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. <<<

Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings. <<<

Laura K M Han, Richard Dinga, Tim Hahn, Christopher R K Ching, Lisa T Eyler, Lyubomir Aftanas, Moji Aghajani, André Aleman, Bernhard T Baune, Klaus Berger, Ivan Brak, Geraldo Busatto Filho, Angela Carballedo, Colm G Connolly, Baptiste Couvy-Duchesne, Kathryn R Cullen, Udo Dannlowski, Christopher G Davey, Danai Dima, Fabio L S Duran, Verena Enneking, Elena Filimonova, Stefan Frenzel, Thomas Frodl, Cynthia H Y Fu, Beata R Godlewska, Ian H Gotlib, Hans J Grabe, Nynke A Groenewold, Dominik Grotegerd, Oliver Gruber, Geoffrey B Hall, Ben J Harrison, Sean N Hatton, Marco Hermesdorf, Ian B Hickie, Tiffany C Ho, Norbert Hosten, Andreas Jansen, Claas Kähler, Tilo Kircher, Bonnie Klimes-Dougan, Bernd Krämer, Axel Krug, Jim Lagopoulos, Ramona Leenings, Frank P MacMaster, Glenda MacQueen, Andrew McIntosh, Quinn McLellan, Katie L McMahon, Sarah E Medland, Bryon A Mueller, Benson Mwangi, Evgeny Osipov, Maria J Portella, Elena Pozzi, Liesbeth Reneman, Jonathan Repple, Pedro G P Rosa, Matthew D Sacchet, Philipp G Sämann, Knut Schnell, Anouk Schrantee, Egle Simulionyte, Jair C Soares, Jens Sommer, Dan J Stein, Olaf Steinsträter, Lachlan T Strike, Sophia I Thomopoulos, Marie-José van Tol, Ilya M Veer, Robert R J M Vermeiren, Henrik Walter, Nic J A van der Wee, Steven J A van der Werff, Heather Whalley, Nils R Winter, Katharina Wittfeld, Margaret J Wright, Mon-Ju Wu, Henry Völzke, Tony T Yang, Vasileios Zannias, Greig I de Zubicaray, Giovana B Zunta-Soares, Christoph Abé, Martin Alda, Ole A Andreassen, Erlend Bøen, Caterina M Bonnin, Erick J Canales-Rodriguez, Dara Cannon, Xavier Caseras, Tiffany M Chaim-Avancini, Torbjørn Elvsåshagen, Pauline Favre, Sonya F Foley, Janice M Fullerton, Jose M Goikolea, Bartholomeus C M Haarman, Tomas Hajek, Chantal Henry, Josselin Houenou, Fleur M Howells, Martin Ingvar, Rayus Kuplicki, Beny Lafer, Mikael Landén, Rodrigo Machado-Vieira, Ulrik F Malt, Colm McDonald, Philip B Mitchell, Leila Nabulsi, Maria Concepcion Garcia Otaduy, Bronwyn J Overs, Mircea Polosan, Edith Pomarol-Clotet, Joaquim Radua, Maria M Rive, Gloria Roberts, Henricus G Ruhe, Raymond Salvador, Salvador Sarró, Theodore D Satterthwaite, Jonathan Savitz, Aart H Schene, Peter R Schofield, Mauricio H Serpa, Kang Sim, Marcio Gerhardt Soeiro-de-Souza, Ashley N Sutherland, Henk S Temmingh, Garrett M Timmons, Anne Uhlmann, Eduard Vieta, Daniel H Wolf, Marcus V Zanetti, Neda Jahanshad, Paul M Thompson, Dick J Veltman, Brenda W J H Penninx, Andre F Marquand, James H Cole, Lianne Schmaal <<<

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates. <<<

Luchang Ming, Debao Fu, Zhaona Wu, Hu Zhao, Xingbing Xu, Tingting Xu, Xiaohu Xiong, Mu Li, Yi Zheng, Ge Li, Ling Yang, Chunjiao Xia, Rongfang Zhou, Keyan Liao, Qian Yu, Wenqi Chai, Sijia Li, Yinmeng Liu, Xiaokun Wu, Jianquan Mao, Julong Wei, Xu Li, Lei Wang, Changyin Wu, Weibo Xie <<<

Panicle architecture is a key determinant of rice grain yield and is mainly determined at the 1-2 mm young panicle stage. Here, we investigated the transcriptome of the 1-2 mm young panicles from 275 rice varieties and identified thousands of genes whose expression levels were associated with panicle traits. Multimodel association studies suggested that many small-effect genetic loci determine spikelet per panicle (SPP) by regulating the expression of genes associated with panicle traits. We found that alleles at cis-expression quantitative trait loci of SPP-associated genes underwent positive selection, with a strong preference for alleles increasing SPP. We further developed a method that integrates the associations of cis- and trans-expression components of genes with traits to identify causal genes at even small-effect loci and construct regulatory networks. We identified 36 putative causal genes of SPP, including SDT (MIR156j) and OsMADS17, and inferred that OsMADS17 regulates SDT expression, which was experimentally validated. Our study reveals the impact of regulatory variants on rice panicle architecture and provides new insights into the gene regulatory networks of panicle traits. <<<