Yunfan Sun, Pin Wu, Zefan Zhang, Zejian Wang, Kaiqian Zhou, Minfang Song, Yuan Ji, Fenglin Zang, Limu Lou, Keqiang Rao, Pengxiang Wang, Yutong Gu, Jie Gu, Binbin Lu, Limeng Chen, Xiuqi Pan, Xiaojing Zhao, Lihua Peng, Dongbing Liu, Xiaofang Chen, Kui Wu, Penghui Lin, Liang Wu, Yulin Su, Min Du, Yingyong Hou, Xinrong Yang, Shuangjian Qiu, Yinghong Shi, Huichuan Sun, Jian Zhou, Xingxu Huang, David H Peng, Liye Zhang, Jia Fan <<<

Comprehensive molecular analyses of metastatic hepatocellular carcinoma (HCC) are lacking. Here, we generate multi-omic profiling of 257 primary and 176 metastatic regions from 182 HCC patients. Primary tumors rich in hypoxia signatures facilitated polyclonal dissemination. Genomic divergence between primary and metastatic HCC is high, and early dissemination is prevalent. The remarkable neoantigen intratumor heterogeneity observed in metastases is associated with decreased T cell reactivity, resulting from disruptions to neoantigen presentation. We identify somatic copy number alterations as highly selected events driving metastasis. Subclones without Wnt mutations show a stronger selective advantage for metastasis than those with Wnt mutations and are characterized by a microenvironment rich in activated fibroblasts favoring a pro-metastatic phenotype. Finally, metastases without Wnt mutations exhibit higher enrichment of immunosuppressive B cells that mediate terminal exhaustion of CD8 T cells via HLA-E:CD94-NKG2A checkpoint axis. Collectively, our results provide a multi-dimensional dissection of the complex evolutionary process of metastasis. <<<

Xin-Min Li, Hannah Jenke, Sören Strauss, Christos Bazakos, Gabriella Mosca, Rena Lymbouridou, Daniel Kierzkowski, Ulla Neumann, Purva Naik, Peter Huijser, Stefan Laurent, Richard S Smith, Adam Runions, Miltos Tsiantis <<<

How is time encoded into organ growth and morphogenesis? We address this question by investigating heteroblasty, where leaf development and form are modified with progressing plant age. By combining morphometric analyses, fate-mapping through live-imaging, computational analyses, and genetics, we identify age-dependent changes in cell-cycle-associated growth and histogenesis that underpin leaf heteroblasty. We show that in juvenile leaves, cell proliferation competence is rapidly released in a "proliferation burst" coupled with fast growth, whereas in adult leaves, proliferative growth is sustained for longer and at a slower rate. These effects are mediated by the SPL9 transcription factor in response to inputs from both shoot age and individual leaf maturation along the proximodistal axis. SPL9 acts by activating CyclinD3 family genes, which are sufficient to bypass the requirement for SPL9 in the control of leaf shape and in heteroblastic reprogramming of cellular growth. In conclusion, we have identified a mechanism that bridges across cell, tissue, and whole-organism scales by linking cell-cycle-associated growth control to age-dependent changes in organ geometry. <<<

OBJECTIVE: To compare available analysis methods for determining fetal fraction on single read next generation sequencing data. This is important as the performance of non-invasive prenatal testing (NIPT) procedures depends on the fraction of fetal DNA.METHODS: We tested six different methods for the detection of fetal fraction in NIPT samples. The same clinically obtained data were used for all methods, allowing us to assess the effect of fetal fraction on the test result, and to investigate the use of fetal fraction for quality control.RESULTS: We show that non-NIPT methods based on body mass index (BMI) and gestational age are unreliable predictors of fetal fraction, male pregnancy specific methods based on read counts on the Y chromosome perform consistently and the fetal sex-independent new methods SeqFF and SANEFALCON are less reliable but can be used to obtain a basic indication of fetal fraction in case of a female fetus.CONCLUSION: We recommend the use of a combination of methods to prevent the issue of reports on samples with insufficient fetal DNA; SANEFALCON to check for presence of fetal DNA, SeqFF for estimating the fetal fraction for a female pregnancy and any Y-based method for estimating the fetal fraction for a male pregnancy. © 2017 The Authors. Prenatal Diagnosis published by John Wiley & Sons, Ltd. <<<

