Separate Domains Research Articles

Unsupervised Domain Adaptative Temporal Sentence Localization with Mutual Information Maximization

Temporal sentence localization (TSL) aims to localize a target segment in a video according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on abundant yet expensive manual annotations for training. Moreover, these trained data-dependent models usually can not generalize well to unseen scenarios because of the inherent domain shift. To facilitate this issue, in this paper, we target another more practical but challenging setting: unsupervised domain adaptative temporal sentence localization (UDA-TSL), which explores whether the localization knowledge can be transferred from a fully-annotated data domain (source domain) to a new unannotated data domain (target domain). Particularly, we propose an effective and novel baseline for UDA-TSL to bridge the multi-modal gap across different domains and learn the potential correspondence between the video-query pairs in target domain. We first develop separate modality-specific domain adaptation modules to smoothly balance the minimization of the domain shifts in cross-dataset video and query domains. Then, to fully exploit the semantic correspondence of both modalities in target domain for unsupervised localization, we devise a mutual information learning module to adaptively align the video-query pairs which are more likely to be relevant in target domain, leading to more truly aligned target pairs and ensuring the discriminability of target features. In this way, our model can learn domain-invariant and semantic-aligned cross-modal representations. Three sets of migration experiments show that our model achieves competitive performance compared to existing methods.

Open-Set Graph Domain Adaptation via Separate Domain Alignment

Domain adaptation has become an attractive learning paradigm, as it can leverage source domains with rich labels to deal with classification tasks in an unlabeled target domain. A few recent studies develop domain adaptation approaches for graph-structured data. In the case of node classification task, current domain adaptation methods only focus on the closed-set setting, where source and target domains share the same label space. A more practical assumption is that the target domain may contain new classes that are not included in the source domain. Therefore, in this paper, we introduce a novel and challenging problem for graphs, i.e., open-set domain adaptive node classification, and propose a new approach to solve it. Specifically, we develop an algorithm for efficient knowledge transfer from a labeled source graph to an unlabeled target graph under a separate domain alignment (SDA) strategy, in order to learn discriminative feature representations for the target graph. Our goal is to not only correctly classify target nodes into the known classes, but also classify unseen types of nodes into an unknown class. Experimental results on real-world datasets show that our method outperforms existing methods on graph domain adaptation.

Bacterial Chaperone Domain Insertions Convert Human FKBP12 into an Excellent Protein-Folding Catalyst-A Structural and Functional Analysis.

Many folding enzymes use separate domains for the binding of substrate proteins and for the catalysis of slow folding reactions such as prolyl isomerization. FKBP12 is a small prolyl isomerase without a chaperone domain. Its folding activity is low, but it could be increased by inserting the chaperone domain from the homolog SlyD of E. coli near the prolyl isomerase active site. We inserted two other chaperone domains into human FKBP12: the chaperone domain of SlpA from E. coli, and the chaperone domain of SlyD from Thermococcus sp. Both stabilized FKBP12 and greatly increased its folding activity. The insertion of these chaperone domains had no influence on the FKBP12 and the chaperone domain structure, as revealed by two crystal structures of the chimeric proteins. The relative domain orientations differ in the two crystal structures, presumably representing snapshots of a more open and a more closed conformation. Together with crystal structures from SlyD-like proteins, they suggest a path for how substrate proteins might be transferred from the chaperone domain to the prolyl isomerase domain.

The link between the early surgery-induced inflammatory response and postoperative cognitive dysfunction in older patients.

Postoperative cognitive dysfunction (POCD) is a common complication in older patients with cancer and is associated with decreased quality of life and increased disability and mortality rates. Systemic inflammation resulting in neuroinflammation is considered important in the pathogenesis of POCD. The aim of this study was to explore the association between the early surgery-induced inflammatory response and POCD within 3 months after surgery in older cancer patients. Patients ≥65 years in need of surgery for a solid tumor were included in a prospective cohort study. Plasma levels of C-reactive protein (CRP), interleukin-1 beta (IL-1β), IL-6, IL-10, and Neutrophil gelatinase-associated lipocalin (NGAL) were measured perioperatively. Cognitive performance was assessed preoperatively and 3 months after surgery. POCD was defined as a decline in cognitive test scores of ≥25% on ≥2 of five tests within the different cognitive domains of memory, executive functioning, and information processing speed. Logistic regression analysis was performed. POCD was observed in 44 (17.7%) of 248 included patients. Age >75, preoperative Mini-Mental State Examination (MMSE) score ≤26 and major surgery were independent significant predictors for POCD. In multivariate logistic regression analysis, no significant associations were shown between the early surgery-induced inflammatory response and either POCD or decline within the different cognitive domains. This study shows that one out of six older patients with cancer developed POCD within 3 months after surgery. The early surgery-induced inflammatory response was neither associated with POCD, nor with decline in the separate cognitive domains. Further research is necessary for better understanding of the complex etiology of POCD.

