The paradigm of surface-expressed programmed death ligand 1 (PDL1) signalling to immune cell programmed death 1 (PD1) to inhibit antitumour immunity has helped to develop effective and revolutionary immunotherapies using antibodies blocking these cell-extrinsic interactions. The recent discovery of cancer cell-intrinsic PDL1 signals has broadened understanding of pathologic tumour PDL1 signal consequences that now includes control of tumour growth and survival pathways, stemness, immune effects, DNA damage responses and gene expression regulation. Many such effects are PD1-independent. These insights demonstrate that the prevailing cell-extrinsic PDL1 signalling paradigm is useful, but incomplete in important respects. This Perspective discusses historical and recent advances in understanding cancer cell-intrinsic PDL1 signals, mechanisms for signal controls and important immunopathologic consequences including resistance to cytotoxic agents, targeted small molecules and immunotherapies. Cancer cell-intrinsic PDL1 signals present novel drug discovery targets and also have potential as reliable treatment response biomarkers. Cancer cell-intrinsic PD1 signals and cell-intrinsic PDL1 signals in non-cancer cells are discussed briefly, as are PDL1 signals from soluble and vesicle-bound PDL1 and PDL1 isoforms. We conclude with suggestions for addressing the most pressing challenges and opportunities in this rapidly developing field. <<<

Despite over a century of research on intelligence, the cognitive processes underlying intelligent behavior are still unclear. In this review, we summarize empirical results investigating the contribution of cognitive processes associated with working memory capacity, processing speed, and executive processes to intelligence differences. Specifically, we (a) evaluate how cognitive processes associated with the three different cognitive domains have been measured, and (b) how these processes are related to individual differences in intelligence. Consistently, this review illustrates that isolating single cognitive processes using average performance in cognitive tasks is hardly possible. Instead, formal models that implement theories of cognitive processes underlying performance in different cognitive tasks may provide more adequate indicators of single cognitive processes. Therefore, we outlined which models for working memory capacity, processing speed, and executive processes may provide more specific insights into cognitive processes associated with individual differences in intelligence. Finally, we discuss implications of a process-oriented intelligence research using cognitive measurement models for psychometric theories of intelligence and argue that a model-based approach might overcome validity problems of traditional intelligence theories. <<<

Affine registration is indispensable in a comprehensive medical image registration pipeline. However, only a few studies focus on fast and robust affine registration algorithms. Most of these studies utilize convolutional neural networks (CNNs) to learn joint affine and non-parametric registration, while the standalone performance of the affine subnetwork is less explored. Moreover, existing CNN-based affine registration approaches focus either on the local misalignment or the global orientation and position of the input to predict the affine transformation matrix, which are sensitive to spatial initialization and exhibit limited generalizability apart from the training dataset. In this paper, we present a fast and robust learning-based algorithm, Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image registration. Our method naturally leverages the global connectivity and locality of the convolutional vision transformer and the multi-resolution strategy to learn the global affine registration. We evaluate our method on 3D brain atlas registration and template-matching normalization. Comprehensive results demonstrate that our method is superior to the existing CNNs-based affine registration methods in terms of registration accuracy, robustness and generalizability while preserving the runtime advantage of the learning-based methods. The source code is available at this https URL. <<<

Vaccines are one of the greatest successes of public health, preventing millions of cases of disease and death in children each year. However, the efficacy of many vaccines can vary greatly between infants from geographically and socioeconomically distinct locations. Differences in the composition of the intestinal microbiome have emerged as one of the main factors that can account for variations in immunisation outcomes. In this Review, we assess the influence of the gut microbiota upon early life immunity, focusing on two important members of the microbiota with health-promoting and immunomodulatory properties: Bifidobacterium and Bacteroides. Additionally, we discuss their immune stimulatory microbial properties, interactions with the host, and their effect on vaccine responses and efficacy in infants. We also provide an overview of current microbiota-based approaches to enhance vaccine outcomes, and describe novel microbe-derived components that could lead to safer, more effective vaccines and vaccine adjuvants. <<<