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick <<<

We introduce the Segment Anything (SA) project: a new task, model, anddataset for image segmentation. Using our efficient model in a data collectionloop, we built the largest segmentation dataset to date (by far), with over 1billion masks on 11M licensed and privacy respecting images. The model isdesigned and trained to be promptable, so it can transfer zero-shot to newimage distributions and tasks. We evaluate its capabilities on numerous tasksand find that its zero-shot performance is impressive -- often competitive withor even superior to prior fully supervised results. We are releasing theSegment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and11M images at https://segment-anything.com to foster research into foundationmodels for computer vision. <<<

Dynamic changes in the three-dimensional (3D) organization of chromatin are associated with central biological processes, such as transcription, replication and development. Therefore, the comprehensive identification and quantification of these changes is fundamental to understanding of evolutionary and regulatory mechanisms. Here, we present Comparison of Hi-C Experiments using Structural Similarity (CHESS), an algorithm for the comparison of chromatin contact maps and automatic differential feature extraction. We demonstrate the robustness of CHESS to experimental variability and showcase its biological applications on (1) interspecies comparisons of syntenic regions in human and mouse models; (2) intraspecies identification of conformational changes in Zelda-depleted Drosophila embryos; (3) patient-specific aberrant chromatin conformation in a diffuse large B-cell lymphoma sample; and (4) the systematic identification of chromatin contact differences in high-resolution Capture-C data. In summary, CHESS is a computationally efficient method for the comparison and classification of changes in chromatin contact data. <<<

Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner, Viet Tran, Fei Tang, Hubert Ramsauer, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, Sepp Hochreiter <<<

CLIP yielded impressive results on zero-shot transfer learning tasks and isconsidered as a foundation model like BERT or GPT3. CLIP vision models thathave a rich representation are pre-trained using the InfoNCE objective andnatural language supervision before they are fine-tuned on particular tasks.Though CLIP excels at zero-shot transfer learning, it suffers from anexplaining away problem, that is, it focuses on one or few features, whileneglecting other relevant features. This problem is caused by insufficientlyextracting the covariance structure in the original multi-modal data. Wesuggest to use modern Hopfield networks to tackle the problem of explainingaway. Their retrieved embeddings have an enriched covariance structure derivedfrom co-occurrences of features in the stored embeddings. However, modernHopfield networks increase the saturation effect of the InfoNCE objective whichhampers learning. We propose to use the InfoLOOB objective to mitigate thissaturation effect. We introduce the novel "Contrastive Leave One Out Boost"(CLOOB), which uses modern Hopfield networks for covariance enrichment togetherwith the InfoLOOB objective. In experiments we compare CLOOB to CLIP afterpre-training on the Conceptual Captions and the YFCC dataset with respect totheir zero-shot transfer learning performance on other datasets. CLOOBconsistently outperforms CLIP at zero-shot transfer learning across allconsidered architectures and datasets. <<<

Abstract Collagen structure in biological tissues imparts its intrinsic physical properties by the formation of several covalent crosslinks. For the first time, two major crosslinks in the skin dihydroxylysinonorleucine (HLNL) and histidinohydroxymerodesmosine (HHMD), were isotopically labelled and then analysed by liquid-chromatography high-resolution accurate-mass mass spectrometry (LC-HRMS) and small-angle neutron scattering (SANS). The isotopic labelling followed by LC-HRMS confirmed the presence of one imino group in both HLNL and HHMD, making them more susceptible to degrade at low pH. The structural changes in collagen due to extreme changes in the pH and chrome tanning were highlighted by the SANS contrast variation between isotopic labelled and unlabelled crosslinks. This provided a better understanding of the interaction of natural crosslinks with the chromium sulphate in collagen suggesting that the development of a benign crosslinking method can help retain the intrinsic physical properties of the leather. This analytical method can also be applied to study artificial crosslinking in other collagenous tissues for biomedical applications. Graphical abstract <<<