The evolution of the demixing morphology in quasi 2D water-lutidine mixtures: a fractal dimension analysis

In this work, the evolution of the morphological properties of the liquid-liquid phase separation process of water-lutidine mixtures in a quasi-2D geometry locally heated by laser absorption is studied at different heating rates by videomicroscopy and complemented by a finite element simulation. Morphological changes of the separated domains are characterised using the fractal dimension and the distribution of domain sizes, capturing the main steps of the process: spinodal decomposition derived from the fluctuations in density at the critical point, separation into a bicontinuous structure, breaking of the structures producing a wide distribution of sizes, and the subsequent growth and coalescence of the demixed regions (domains) to produce a complete separation. A comparison with the simplest case of typical nucleation is made by performing the same analysis at a concentration where critical fluctuations are not present. We conclude that the methodology presented here can be used to characterise the main steps of the phase separation process and distinguish between spinodal decomposition and nucleation.

Insights into Molecular Diversity within the FUS/EWS/TAF15 Protein Family: Unraveling Phase Separation of the N-Terminal Low-Complexity Domain from RNA-Binding Protein EWS.

The FET protein family, comprising FUS, EWS, and TAF15, plays crucial roles in mRNA maturation, transcriptional regulation, and DNA damage response. Clinically, they are linked to Ewing family tumors and neurodegenerative diseases such as amyotrophic lateral sclerosis. The fusion protein EWS::FLI1, the causative mutation of Ewing sarcoma, arises from a genomic translocation that fuses a portion of the low-complexity domain (LCD) of EWS (EWSLCD) with the DNA binding domain of the ETS transcription factor FLI1. This fusion protein modifies transcriptional programs and disrupts native EWS functions, such as splicing. The exact role of the intrinsically disordered EWSLCD remains a topic of active investigation, but its ability to phase separate and form biomolecular condensates is believed to be central to EWS::FLI1's oncogenic properties. Here, we used paramagnetic relaxation enhancement NMR, microscopy, and all-atom molecular dynamics (MD) simulations to better understand the self-association and phase separation tendencies of the EWSLCD. Our NMR data and mutational analysis suggest that a higher density and proximity of tyrosine residues amplify the likelihood of condensate formation. MD simulations revealed that the tyrosine-rich termini exhibit compact conformations with unique contact networks and provided critical input on the relationship between contacts formed within a single molecule (intramolecular) and inside the condensed phase (intermolecular). These findings enhance our understanding of FET proteins' condensate-forming capabilities and underline differences between EWS, FUS, and TAF15.

Proxy re-encryption with plaintext checkable encryption for integrating digital twins into IIoT

Digital Twin (DT) technology has emerged as a robust mechanism for overseeing and improving the lifecycle processes of the Industrial Internet of Things (IIoT) by creating their virtual counterparts. The widespread employment of cloud computing is apparent in delivering computational and storage services for the DT within the IIoT environment. Ensuring the confidentiality of DT data is crucial since data owners, cloud service providers, and users operate across separate trust domains. Accordingly, public key encryption is a viable solution for managing access. However, traditional public key encryption encounters challenges in achieving effective search and secure sharing of encrypted DT data in the cloud. This paper introduces a proxy re-encryption scheme with plaintext checkable encryption for data access control (PRE-IBSC-PCE), which addresses the challenges associated with searching for and sharing encrypted digital twin data in the cloud. The PRE-IBSC-PCE achieves efficient data search and sharing while ensuring privacy, integrity, non-repudiation, and authenticity. Meanwhile, we prove that PRE-IBSC-PCE is secure under the decisional bilinear Diffie–Hellman assumption in the random oracle model. Lastly, when compared to similar schemes, the performance analysis of the PRE-IBSC-PCE is more efficient and suitable for DT applications in IIoT.

