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Peptide-major histocompatibility complex (pMHC) multimers enable the detection of antigen-specific T cells in studies ranging from vaccine efficacy to cancer immunotherapy. However, this technology is unreliable when applied to pMHC class II for the detection of CD4+ T cells. Here, using a combination of molecular biological and immunological techniques, we cloned sequences encoding human leukocyte antigen (HLA)-DP, HLA-DQ and HLA-DR molecules with enhanced CD4 binding affinity (with a Kd of 8.9 ± 1.1 µM between CD4 and affinity-matured HLA-DP4) and produced affinity-matured class II dimers that stain antigen-specific T cells better than conventional multimers in both in vitro and ex vivo analyses. Using a comprehensive library of dimers for HLA-DP4, which is the most frequent HLA allele in many ancestry groups, we mapped 103 HLA-DP4-restricted epitopes derived from diverse tumor-associated antigens and cloned the cognate T-cell antigen receptor (TCR) genes from in vitro-stimulated CD4+ T cells. The availability of affinity-matured class II dimers across HLA-DP, HLA-DQ and HLA-DR alleles will aid in the investigation of human CD4+ T-cell responses.Lung and airway epithelial cells generated in vitro from human pluripotent stem cells (hPSCs) have applications in regenerative medicine, modeling of lung disease, drug screening and studies of human lung development. Here, we describe a strategy for directed differentiation of hPSCs into mature lung and airway epithelial cells obtained through maturation of NKX2.1+ hPSC-derived lung progenitors in a 3D matrix of collagen I in the absence of glycogen synthase kinase 3 inhibition. This protocol is an extension of our previously published protocol on the directed differentiation of lung and airway epithelium from hPSCs that modifies the technique and offers additional applications. This protocol is conducted in defined media conditions, has a duration of 50-80 d, does not require reporter lines and results in cultures containing mature alveolar type II and I cells as well as airway basal, ciliated, club and neuroendocrine cells. We also present a flow cytometry strategy to assess maturation in the cultures. Several of these populations, including mature NGFR+ basal cells, can be prospectively isolated by cell sorting and expanded for further investigation.Computational methods are key in microbiome research, and obtaining a quantitative and unbiased performance estimate is important for method developers and applied researchers. For meaningful comparisons between methods, to identify best practices and common use cases, and to reduce overhead in benchmarking, it is necessary to have standardized datasets, procedures and metrics for evaluation. In this tutorial, we describe emerging standards in computational meta-omics benchmarking derived and agreed upon by a larger community of researchers. Specifically, we outline recent efforts by the Critical Assessment of Metagenome Interpretation (CAMI) initiative, which supplies method developers and applied researchers with exhaustive quantitative data about software performance in realistic scenarios and organizes community-driven benchmarking challenges. We explain the most relevant evaluation metrics for assessing metagenome assembly, binning and profiling results, and provide step-by-step instructions on how to generate them. The instructions use simulated mouse gut metagenome data released in preparation for the second round of CAMI challenges and showcase the use of a repository of tool results for CAMI datasets. This tutorial will serve as a reference for the community and facilitate informative and reproducible benchmarking in microbiome research.Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we report an interpretable weakly supervised deep-learning method for data-efficient WSI processing and learning that only requires slide-level labels. The method, which we named clustering-constrained-attention multiple-instance learning (CLAM), uses attention-based learning to identify subregions of high diagnostic value to accurately classify whole slides and instance-level clustering over the identified representative regions to constrain and refine the feature space. By applying CLAM to the subtyping of renal cell carcinoma and non-small-cell lung cancer as well as the detection of lymph node metastasis, we show that it can be used to localize well-known morphological features on WSIs without the need for spatial labels, that it overperforms standard weakly supervised classification algorithms and that it is adaptable to independent test cohorts, smartphone microscopy and varying tissue content.The discovery of intrinsic ferromagnetism in ultrathin two-dimensional van der Waals crystals opens up exciting prospects for exploring magnetism in the ultimate two-dimensional limit. Here, we show that environmentally stable CrSe2 nanosheets can be readily grown on a dangling-bond-free WSe2 substrate with systematically tunable thickness down to the monolayer limit. These CrSe2/WSe2 heterostructures display high-quality van der Waals interfaces with well-resolved moiré superlattices and ferromagnetic behaviour. We find no apparent change in surface roughness or magnetic properties after months of exposure in air. Our calculations suggest that charge transfer from the WSe2 substrate and interlayer coupling within CrSe2 play a critical role in the magnetic order in few-layer CrSe2 nanosheets. read more The highly controllable growth of environmentally stable CrSe2 nanosheets with tunable thickness defines a robust two-dimensional magnet for fundamental studies and potential applications in magnetoelectronic and spintronic devices.Organic light-emitting transistors, three-terminal devices combining a thin-film transistor with a light-emitting diode, have generated increasing interest in organic electronics. However, increasing their efficiency while keeping the operating voltage low still remains a key challenge. Here, we demonstrate organic permeable base light-emitting transistors; these three-terminal vertical optoelectronic devices operate at driving voltages below 5.0 V; emit in the red, green and blue ranges; and reach, respectively, peak external quantum efficiencies of 19.6%, 24.6% and 11.8%, current efficiencies of 20.6 cd A-1, 90.1 cd A-1 and 27.1 cd A-1 and maximum luminance values of 9,833 cd m-2, 12,513 cd m-2 and 4,753 cd m-2. Our simulations demonstrate that the nano-pore permeable base electrode located at the centre of the device, which forms a distinctive optical microcavity and regulates charge carrier injection and transport, is the key to the good performance obtained. Our work paves the way towards efficient and low-voltage organic light-emitting transistors, useful for power-efficient active matrix displays and solid-state lighting.