Sjors G J G In 't Veld, Mohammad Arkani, Edward Post, Mafalda Antunes-Ferreira, Silvia D'Ambrosi, Daan C L Vessies, Lisa Vermunt, Adrienne Vancura, Mirte Muller, Anna-Larissa N Niemeijer, Jihane Tannous, Laura L Meijer, Tessa Y S Le Large, Giulia Mantini, Niels E Wondergem, Kimberley M Heinhuis, Sandra van Wilpe, A Josien Smits, Esther E E Drees, Eva Roos, Cyra E Leurs, Lee-Ann Tjon Kon Fat, Ewoud J van der Lelij, Govert Dwarshuis, Maarten J Kamphuis, Lisanne E Visser, Romee Harting, Annemijn Gregory, Markus W Schweiger, Laurine E Wedekind, Jip Ramaker, Kenn Zwaan, Heleen Verschueren, Idris Bahce, Adrianus J de Langen, Egbert F Smit, Michel M van den Heuvel, Koen J Hartemink, Marijke J E Kuijpers, Mirjam G A Oude Egbrink, Arjan W Griffioen, Rafael Rossel, T Jeroen N Hiltermann, Elizabeth Lee-Lewandrowski, Kent B Lewandrowski, Philip C De Witt Hamer, Mathilde Kouwenhoven, Jaap C Reijneveld, William P J Leenders, Ann Hoeben, Irma M Verdonck-de Leeuw, C René Leemans, Robert J Baatenburg de Jong, Chris H J Terhaard, Robert P Takes, Johannes A Langendijk, Saskia C de Jager, Adriaan O Kraaijeveld, Gerard Pasterkamp, Minke Smits, Jack A Schalken, Sylwia Łapińska-Szumczyk, Anna Łojkowska, Anna J Żaczek, Henk Lokhorst, Niels W C J van de Donk, Inger Nijhof, Henk-Jan Prins, Josée M Zijlstra, Sander Idema, Johannes C Baayen, Charlotte E Teunissen, Joep Killestein, Marc G Besselink, Lindsay Brammen, Thomas Bachleitner-Hofmann, Farrah Mateen, John T M Plukker, Michal Heger, Quirijn de Mast, Ton Lisman, D Michiel Pegtel, Harm-Jan Bogaard, Jacek Jassem, Anna Supernat, Niven Mehra, Winald Gerritsen, Cornelis D de Kroon, Christianne A R Lok, Jurgen M J Piek, Neeltje Steeghs, Winan J van Houdt, Ruud H Brakenhoff, Gabe S Sonke, Henk M Verheul, Elisa Giovannetti, Geert Kazemier, Siamack Sabrkhany, Ed Schuuring, Erik A Sistermans, Rob Wolthuis, Hanne Meijers-Heijboer, Josephine Dorsman, Cees Oudejans, Bauke Ylstra, Bart A Westerman, Daan van den Broek, Danijela Koppers-Lalic, Pieter Wesseling, R Jonas A Nilsson, W Peter Vandertop, David P Noske, Bakhos A Tannous, Nik Sol, Myron G Best, Thomas Wurdinger <<<

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I-IV cancer patients and in half of 352 stage I-III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening. <<<

Children with genetic diseases endure a prolonged and costly "diagnostic odyssey." The use of whole exome sequencing (WES) and whole genome sequencing (WGS) has improved the diagnosis rate, ending the odyssey. However, the additional costs associated WES/WGS has impeded their adoption in Asian settings. We aim to estimate the expected change to the mean number of diagnostic tests used, and the associated costs from a decision to use WES early in the diagnostic pathways of pediatric phenotypes, as compared to Existing Practice. Retrospective data from a patient cohort recruited under the Singapore Undiagnosed Disease Program from a tertiary hospital in Singapore, for the period October 2004 to September 2020, was analyzed. Four phenotype-specific subgroups were used: multiple congenital anomalies (MCA) without developmental delay; global developmental delay (GDD); neuromuscular disorder (NMD) and primary immunodeficiency disorder (PID). Patients had undergone a traditional diagnostic pathway and received a diagnosis either through clinical exome or WES or WGS. A costs only analysis was performed, by tabulating the outcomes "test quantity" and "test costs" incurred by patients. The outcomes were compared with alternate diagnostic pathways which incorporates the early introduction of WES trio testing. To include uncertainty in cost outcomes, simulation studies were done on uncertain parameters. Cost outcomes are reported in Singapore dollars (S$). The 92 included patients had MCA (n = 48), GDD (n = 29), NMD (n = 10), or PID (n = 5). Patients were aged between 18 days and 26 years, 52.2% were males. The majority were of Chinese ethnicity (81.5%). If patients had access to WES directly, test quantity reduced by 97.38% for MCA, 96.98% for GDD, 96.56% for NMD, and 99.84% for PID. The expected cost savings per patient were $5940 for MCA (US$4433), $5342 for GDD (US$3986), $4622 for NMD (US$3449), and $58,497 for PID (US$43,654). Uncertainty assessment for MCA and GDD patients showed a respective likelihood of 86.9% and 97.4% for cost savings. Adoption of alternate diagnostic pathways with early WES in selected pediatric subgroups are likelt to reduce costs, when compared to Existing Practice. Benefits arising from earlier diagnosis, and the potential cost savings could mitigate the large initial cost of implementing WES in Asian settings. <<<