BPR3P0128, a non-nucleoside RNA-dependent RNA polymerase inhibitor, inhibits SARS-CoV-2 variants of concern and exerts synergistic antiviral activity in combination with remdesivir.

Viral RNA-dependent RNA polymerase (RdRp), a highly conserved molecule in RNA viruses, has recently emerged as a promising drug target for broad-acting inhibitors. Through a Vero E6-based anti-cytopathic effect assay, we found that BPR3P0128, which incorporates a quinoline core similar to hydroxychloroquine, outperformed the adenosine analog remdesivir in inhibiting RdRp activity (EC50 = 0.66 µM and 3 µM, respectively). BPR3P0128 demonstrated broad-spectrum activity against various severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern. When introduced after viral adsorption, BPR3P0128 significantly decreased SARS-CoV-2 replication; however, it did not affect the early entry stage, as evidenced by a time-of-drug-addition assay. This suggests that BPR3P0128's primary action takes place during viral replication. We also found that BPR3P0128 effectively reduced the expression of proinflammatory cytokines in human lung epithelial Calu-3 cells infected with SARS-CoV-2. Molecular docking analysis showed that BPR3P0128 targets the RdRp channel, inhibiting substrate entry, which implies it operates differently-but complementary-with remdesivir. Utilizing an optimized cell-based minigenome RdRp reporter assay, we confirmed that BPR3P0128 exhibited potent inhibitory activity. However, an enzyme-based RdRp assay employing purified recombinant nsp12/nsp7/nsp8 failed to corroborate this inhibitory activity. This suggests that BPR3P0128 may inhibit activity by targeting host-related RdRp-associated factors. Moreover, we discovered that a combination of BPR3P0128 and remdesivir had a synergistic effect-a result likely due to both drugs interacting with separate domains of the RdRp. This novel synergy between the two drugs reinforces the potential clinical value of the BPR3P0128-remdesivir combination in combating various SARS-CoV-2 variants of concern.

Bio-based flame retardant for manufacturing fire safety, strong yet tough versatile epoxy resin

Bio-based flame retardants have attracted growing attention for sustainability and comparable efficiency to petroleum-based counterparts but suffer from compatibility issues with epoxy resins and the trade-off between strength and toughness. Here, a Schiff base-derived phosphorus-based flame retardant (TV-DOPO) was developed from biobased vanillin and tyramine. In the presence of the binary curing agents, nanoscale in-situ constructive phase separation induced by hierarchical thermal curing process and strong hydrogen bonding provide the composite resin with enhanced both stiffness and toughness. The flexural and impact strengths reached 159 MPa and 55.4 kJ m−2, which were increased by 36.5% and 129.6%, respectively, compared with the pristine epoxy, meanwhile excellent flame retardance was well maintained (UL-94 V-0 rating). Remarkably, these conjugated phosphaphenanthrene-containing composite resins demonstrate excellent UV-shielding capacity and optical transmittance. In this work, in-situ construction of a crosslinked network with tunable size of phase separation domains can be simply achieved by modulating the ratio of binary curing agents, to fabricate epoxy resins with all-around enhanced performance.

A Robust and Real-Time Framework of Cross-Subject Myoelectric Control Model Calibration via Multi-Source Domain Adaptation.

Surface electromyogram (sEMG) has been widely used in hand gesture recognition. However, most previous studies focused on user-personalized models, which require a great amount of data from each new target user to learn the user-specific EMG patterns. In this work, we present a novel real-time gesture recognition framework based on multi-source domain adaptation, which learns extra knowledge from the data of other users, thereby reducing the data collection burdens on the target user. Additionally, compared with conventional domain adaptation methods which treat data from all users in the source domain as a whole, the proposed multi-source method treat data from different users as multiple separate source domains. Therefore, more detailed statistical information on the data distribution from each user can be learned effectively. High-density sEMG (256 channels) from 20 subjects was used to validate the proposed method. Importantly, we evaluated our method with a simulated real-time processing pipeline on continuous sEMG data stream, rather than well-segmented data. The false alarm rate during rest periods in an EMG data stream, which is typically neglected by previous studies performing offline analyses, was also considered. Our results showed that, with only 1 s sEMG data per gesture from the new user, the 10-gesture classification accuracy reached 87.66 % but the false alarm rate was reduced to 1.95 %. Our method can reduce the frustratingly heavy data collection burdens on each new user.

