuelled by deep learning, DeepRefiner offers the ability to leverage cutting-edge deep neural network architectures which can be calibrated for on-demand selection of adventurous or conservative refinement modes targeted at degree or consistency of refinement. The method has been extensively tested in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments under the group name ‘Bhattacharya-Server’ and was officially ranked as the No. 2 refinement server in CASP13  and No. 2 refinement server in CASP14 . The DeepRefiner web interface offers a number of convenient features, including (i) fully customizable refinement job submission and validation; (ii) automated job status update, tracking, and notifications; (ii) interactive and interpretable web-based results retrieval with quantitative and visual analysis and (iv) extensive help information on job submission and results interpretation via web-based tutorial and help tooltips.The DeepRefiner webserver, freely available at   Deep learning has transformed protein structure prediction. Recent editions of the Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments have witnessed a major breakthrough in accurately predicting the structure of a protein from sequence information through the application of advanced deep neural network architectures . Howeverhttp://watson.cse.eng.auburn.edu/DeepRefiner/.Here, we present DeepRefiner, an interactive and fully configurable webserver for high-accuracy protein structure refinement by deep network calibration. DeepRefiner first estimates residue-level errors from a starting structure using an ensemble of advanced deep neural network architectures and subsequently minimizes the cumulative error through energy-minimization-based restrained relaxation, leading to five refined structures. The advanced error estimation module in DeepRefiner employs a high-resolution version of our successful application of very deep and fully convolutional residual neural networks  for distFigure The front-end module of the webserver provides a web-based job submission interface where the user needs to provide only a starting structure in PDB format and a job name as mandatory inputs. The interface offers a wide range of options for customizing a refinement job including the ability to select deep network architectures, calibrate the refinement modes for on-demand selection of adventurous or conservative refinement targeted at degree or consistency of refinement, perform comprehensive post-refinement analysis, and protect job privacy. Two deep neural network architectures for residue-level error estimation are available, both independently supporting on-demand selection of adventurous or conservative refinement modes by calibrating the model ensemble. The submitted refinement job is then dynamically validated for consistency and passed on to the back-end via Common Gateway Interface (CGI).The back-end of the webserver consists of three sequentially interdependent layers including the webserver layer, queuing layer, and computing layer. The webserver layer directly interacts with the front-end and maintains job statistics and status using a MySQL database. The queuing layer performs job scheduling by implementing a first-in-first-out (FIFO) job queue. Additionally, it continuously interacts with the webserver layer to dynamically update job status and subsequently communicates with the front-end through CGI. Once the queuing layer releases a job for execution, the job starts running in the computing layer. The computing layer executes the job based on the supplied job parameters by first employing the selected deep neural network architecture for estimating the residue-level errors from the starting structure and then performing either adventurous or conservative refinement through energy-minimization-based restrained relaxation by calibrating the chosen model ensemble to generate five refined structures. After completion of the refinement job, the webserver performs comprehensive post-refinement analysis on the refined structures by estimating their global and local qualities; evaluating and reporting the scores of several knowledge-based statistical potentials including GOAP, OPUS-PSP, DFIRE, and RWPlus ; perform). The ResNet classifiers represent a high-resolution version of our successful application of distance-based protein model quality estimation  ,20 and dtimation , while sα atom of the starting structure in the form of Rosetta Coordinate Constraint with FLAT_HARMONIC function at 0.5, 1, 2 and 4Å thresholds in conjunction with the REF15 scoring function of Rosetta . ‘Bhattacharya-Server’ was officially ranked No. 2 among all other refinement server groups in both CASP13  and CASP14 . We report the per-target Z-score in the supplementary information (see Z-score). We further analyze the degree of structural refinement attained by ‘Bhattacharya-Server’ for CASP13 and CASP14 refinement targets in terms of various accuracy measures including GDT-HA, GDC-sc, and MolProbity scores with respect to length and accuracy of the starting structures (in terms of GDT-HA) considering the best submission. The results demonstrate that most promising refinement cases are generally observed for smaller targets having length less than 100 residues and those in the medium range of starting accuracies having starting GDT-HA scores between 40 and 60 units (see The refinement protocol employed in DeepRefiner has been extensively