Sunita Keshari, Alexander S Shavkunov, Qi Miao, Akata Saha, Charmelle D Williams, Anna M Highsmith, Josué E Pineda, Elise Alspach, Kenneth H Hu, Kristen E Pauken, Ken Chen, Matthew M Gubin <<<

The goal of therapeutic cancer vaccines and immune checkpoint therapy (ICT) is to eliminate cancer by expanding and/or sustaining T cells with anti-tumor capabilities. However, whether cancer vaccines and ICT enhance anti-tumor immunity by distinct or overlapping mechanisms remains unclear. Here, we compared effective therapeutic tumor-specific mutant neoantigen (NeoAg) cancer vaccines with anti-CTLA-4 and/or anti-PD-1 ICT in preclinical models. Both NeoAg vaccines and ICT induce expansion of intratumoral NeoAg-specific CD8 T cells, though the degree of expansion and acquisition of effector activity was much more substantial following NeoAg vaccination. Further, we found that NeoAg vaccines are particularly adept at inducing proliferating and stem-like NeoAg-specific CD8 T cells. Single cell T cell receptor (TCR) sequencing revealed that TCR clonotype expansion and diversity of NeoAg-specific CD8 T cells relates to their phenotype and functional state associated with specific immunotherapies employed. Effective NeoAg vaccines and ICT required both CD8 and CD4 T cells. While NeoAg vaccines and anti-PD-1 affected the CD4 T cell compartment, it was to less of an extent than observed with anti-CTLA-4, which notably induced ICOSBhlhe40 Th1-like CD4 T cells and, when combined with anti-PD-1, a small subset of Th2-like CD4 T cells. Although effective NeoAg vaccines or ICT expanded intratumoral M1-like iNOS macrophages, NeoAg vaccines expanded rather than suppressed (as observed with ICT) M2-like CX3CR1CD206 macrophages, associated with the vaccine adjuvant. Further, combining NeoAg vaccination with ICT induced superior efficacy compared to either therapy in isolation, highlighting the utility of combining these modalities to eliminate cancer. <<<

The interleukin-1 (IL-1) family of cytokines and receptors are implicated in the functioning of innate and adaptive immunity and the genesis of inflammation. They are widely expressed in structural and immune cells with marked expression within barrier mucosal surfaces. In the lung, gut and skin, which are common entry sites for pathogens, they play essential functions in maintaining the functional integrity of the barrier and manage innate and adaptive immunity in response to insult and infections. In tissue sites, the IL-1 cytokines are tightly regulated by mechanisms involving decoy receptors and protease degradation. Dysregulation of these processes are associated with aberrant tissue inflammation leading to a number of inflammatory diseases. This review will address the roles of the different IL-1 cytokines at the lung, gut and skin barrier surfaces at homeostasis, and their roles as inflammatory mediators in diseases such as asthma, chronic obstructive pulmonary disease, inflammatory bowel diseases, atopic dermatitis and psoriasis. <<<

Zaoqu Liu, Long Liu, Siyuan Weng, Chunguang Guo, Qin Dang, Hui Xu, Libo Wang, Taoyuan Lu, Yuyuan Zhang, Zhenqiang Sun, Xinwei Han <<<

Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients. <<<

Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients without making any assumptions about the machine-learning algorithm that supplies the predictions. Furthermore, more accurate predictions translate to smaller confidence intervals. Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning. The benefits of prediction-powered inference were demonstrated with datasets from proteomics, astronomy, genomics, remote sensing, census analysis, and ecology. <<<