Stress Patterns and Crustal Anisotropy in the Eastern Alps: Insights From Seismological and Geological Observations

AbstractIn the Eastern Alps, the indentation of the Adriatic promontory since the Cenozoic affected the kinematics of separate crustal domains bounded by faults that accommodate lateral extrusion processes and differential shortening. Deciphering the pattern of crustal stresses in the orogen interior is challenging, due to the lack of in situ stress measurements at crustal depths. We define stress regimes and the orientations of the most‐compressive horizontal stress (SHmax) by integrating published results with new data, including stress analysis from fault plane solutions, estimation of crustal anisotropy through the shear wave splitting analysis, paleostress determination from fault slip data, and computation of cumulative seismic displacements. The retrieved regional SHmax are generally consistent with N‐S convergence. In the northern part of the study area, current stress orientations are almost parallel to paleo‐SHmax, suggesting a rather uniform compressional regime since the late Cenozoic. Conversely, sharp deflections and divergence with paleo‐SHmax appear at the western border of the Adriatic promontory across major transpressive and extensional shear zones and in the Southalpine domain, indicating a change in tectonically induced second‐order stresses. A current strike‐slip regime with subordinate orogen‐parallel seismic displacements affects a belt to north of the Periadriatic Lineament and NE‐extension characterizes the Ortles‐Engandine region. Seismic anisotropy locally exhibits fault‐parallel fast axes (Brenner‐Giudicarie fault‐systems, Dinaric and Southalpine thrusts), whereas stress‐induced anisotropy parallel to SHmax characterizes the southern part of the orogen. Cumulative seismic displacements are small compared to geodetic ones, and unravel partitioning of deformation into second‐order transpressive and extensional belts in response to indentation.

Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG

Variational autoencoder (VAE)-based representation learning algorithms are explored for their capability to disentangle tokamak size dependence from other dependencies in a dataset of thousands of observed pedestal electron density and temperature profiles from JET and ASDEX Upgrade tokamaks. Representation learning aims to establish a useful representation that characterizes the dataset. In the context of magnetic confinement fusion devices, a useful representation could be considered to map the high-dimensional observations to a manifold that represents the actual degrees of freedom of the plasma scenario. A desired property for these representations is organization of the information into disentangled variables, enabling interpretation of the latent variables as representations of semantically meaningful characteristics of the data. The representation learning algorithms in this work are based on VAE that encodes the pedestal profile information into a reduced dimensionality latent space and learns to reconstruct the full profile information given the latent representation. Attaching an auxiliary regression objective for the machine control parameter configuration, broadly following the architecture of the domain invariant variational autoencoder (DIVA), the model learns to associate device control parameters with the latent representation. With this multimachine dataset, the representation does encode density scaling with device size that is qualitatively consistent with Greenwald density limit scaling. However, if the major radius of the device is given through a common regression objective with the other machine control parameters, the latent state of the representation struggles to clearly disentangle the device size from changes of the other machine control parameters. When separating the device size as an independent latent variable with dedicated regression objectives, similar to separation of domain and class labels in the original DIVA publication, the latent space becomes well organized as a function of the device size.

Determination of Tea Quality: A Separate Domain Graph Convolution Network Combined With an Electronic Nose

Determination of Tea Quality: A Separate Domain Graph Convolution Network Combined With an Electronic Nose

Sequestration within peptide coacervates improves the fluorescence intensity, kinetics, and limits of detection of dye-based DNA biosensors

Peptide-based liquid-liquid phase separated domains, or coacervates, are a biomaterial gaining new interest due to their exciting potential in fields ranging from biosensing to drug delivery. In this study, we demonstrate that coacervates provide a simple and biocompatible medium to improve nucleic acid biosensors through the sequestration of both the biosensor and target strands within the coacervate, thereby increasing their local concentration. Using the well-established polyarginine (R9) – ATP coacervate system and an energy transfer-based DNA molecular beacon we observed three key improvements: i) a greater than 20-fold reduction of the limit of detection within coacervates when compared to control buffer solutions; ii) an increase in the kinetics, equilibrium was reached more than 4-times faster in coacervates; and iii) enhancement in the dye fluorescent quantum yields within the coacervates, resulting in greater signal-to-noise. The observed benefits translate into coacervates greatly improving bioassay functionality.