tested in the refinement category of CASP13 and CASP14 in a strictly blind manner under the group name ‘Bhattacharya-Server’ and was ranked highly among all refinement servers. Table nits see . The DeeIn Figure The server runs on a Linux cluster of 2.20-GHz Intel Xeon E5-2698 v4 20-core processors. The web application uses the PHP scripting language, JavaScript programming language and MySQL database. WebGL-based molecular visualization package 3Dmol.js  is used α-RMSD. Structural agreement between the starting structure and the refined structures in terms of secondary structure and solvent accessibility is shown in a visually interpretable manner. Estimated local quality containing the residue-level error estimates are visualized in a graphical format for the identification of potentially unreliable local regions. The full set of results, including the refined structure and text files containing the refinement analysis, can be downloaded as a compressed zipped archive.The mandatory inputs are a job name and a starting structure for refinement in PDB format. The customizable DeepRefiner interface provides users the ability to fully configure various optional job parameters including deep learning model, refinement mode, post-refinement analysis, and job privacy. An optional email address can also be provided for automated status update of the refinement job via email. The number of residues in the starting structure is limited to 500 for computational efficiency. The average run time is several hours after the job enters the running state. Five refined structures ranked based on the estimated global qualities, MolProbity scores, and various statistical potentials are visualized in the interactive web interface and are downloadable in the PDB format. Information on structural comparison between the starting structure and the refined structures is provided in terms of GDC-sc, GDT-HA, GDT-TS and CDeepRefiner presents a publicly available webserver for accurate and efficient protein structure refinement. DeepRefiner leverages cutting-edge deep neural network architectures that can be calibrated for on-demand selection of adventurous or conservative refinement modes targeted at degree or consistency of refinement. The method was successful in blind refinement experiments in CASP13 and CASP14. DeepRefiner offers an interactive and versatile web interface for the submission, monitoring, results retrieval, and analysis of refinement jobs in order to drive a moderately accurate starting structure towards the experimental quality. We may further improve the accuracy of our method in particular and structure refinement in general by exploring newer deep learning models to guide refinement that can be autonomously calibrated based on the quality of the starting structure and by directly outputting Cartesian coordinates of the refined models without the need for energy-minimization-based restrained relaxation. More generally, integrated sampling and scoring from a unified deep architecture shall further improve protein structure refinement.http://watson.cse.eng.auburn.edu/DeepRefiner/.DeepRefiner webserver is freely available at gkab361_Supplemental_FileClick here for additional data file.


Dear Editor, through human cells or tissue transplantation is still under investigation. SARS-CoV-2has been detected in tears and conjunctival secretions suggesting that transplanted oculartissues may carry the risk of donor-recipient transmission. As a result, stringent regulations have been issued to reduce potential contaminationfrom donors leading to a significant decrease in the corneal procurement and distribution,which is also observed due to pandemic related cessation of elective surgeries. We report theeffect of COVID-19 pandemic on the rate of donation and distribution of corneal tissuesobserved at Fondazione Banca degli Occhi del Veneto , one of the largesteye banks in Europe. A two-tailed non-parametric Mann–Whitney test with 95% CI was used tocheck the statistical difference between the tissues procured and distributed fortransplantation in 2019 and 2020. Retrospective data collected from FBOV database during thelockdown period (between 9th March and 8th May 2020) showed a significant decline in thenumber of tissues procured  and distributed fortransplantation . However, during the first week afterthe lockdown was eased (11th–14th May), we observed that the donation rate did not improvesignificantly  but the tissues requested fortransplantation inclined  soon after the elective surgerieswere partially resumed.The potential for transmission of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the Global Alliance of Eye Bank Associations (GAEBA) were released with the aim to largely exclude donor tissues positive for, or in recentclose contact with, COVID-19 patient or deceased. In Italy, indications from the Ministry ofHealth required to perform nasopharyngeal swabs on the donors and to obtain a confirmednegative result for SARS-CoV-2 before the tissue is released for transplantation, which hasnow become mandatory for all the donors. The implementation of these guidelines has reducedthe probability of inadvertent distribution of corneas from asymptomatic donors, which hasemerged as a significant feature of COVID-19. In FBOV, of the 301 nasopharyngeal swabs performed (donors with the above inclusioncriteria), only three donors were confirmed as SARS-CoV-2 positive. It is conceivable