The cancer-immunity cycle provides a framework to understand the series of events that generate anti-cancer immune responses. It emphasizes the iterative nature of the response where the killing of tumor cells by T cells initiates subsequent rounds of antigen presentation and T cell stimulation, maintaining active immunity and adapting it to tumor evolution. Any step of the cycle can become rate-limiting, rendering the immune system unable to control tumor growth. Here, we update the cancer-immunity cycle based on the remarkable progress of the past decade. Understanding the mechanism of checkpoint inhibition has evolved, as has our view of dendritic cells in sustaining anti-tumor immunity. We additionally account for the role of the tumor microenvironment in facilitating, not just suppressing, the anti-cancer response, and discuss the importance of considering a tumor's immunological phenotype, the "immunotype". While these new insights add some complexity to the cycle, they also provide new targets for research and therapeutic intervention. <<<

The prevalence of SARS-CoV-2 is a great threat to global public health. However, the relationship between the viral pathogen SARS-CoV-2 and host innate immunity has not yet been well studied. The genome of SARS-CoV-2 encodes a viral protease called 3C-like protease. This protease is responsible for cleaving viral polyproteins during replication. In this investigation, 293T cells were transfected with SARS-CoV-2 3CL and then infected with Sendai virus (SeV) to induce the RIG-I like receptor (RLR)-based immune pathway. q-PCR, luciferase reporter assays, and western blotting were used for experimental analyses. We found that SARS-CoV-2 3CL significantly downregulated IFN-β mRNA levels. Upon SeV infection, SARS-CoV-2 3CL inhibited the nuclear translocation of IRF3 and p65 and promoted the degradation of IRF3. This effect of SARS-CoV-2 3CL on type I IFN in the RLR immune pathway opens up novel ideas for future research on SARS-CoV-2. <<<

Artificial intelligence (AI) is widely used for exploiting multimodal biomedical data, with increasingly accurate predictions and model-agnostic interpretations, which are however also agnostic to biological mechanisms. Combining metabolic modelling, 'omics, and imaging data via multimodal AI can generate predictions that can be interpreted mechanistically and transparently, therefore with significantly higher therapeutic potential. <<<

Andrew M. Gross, Subramanian S. Ajay, Vani Rajan, Carolyn Brown, Krista Bluske, Nicole J. Burns, Aditi Chawla, Alison J. Coffey, Alka Malhotra, Alicia Scocchia, Erin Thorpe, Natasa Dzidic, Karine Hovanes, Trilochan Sahoo, Egor Dolzhenko, Bryan Lajoie, Amirah Khouzam, Shimul Chowdhury, John Belmont, Eric Roller, Sergii Ivakhno, Stephen Tanner, Julia McEachern, Tina Hambuch, Michael Eberle, R. Tanner Hagelstrom, David R. Bentley, Denise L. Perry, Ryan J. Taft <<<

Diffusion models have significantly advanced the fields of image, audio, andvideo generation, but they depend on an iterative sampling process that causesslow generation. To overcome this limitation, we propose consistency models, anew family of models that generate high quality samples by directly mappingnoise to data. They support fast one-step generation by design, while stillallowing multistep sampling to trade compute for sample quality. They alsosupport zero-shot data editing, such as image inpainting, colorization, andsuper-resolution, without requiring explicit training on these tasks.Consistency models can be trained either by distilling pre-trained diffusionmodels, or as standalone generative models altogether. Through extensiveexperiments, we demonstrate that they outperform existing distillationtechniques for diffusion models in one- and few-step sampling, achieving thenew state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 forone-step generation. When trained in isolation, consistency models become a newfamily of generative models that can outperform existing one-step,non-adversarial generative models on standard benchmarks such as CIFAR-10,ImageNet 64x64 and LSUN 256x256. <<<