A finite volume method to solve the Poisson equation with jump conditions and surface charges: Application to electroporation

Efficient numerical schemes for solving the Poisson equation with jump conditions are of great interest for a variety of problems, including the modeling of electroporation phenomena and filamentary discharges. In this paper, we propose a modification to a finite volume scheme, namely the discrete dual finite volume method, in order to account for jump conditions with surface charges, i.e. with a source term. Our numerical tests demonstrate second-order convergence even with highly distorted meshes. We then apply the proposed method to model electroporation phenomena in biological cells by proposing a model that considers the thickness of the cell membrane as a separate domain, which differs from the literature. We show the advantages of the proposed method in this context through numerical experiments.

Comparing Ammonium and Tetraaminophosphonium Anion-Exchange Membranes Derived from Vinyl-Addition Polynorbornene Copolymers.

Herein, we systematically examined how composition influenced the properties of vinyl addition polynorbornene anion exchange membranes (AEMs) prepared from 5-n-hexyl-2-norbornene and 5-(4-bromobutyl)-2-norbornene. Copolymerization kinetics revealed that 5-n-hexyl-2-norbornene is consumed faster than 5-(4-bromobutyl)-2-norbornene, leading to a portion of the chain being richer in bromoalkyl groups. The alkyl halide pendants can then be converted to either trimethylammonium or tetrakis(dialkylamino)phosphonium cations through straightforward substitution with trimethylamine or a tris(dialkylamino)phosphazene. A series of cationic ammonium polymers were synthesized first, where conductivity and water uptake increased as a function of increasing ionic content in the polymer. The optimized copolymer had a hydroxide conductivity of 95 ± 6 mS/cm at 80 °C. The living polymerization of the two monomers catalyzed by a cationic tert-butylphosphine palladium catalyst also enabled precise changes in the molecular weight while keeping the functional group concentration constant. Molecular weight did not have a significant impact on hydroxide conductivity over the range of ∼60-190 kg/mol (Mn). The optimized tetraaminophosphonium AEM had the highest conductivity for any tetraaminophosphonium polymer to date (70 ± 3 mS/cm at 80 °C). Clear phase separation and larger domains were observed for the phosphonium-based AEM compared to the ammonium at an identical composition, which is attributed to the larger occupied volume of the phosphorus cation. Fuel cell studies with the two membranes resulted in peak power densities of 1.59 and 0.79 W/cm2 for the ammonium and tetraaminophosphonium membrane electrode assemblies, respectively. The ammonium-based membrane was more water permeable as evidenced by water limiting current studies, which likely contributed to the improved performance.

Structural and mechanistic characterization of bifunctional heparan sulfate N-deacetylase-N-sulfotransferase 1

Heparan sulfate (HS) polysaccharides are major constituents of the extracellular matrix, which are involved in myriad structural and signaling processes. Mature HS polysaccharides contain complex, non-templated patterns of sulfation and epimerization, which mediate interactions with diverse protein partners. Complex HS modifications form around initial clusters of glucosamine-N-sulfate (GlcNS) on nascent polysaccharide chains, but the mechanistic basis underpinning incorporation of GlcNS itself into HS remains unclear. Here, we determine cryo-electron microscopy structures of human N-deacetylase-N-sulfotransferase (NDST)1, the bifunctional enzyme primarily responsible for initial GlcNS modification of HS. Our structures reveal the architecture of both NDST1 deacetylase and sulfotransferase catalytic domains, alongside a non-catalytic N-terminal domain. The two catalytic domains of NDST1 adopt a distinct back-to-back topology that limits direct cooperativity. Binding analyses, aided by activity-modulating nanobodies, suggest that anchoring of the substrate at the sulfotransferase domain initiates the NDST1 catalytic cycle, providing a plausible mechanism for cooperativity despite spatial domain separation. Our data shed light on key determinants of NDST1 activity, and describe tools to probe NDST1 function in vitro and in vivo.

Phenotypic Patterning through Copy Number Adaptation to Environmental Gradients.

