Lundbergmcdonald9630

Individuals with an autism spectrum disorder (ASD) diagnosis are often described as having an eye for detail. But it remains to be shown that a detail-focused processing bias is a ubiquitous property of vision in individuals with ASD. To address this question, we investigated whether a greater number of autistic traits in neurotypical subjects is associated with an increased reliance on image details during a natural image recognition task. To this end, we use a novel reverse correlation-based method (feature diagnosticity mapping) for measuring the relative importance of low-level image features for object recognition. The main finding of this study is that image recognition in participants with an above-median number of autistic traits benefited more from the presence of high-spatial frequency image features. Furthermore, we found that this reliance-on-detail effect was best predicted by the presence of the most clinically relevant autistic traits. Therefore, our findings suggest that a greater number of autistic traits in neurotypical individuals is associated with a more detail-oriented visual information processing strategy and that this effect might generalize to a clinical ASD population.Solid organ transplantation is a lifesaving therapy for patients with end-organ disease. Current immunosuppression protocols are not designed to target antigen-specific alloimmunity and are uncapable of preventing chronic allograft injury. As myeloid-derived suppressor cells (MDSCs) are potent immunoregulatory cells, we tested whether donor-derived MDSCs can protect heart transplant allografts in an antigen-specific manner. C57BL/6 (H2Kb, I-Ab) recipients pre-treated with BALB/c MDSCs were transplanted with either donor-type (BALB/c, H2Kd, I-Ad) or third-party (C3H, H2Kk, I-Ak) cardiac grafts. Spleens and allografts from C57BL/6 recipients were harvested for immune phenotyping, transcriptomic profiling and functional assays. Single injection of donor-derived MDSCs significantly prolonged the fully MHC mismatched allogeneic cardiac graft survival in a donor-specific fashion. Transcriptomic analysis of allografts harvested from donor-derived MDSCs treated recipients showed down-regulated proinflammatory cytokines. H2DCFDA research buy Immune phenotyping showed that the donor MDSCs administration suppressed effector T cells in recipients. Interestingly, significant increase in recipient endogenous CD11b+Gr1+ MDSC population was observed in the group treated with donor-derived MDSCs compared to the control groups. Depletion of this endogenous MDSCs with anti-Gr1 antibody reversed donor MDSCs-mediated allograft protection. Furthermore, we observed that the allogeneic mixed lymphocytes reaction was suppressed in the presence of CD11b+Gr1+ MDSCs in a donor-specific manner. Donor-derived MDSCs prolong cardiac allograft survival in a donor-specific manner via induction of recipient's endogenous MDSCs.Diarrhoea is one of the most burdensome and common adverse events of chemotherapeutics, and has no standardised therapy to date. Increasing evidence suggests that the gut microbiome can influence the development of chemotherapy-induced diarrhoea. Here we report findings from a randomised clinical trial of faecal microbiota transplantation (FMT) to treat diarrhoea induced by tyrosine kinase inhibitors (TKI) in patients with metastatic renal cell carcinoma (ClinicalTrials.gov number NCT04040712). The primary outcome is the resolution of diarrhoea four weeks after the end of treatments. Twenty patients are randomised to receive FMT from healthy donors or placebo FMT (vehicle only). Donor FMT is more effective than placebo FMT in treating TKI-induced diarrhoea, and a successful engraftment is observed in subjects receiving donor faeces. No serious adverse events are observed in both treatment arms. The trial meets pre-specified endpoints. Our findings suggest that the therapeutic manipulation of gut microbiota may become a promising treatment option to manage TKI-dependent diarrhoea.Plants utilize a UV-B (280 to 315 nm) photoreceptor UVR8 (UV RESISTANCE LOCUS 8) to sense environmental UV levels and regulate gene expression to avoid harmful UV effects. Uniquely, UVR8 uses intrinsic tryptophan for UV-B perception with a homodimer structure containing 26 structural tryptophan residues. However, besides 8 tryptophans at the dimer interface to form two critical pyramid perception centers, the other 18 tryptophans' functional role is unknown. Here, using ultrafast fluorescence spectroscopy, computational methods and extensive mutations, we find that all 18 tryptophans form light-harvesting networks and funnel their excitation energy to the pyramid centers to enhance light-perception efficiency. We determine the timescales of all elementary tryptophan-to-tryptophan energy-transfer steps in picoseconds to nanoseconds, in excellent agreement with quantum computational calculations, and finally reveal a significant leap in light-perception quantum efficiency from 35% to 73%. This photoreceptor is the first system discovered so far, to be best of our knowledge, using natural amino-acid tryptophans to form networks for both light harvesting and light perception.Developing effective catalysts based on earth abundant elements is critical for CO2 electroreduction. However, simultaneously achieving a high Faradaic efficiency (FE) and high current density of CO (jCO) remains a challenge. Herein, we prepare a Mn single-atom catalyst (SAC) with a Mn-N3 site embedded in graphitic carbon nitride. The prepared catalyst exhibits a 98.8% CO FE with a jCO of 14.0 mA cm-2 at a low overpotential of 0.44 V in aqueous electrolyte, outperforming all reported Mn SACs. Moreover, a higher jCO of 29.7 mA cm-2 is obtained in an ionic liquid electrolyte at 0.62 V overpotential. In situ X-ray absorption spectra and density functional theory calculations demonstrate that the remarkable performance of the catalyst is attributed to the Mn-N3 site, which facilitates the formation of the key intermediate COOH* through a lowered free energy barrier.A common analysis of single-cell sequencing data includes clustering of cells and identifying differentially expressed genes (DEGs). How cell clusters are defined has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. To address this difficulty, we present singleCellHaystack, a method that enables the prediction of DEGs without relying on explicit clustering of cells. Our method uses Kullback-Leibler divergence to find genes that are expressed in subsets of cells that are non-randomly positioned in a multidimensional space. Comparisons with existing DEG prediction approaches on artificial datasets show that singleCellHaystack has higher accuracy. We illustrate the usage of singleCellHaystack through applications on 136 real transcriptome datasets and a spatial transcriptomics dataset. We demonstrate that our method is a fast and accurate approach for DEG prediction in single-cell data. singleCellHaystack is implemented as an R package and is available from CRAN and GitHub.