howeverthat false negative rate encountered with PCR testing could lead to an underestimated value oftruly affected donors. A key issue of COVID-19 pandemic is the cancellation of all electivesurgeries. This is in part due to hospital reorganisation and redeployment of the staff fromdifferent specialties recruited to help intensive care units. Furthermore, non-imminentsight-threating surgery has been postponed to avoid ‘at-risk’ cohort of patients typicallybeing elderly. Strict regulations, higher maintenance costs and cancellation of electivesurgeries have significantly challenged the eye banks. Although COVID-19 cases are flatteningover time and the lockdown is partially eased, a further delay is expected before most centresare fully operational. The safety measures implemented by the eye banks will not be relaxed inthe foreseeable future, with testing for SARS-CoV-2 becoming part of routine screeningpractices together with HIV and HCV. Moreover, approximately 4% of donors died fromrespiratory related complications (FBOV data) in 2019; a group that will now have to becarefully scrutinised.Following the outbreak of the COVID-19 pandemic, guidelines from the societies concerning eyebanks such as the Eye Bank Association of America and because of the pandemic this demand is expected to rise significantly which maybecome a serious threat to carrying out sight-saving surgery in the near future. To ease theimmediate pressure on the number of available tissues, it would be reasonable to utilise asingle tissue for multiple transplants either as anterior/posterior lamellar grafts or bydividing a single layer into multiple grafts. In addition, research on whether SARS-CoV-2 can be completely eliminated from an exvivo donor cornea will be extremely necessary in order to recover the currently reduced supplyand distribution of donor tissues for corneal transplants.An estimated 12.7 million patients are waiting for corneal transplants worldwide


Epilepsy is caused by abnormal electrical discharges  in a specific part of the brain . Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source–space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations. Epilepsy is a neurological disorder that is predominantly characterized by a tendency for the recurrent and unpredictable hypersynchronous neuronal activity that interrupts the normal brain function  currents in the pyramidal neurons of the cerebral cortex  dipole modeling, where the position and amplitude of one to a few equivalent current dipoles (ECDs) are estimated over relatively short time windows   has proposed HFOs as a promising biomarker of localization of the EZ  can be divided into five types: delta band (0.1–4 Hz), theta band (4–8 Hz), alpha band (8–12 Hz), beta band (12–30 Hz), and gamma band (30–80 Hz), which are referred to as “low-frequency signals” relative to HFOs  patients is stronger than that of healthy controls  is one of the most common epilepsy syndromes and is considered a brain network disorder with predominantly frontal  or eyes open (EO) during the resting-state MEG scan, it is important to know whether the choice of keeping EC or EO affects the resting-state functional connectivity. Previous studies have demonstrated significant differences in the resting-state connectivity patterns between EC conditions and EO conditions using fMRI (Yan et al., These significant differences in the resting-state functional connectivity between the EO state and the EC state may support the hypothesis of Marx et al.  on mentaEpilepsy has been widely considered an archetypical brain network disorder that is commonly investigated using the resting-state functional connectivity. With its advantages of high temporal resolution and spatial resolution, MEG has been increasingly used to investigate the resting-state functional connectivity in epileptic presurgical evaluation. In this paper, the different metrics of abnormal functional connectivity are reviewed based on low-frequency signals and high-frequency oscillations. The application of MEG combined with iEEG approaches enhances the characterization of functional connectivity in the resting-state network, which provides complementary information for improving epilepsy surgery. More importantly, the resting-state functional connectivity based on MEG signals represents a potential predictor of postsurgical seizure outcome. Considering the resting-state connectivity differences between eyes-open conditions and eyes-closed conditions, an approach performing analysis using data from both states would be added to obtain more information about this area of uncertainty. The findings reviewed here provide grounds that the resting-state functional connectivity derived from MEG signals provides a strong contribution to a presurgical evaluation in epilepsy.NX, WS, JQ, JW, and QW all contributed to the writing and revision of this manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The performance of nanoelectronic and molecular electronic devices relies strongly on the employed functional units and their addressability, which is often a matter of appropriate interfaces and device design. Here, we compare two promising designs to build solid-state electronic devices utilizing the same functional unit. Optically addressable Ru-terpyridine complexes were incorporated in supramolecular wires or employed as ligands of gold nanoparticles and contacted by nanoelectrodes. The resulting small-area