We recently described a paradigm for engineering bacterial adaptation using plasmids coupled to the same origin of replication. In this study, we use plasmid coupling to generate spatially separated and phenotypically distinct populations in response to heterogeneous environments. Using a custom microfluidic device, we continuously tracked engineered populations along induced gradients, enabling an in-depth analysis of the spatiotemporal dynamics of plasmid coupling. Our observations reveal a pronounced phenotypic separation within 4 h exposure to an opposing gradient of AHL and arabinose. Additionally, by modulating the burden strength balance between coupled plasmids, we demonstrate the inherent limitations and tunability of this system. Intriguingly, phenotypic separation persists for an extended time, hinting at a biophysical spatial retention mechanism reminiscent of natural speciation processes. Complementing our experimental data, mathematical models provide invaluable insights into the underlying mechanisms and guide optimization of plasmid coupling for prospective applications of environmental copy number adaptation engineering across separated domains.

An assessment of dynamic facial emotion recognition and theory of mind in children with ADHD: An eye-tracking study.

Deficits in social cognition in attention deficit hyperactivity disorder (ADHD) have been associated with difficulties in functioning. Since recognizing emotional facial expressions is essential for developing the perceptual components of the theory of mind (ToM), it is important to assess this relationship in children with ADHD. This study therefore compared the recognition of emotional stimuli and gaze patterns between children with ADHD and healthy children using eye-tracking with dynamic facial images. It also examined the relationship between facial emotion recognition accuracy, gaze patterns, ToM scores, and ADHD symptoms. Children with ADHD aged 8-13 (n = 47) and a control group (n = 38) completed a facial emotion recognition test, ToM tests, and the Conners' Parent Rating Scale. Participants' gaze patterns in response to dynamic facial emotion expressions were recorded using eye-tracking technology. Children with ADHD exhibited significantly lower accuracy in the recognition of the facial expressions of disgust and anger. The percentage fixation in the eye region was also significantly lower for happy, angry, sad, disgusted, and neutral emotions in the children with ADHD compared to the control group. No relationship was determined between the percentage of fixations on facial areas of interests and ADHD symptoms or ToM tests. This study provides evidence that children with ADHD experience deficits in visual attention to emotional cues. In addition, it suggests that facial emotion recognition deficits in children with ADHD represent a separate domain of social cognition that develops independently of ToM skills and core symptoms. Understanding and treating the social difficulties of individuals with ADHD may help improve their social functioning.

Social Brain Perspectives on the Social and Evolutionary Neuroscience of Human Language.

Human language and social cognition are two key disciplines that have traditionally been studied as separate domains. Nonetheless, an emerging view suggests an alternative perspective. Drawing on the theoretical underpinnings of the social brain hypothesis (thesis of the evolution of brain size and intelligence), the social complexity hypothesis (thesis of the evolution of communication), and empirical research from comparative animal behavior, human social behavior, language acquisition in children, social cognitive neuroscience, and the cognitive neuroscience of language, it is argued that social cognition and language are two significantly interconnected capacities of the human species. Here, evidence in support of this view reviews (1) recent developmental studies on language learning in infants and young children, pointing to the important crucial benefits associated with social stimulation for youngsters, including the quality and quantity of incoming linguistic information, dyadic infant/child-to-parent non-verbal and verbal interactions, and other important social cues integral for facilitating language learning and social bonding; (2) studies of the adult human brain, suggesting a high degree of specialization for sociolinguistic information processing, memory retrieval, and comprehension, suggesting that the function of these neural areas may connect social cognition with language and social bonding; (3) developmental deficits in language and social cognition, including autism spectrum disorder (ASD), illustrating a unique developmental profile, further linking language, social cognition, and social bonding; and (4) neural biomarkers that may help to identify early developmental disorders of language and social cognition. In effect, the social brain and social complexity hypotheses may jointly help to describe how neurotypical children and adults acquire language, why autistic children and adults exhibit simultaneous deficits in language and social cognition, and why nonhuman primates and other organisms with significant computational capacities cannot learn language. But perhaps most critically, the following article argues that this and related research will allow scientists to generate a holistic profile and deeper understanding of the healthy adult social brain while developing more innovative and effective diagnoses, prognoses, and treatments for maladies and deficits also associated with the social brain.