No abstract

The evolution of humans as a highly social species tuned the brain to the social world; yet the mechanisms by which humans coordinate their brain response online during social interactions remain unclear. Using hyperscanning EEG recordings, we measured brain-to-brain synchrony in 104 adults during a male-female naturalistic social interaction, comparing romantic couples and strangers. Neural synchrony was found for couples, but not for strangers, localized to temporal-parietal structures and expressed in gamma rhythms. Brain coordination was not found during a three-minute rest, pinpointing neural synchrony to social interactions among affiliative partners. Brain-to-brain synchrony was linked with behavioral synchrony. Among couples, neural synchrony was anchored in moments of social gaze and positive affect, whereas among strangers, longer durations of social gaze and positive affect correlated with greater neural synchrony. Brain-to-brain synchrony was unrelated to episodes of speech/no-speech or general content of conversation. Our findings link brain-to-brain synchrony to the degree of social connectedness among interacting partners, ground neural synchrony in key nonverbal social behaviors, and highlight the role of human attachment in providing a template for two-brain coordination.

Summary Recent scientific discoveries fuelled by the application of next‐generation DNA and RNA sequencing technologies highlight the striking impact of these platforms in characterizing multiple aspects in genomics research. This technology has been used in the study of the B‐cell and T‐cell receptor repertoire. The novelty of immunosequencing comes from the recent rapid development of techniques and the exponential reduction in cost of sequencing. Here, we describe some of the technologies, which we collectively refer to as Rep‐Seq (repertoire sequencing), to portray achievements in the field and to present the essential and inseparable role of next‐generation sequencing to the understanding of entities in immune response. The large Rep‐Seq data sets that should be available in the near future call for new computational algorithms to segue the transition from ‘classic’ molecular‐based analysis to system‐wide analysis. The combination of new algorithms with high‐throughput data will form the basis for possible new clinical implications in personalized medicine and deeper understanding of immune behaviour and immune response.

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO reaches similar results to state of the art methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO . ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/ .

Lumpy skin disease (LSD) is a severe viral disease of cattle. Circumstantial evidence suggests that the virus is transmitted mechanically by blood-feeding arthropods. We compared the importance of transmission via direct and indirect contact in field conditions by using mathematical tools. We analyzed a dataset collected during the LSD outbreak in 2006 in a large dairy herd, which included ten separated cattle groups. Outbreak dynamics and risk factors for LSD were assessed by a transmission model. Transmission by three contact modes was modelled; indirect contact between the groups within a herd, direct contact or contact via common drinking water within the groups and transmission by contact during milking procedure. Indirect transmission was the only parameter that could solely explain the entire outbreak dynamics and was estimated to have an overall effect that was over 5 times larger than all other possible routes of transmission, combined. The R0 value induced by indirect transmission per the presence of an infectious cow for 1 day in the herd was 15.7, while the R0 induced by direct transmission was 0.36. Sensitivity analysis showed that this result is robust to a wide range of assumptions regarding mean and standard deviation of incubation period and regarding the existence of sub-clinically infected cattle. These results indicate that LSD virus spread within the affected herd could hardly be attributed to direct contact between cattle or contact through the milking procedure. It is therefore concluded that transmission mostly occurs by indirect contact, probably by flying, blood-sucking insects. This has important implications for control of LSD.

IntroductionPredicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding.MethodsWe have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction.Results and DiscussionERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II.