Recent breakthroughs in cancer immunotherapy and decreasing costs of high-throughput technologies have sparked intensive research into tumour-immune cell interactions using genomic tools. The wealth of the generated data and the added complexity pose considerable challenges and require computational tools to process, to analyse and to visualize the data. Recently, various tools have been developed and used to mine tumour immunologic and genomic data effectively and to provide novel mechanistic insights. Here, we review computational genomics tools for cancer immunology and provide information on the requirements and functionality in order to assist in the selection of tools and assembly of analytical pipelines. <<<

Zhi-Wei Guo , Ke Wang , Xiang Huang , Kun Li , Guojun Ouyang , Xu Yang , Jiayu Tan , Haihong Shi , Liangping Luo , Xincai Zhang , Min Zhang , Bo-Wei Han , Xiangming Zhai , Yingsong Wu , Fang Yang , Xue-Xi Yang , Jia Tang <<<

Preterm birth (PTB) occurs in around 11% of all births worldwide, resulting in significant morbidity and mortality for both mothers and offspring. Identification of pregnancies at risk of preterm birth in early pregnancy may help improve intervention and reduce its incidence. However, there exist few methods for PTB prediction developed with large sample size, high throughput screening and validation in independent cohorts. Here, we established a large scale, multi center, and case control study that included 2,590 pregnancies (2,072 full term and 518 preterm pregnancies) from three independent hospitals to develop a preterm birth classifier. We implemented whole genome sequencing on their plasma cfDNA and then their promoter profiling (read depth spanning from -1 KB to +1 KB around the transcriptional start site) was analyzed. Using three machine learning models and two feature selection algorithms, classifiers for predicting preterm delivery were developed. Among them, a classifier based on the support vector machine model and backward algorithm, named PTerm (Promoter profiling classifier for preterm prediction), exhibited the largest AUC value of 0.878 (0.852-0.904) following LOOCV cross validation. More importantly, PTerm exhibited good performance in three independent validation cohorts and achieved an overall AUC of 0.849 (0.831-0.866). Taken together, PTerm could be based on current noninvasive prenatal test (NIPT) data without changing its procedure or adding detection cost, which can be easily adapted for preclinical tests. <<<

Ja Hye Kim, Shinwon Hwang, Hyeonju Son, Dongsun Kim, Il Bin Kim, Myeong-Heui Kim, Nam Suk Sim, Dong Seok Kim, Yoo-Jin Ha, Junehawk Lee, Hoon-Chul Kang, Jeong Ho Lee, Sangwoo Kim <<<

Most somatic mutations that arise during normal development are present at low levels in single or multiple tissues depending on the developmental stage and affected organs. However, the effect of human developmental stages or mutations of different organs on the features of somatic mutations is still unclear. Here, we performed a systemic and comprehensive analysis of low-level somatic mutations using deep whole-exome sequencing (average read depth ~500×) of 498 multiple organ tissues with matched controls from 190 individuals. Our results showed that early clone-forming mutations shared between multiple organs were lower in number but showed higher allele frequencies than late clone-forming mutations [0.54 vs. 5.83 variants per individual; 6.17% vs. 1.5% variant allele frequency (VAF)] along with less nonsynonymous mutations and lower functional impacts. Additionally, early and late clone-forming mutations had unique mutational signatures that were distinct from mutations that originated from tumors. Compared with early clone-forming mutations that showed a clock-like signature across all organs or tissues studied, late clone-forming mutations showed organ, tissue, and cell-type specificity in the mutation counts, VAFs, and mutational signatures. In particular, analysis of brain somatic mutations showed a bimodal occurrence and temporal-lobe-specific signature. These findings provide new insights into the features of somatic mosaicism that are dependent on developmental stage and brain regions. <<<