nanodevices were thoroughly electrically characterized as a function of temperature and light exposure. Differences in the resulting device conductance could be attributed to the device design and the respective transport mechanism, that is, thermally activated hopping conduction in the case of Ru-terpyridine wire devices or sequential tunneling in nanoparticle-based devices. Furthermore, the conductance switching of nanoparticle-based devices upon 530 nm irradiation was attributed to plasmon-induced metal-to-ligand charge transfer in the Ru-terpyridine complexes used as switching ligands. Finally, our results reveal a superior device performance of nanoparticle-based devices compared to molecular wire devices based on Ru-terpyridine complexes as functional units. The mol2(DMSO)MPTP-SAc in acetic acid solution using the protocol given recently -complex in the applied electric field.The most likely explanation for the small current increase due to the illumination of Ru(MPTP)ribed in –42. This, exists . The tra 500 meV , which i2–AuNP devices demands the adjustment of an effective equilibrium between light intensity, local surface plasmons of the AuNP, fraction of Ru(MPTP)2-complexes in the ground state, charge carrier density, density of trap states on the AuNP cores or TP ligands and the applied electric field. The resulting steady-state current depends on this equilibrium. The difference between the current under light illumination and the dark current, ΔIL_D, corresponds to the current enabled by plasmon-induced energy transfer to the Ru(MPTP)2-complexes and charge separation in an electrical field. This finding is in analogy to the plasmon-induced isomerization found in azobenzene derivatives used as ligands in nanoparticle arrays [IL_D is assumed to be hopping conduction since the charge carriers are mainly located in the ligand shell of the AuNP. Furthermore, the activation energy for this process as well as the current range correspond well to the above described hopping transport through Ru(TP)2-complex wire devices. Thus, ΔIL_D can only contribute a relatively small amount to the total current across the Ru(MPTP)2–AuNP device and consequently leads to a low on/off ratio.This supposed mechanism for current increase in Ru(MPTP)e arrays . The conIL_D, a straightforward way to improve the on/off ratio of Ru(MPTP)2–AuNP devices would be to increase the distance between adjacent AuNPs in the electrode gap, for example by employing longer ligand molecules. This will cause a decrease of the tunneling current contribution through the device, that is, an increase of the ΔIL_D contribution to the total current, and thus an increase of the on/off ratio. These insights indicate that Ru(TP)2-complex wires are not so useful to bridge nanometer-sized gaps (<8 nm) between electrodes as single or few Ru(MPTP)2–AuNP building blocks in order to build small-area functional devices. This is caused by their extremely low conductance, the missing antennae effect of AuNPs in optical devices and, in consequence, the absence of relevant switching currents as response to an electrical or optical trigger. Moreover, the possibilities to tune Ru(TP)2–AuNP devices are more versatile.Since it will be difficult to increase Δ2-complexes and AuNPs integrated in CMOS-compatible devices are useful switching elements that can be addressed by optical means. Furthermore, we could show that the device performance is sensibly determined by the molecule functionality and depends strongly upon the chosen device design, which can be used to define the transport mechanism. The use of long Ru(TP)2-complex wires bridging 8 to 20 nm gaps between nanoelectrodes results in hopping conduction with a low transport efficiency and the loss of significant switching ability. In a readily accessible approach using a few Ru(TP)2–AuNP building blocks with a diameter of 14 nm to bridge nanoelectrode gaps of 20 to 50 nm a light-driven conductance switching was measured. The underlying mechanism leading to an increase in current as response to light irradiation was identified as plasmon-induced metal-to-ligand charge transfer in Ru(TP)2 complexes and subsequent charge separation in the applied electrical field. Compared to Ru(TP)2-complex nanowire devices the performance of Ru(TP)2–AuNP devices is superior due to (I) the conductance mechanism based on sequential tunneling leading to a higher current, (II) the optical antennae effect of AuNPs, which allows for an energy transfer to the functional molecules, and (III) the ease and versatility of adopting the properties of the constituting elements according to the requirements of the respective devices. Our results reveal that the intrinsic properties of Ru(TP)2 complexes can be well preserved in Ru(TP)2–AuNP devices. We are convinced that this kind of devices based on functionalized AuNPs reveals the potential for the application in nanoelectronics or as sensors and we hope that our results inspire further developments in this field.Overall, we demonstrated that hybrid materials from Ru(TP)2-complex wire growth, activation energies of Ru(MPTP)2–AuNP devices, a scheme for sequential tunneling in Ru(MPTP)2–AuNP devices, reference measurements and additional data of optically addressed devices.The Supporting Information includes the schemes of the chemical compounds used, SEM images of nanogaps and nanoparticles, XPS analysis performed during Ru(TP)File 1Additional experimental data.