Hepatitis B virus (HBV) reactivation during immunosuppression can lead to severe acute hepatitis, fulminant liver failure, and death. Here, we investigated hepatitis B surface antigen (HBsAg) genetic features underlying this phenomenon by analyzing 93 patients: 29 developing HBV reactivation and 64 consecutive patients with chronic HBV infection (as control). HBsAg genetic diversity was analyzed by population-based and ultradeep sequencing (UDS). Before HBV reactivation, 51.7% of patients were isolated hepatitis B core antibody (anti-HBc) positive, 31.0% inactive carriers, 6.9% anti-HBc/anti-HBs (hepatitis B surface antibody) positive, 6.9% isolated anti-HBs positive, and 3.4% had an overt HBV infection. Of HBV-reactivated patients, 51.7% were treated with rituximab, 34.5% with different chemotherapeutics, and 13.8% with corticosteroids only for inflammatory diseases. In total, 75.9% of HBV-reactivated patients (vs. 3.1% of control patients; P < 0.001) carried HBsAg mutations localized in immune-active HBsAg regions. Of the 13 HBsAg mutations found in these patients, 8 of 13 (M103I-L109I-T118K-P120A-Y134H-S143L-D144E-S171F) reside in a major hydrophilic loop (target of neutralizing antibodies [Abs]); some of them are already known to hamper HBsAg recognition by humoral response. The remaining five (C48G-V96A-L175S-G185E-V190A) are localized in class I/ II-restricted T-cell epitopes, suggesting a role in HBV escape from T-cell-mediated responses. By UDS, these mutations occurred in HBV-reactivated patients with a median intrapatient prevalence of 73.3% (range, 27.6%-100%) supporting their fixation in the viral population as a predominant species. In control patients carrying such mutations, their median intrapatient prevalence was 4.6% (range, 2.5%-11.3%; P < 0.001). Finally, additional N-linked glycosylation (NLG) sites within the major hydrophilic loop were found in 24.1% of HBV-reactivated patients (vs. 0% of chronic patients; P < 0.001); 5 of 7 patients carrying these sites remained HBsAg negative despite HBV reactivation. NLG can mask immunogenic epitopes, abrogating HBsAg recognition by Abs. Conclusion: HBV reactivation occurs in a wide variety of clinical settings requiring immune-suppressive therapy, and correlates with HBsAg mutations endowed with enhanced capability to evade immune response. This highlights the need for careful patient monitoring in all immunosuppressive settings at reactivation risk and of establishing a prompt therapy to prevent HBV-related clinical complications. (HEPATOLOGY 2015;61:823-833)

Many systems in chemistry, biology, finance, and social sciences present emerging features that are not easy to guess from the elementary interactions of their microscopic individual components. In the past, the macroscopic behavior of such systems was modeled by assuming that the collective dynamics of microscopic components can be effectively described collectively by equations acting on spatially continuous density distributions. It turns out that, to the contrary, taking into account the actual individual͞ discrete character of the microscopic components of these systems is crucial for explaining their macroscopic behavior. In fact, we find that in conditions in which the continuum approach would predict the extinction of all of the population (respectively the vanishing of the invested capital or the concentration of a chemical substance, etc.), the microscopic granularity insures the emergence of macroscopic localized subpopulations with collective adaptive properties that allow their survival and development. In particular it is found that in two dimensions ''life'' (the localized proliferating phase) always prevails.I n addition to physics, an increasing range of sciences (chemistry, biology, ecology, finance, urban and social planning) have moved in the last century to quantitative mathematical methods.Along with the obvious benefits, it turns out that the traditional differential equations approach has brought some fallacy into the study of such sciences.We present here a very simple generic model that contains proliferating (and dying) individuals, and we show that in reality it behaves very differently than its representation in terms of continuum density distributions. In conditions in which the continuum equations predict the population extinction, the individuals self-organize in spatio-temporally localized adaptive patches, which ensure their survival and development.This phenomenon admits multiple interpretations in various fields:If the individuals are interpreted as interacting molecules, the resulting chemical system emerges spatial patches of high density that evolve adaptively in a way similar with the first selfsustaining systems that might have anticipated living cells.If the individuals are the carriers of specific genotypes represented in the genetic space, the patches can be identified with species, which rather than becoming extinct, evolve between various genomes (locations in the genetic space) by abandoning regions of low viability in favor of more viable regions. This adaptive speciation behavior emerges despite the total randomness we assume for the individuals motions in the genetic space (mutations).Interpreted as financial traders, the individuals develop a ''herding'' behavior despite the fact that we do not introduce communication or interaction between them. The model leads to the flourishing of markets, which the continuum analysis would doom to extinction.All these phenomena have in common the emergence of large, macroscopic structures from apparently uniform background (...