Goutham Atla, Silvia Bonàs-Guarch, Mirabai Cuenca-Ardura, Anthony Beucher, Daniel J M Crouch, Javier Garcia-Hurtado, Ignasi Moran, T2DSystems Consortium, Manuel Irimia, Rashmi B Prasad, Anna L Gloyn, Lorella Marselli, Mara Suleiman, Thierry Berney, Eelco J P de Koning, Julie Kerr-Conte, Francois Pattou, John A Todd, Lorenzo Piemonti, Jorge Ferrer <<<

BACKGROUND: Non-coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non-coding variants influence diabetes susceptibility are unknown.RESULTS: We examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that common genetic variation has a widespread influence on the splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome-wide association as well as genetic co-localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G-protein-mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3.CONCLUSIONS: These data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit. <<<

Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. In this paper, we propose a self-supervised framework for learning generalizable representations for non-stationary time series. Our approach, called Temporal Neighborhood Coding (TNC), takes advantage of the local smoothness of a signal's generative process to define neighborhoods in time with stationary properties. Using a debiased contrastive objective, our framework learns time series representations by ensuring that in the encoding space, the distribution of signals from within a neighborhood is distinguishable from the distribution of non-neighboring signals. Our motivation stems from the medical field, where the ability to model the dynamic nature of time series data is especially valuable for identifying, tracking, and predicting the underlying patients' latent states in settings where labeling data is practically impossible. We compare our method to recently developed unsupervised representation learning approaches and demonstrate superior performance on clustering and classification tasks for multiple datasets. <<<

Polymorphic inversions are ubiquitous in humans and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand. <<<

Long-read sequencing (LRS) has been around for more than a decade, but widespread adoption of the technology has been slow due to the perceived high error rates and high sequencing cost. This is changing due to the recent advancements to produce highly accurate sequences and the reducing costs. LRS promises significant improvement over short read sequencing in four major areas: (1) better detection of structural variation (2) better resolution of highly repetitive or nonunique regions (3) accurate long-range haplotype phasing and (4) the detection of base modifications natively from the sequencing data. Several successful applications of LRS have demonstrated its ability to resolve molecular diagnoses where short-read sequencing fails to identify a cause. However, the argument for increased diagnostic yield from LRS remains to be validated. Larger cohort studies may be required to establish the realistic boundaries of LRS's clinical utility and analytical validity, as well as the development of standards for clinical applications. We discuss the limitations of the current standard of care, and contrast with the applications and advantages of two major LRS platforms, PacBio and Oxford Nanopore, for molecular diagnostics of constitutional disorders, and present a critical argument about the potential of LRS in diagnostic settings. <<<

Cancer subtypes identification is one of the critical steps toward advancing personalized anti-cancerous therapies. Accumulation of a massive amount of multi-platform omics data measured across the same set of samples provides an opportunity to look into this deadly disease from several views simultaneously. Few integrative clustering approaches are developed to capture shared information from all the views to identify cancer subtypes. However, they have certain limitations. The challenge here is identifying the most relevant feature space from each omic view and systematically integrating them. Both the steps should lead toward a global clustering solution with biological significance. In this respect, a novel multi-omics clustering algorithm named RISynG (Recursive Integration of Synergised Graph-representations) is presented in this study. RISynG represents each omic view as two representation matrices that are Gramian and Laplacian. A parameterised combination function is defined to obtain a synergy matrix from these representation matrices. Then a recursive multi-kernel approach is applied to integrate the most relevant, shared, and complementary information captured via the respective synergy matrices. At last, clustering is applied to the integrated subspace. RISynG is benchmarked on five multi-omics cancer datasets taken from The Cancer Genome Atlas. The experimental results demonstrate RISynG's efficiency over the other approaches in this domain. <<<

We design a predictive layer for structured-output prediction (SOP) that can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints. Our Semantic Probabilistic Layer (SPL) can model intricate correlations, and hard constraints, over a structured output space all while being amenable to end-to-end learning via maximum likelihood. SPLs combine exact probabilistic inference with logical reasoning in a clean and modular way, learning complex distributions and restricting their support to solutions of the constraint. As such, they can faithfully, and efficiently, model complex SOP tasks beyond the reach of alternative neuro-symbolic approaches. We empirically demonstrate that SPLs outperform these competitors in terms of accuracy on challenging SOP tasks including hierarchical multi-label classification, pathfinding and preference learning, while retaining perfect constraint satisfaction. <<<

Jinrong Peng , Donald E. Richards , Nigel M. Hartley , George P. Murphy , Katrien M. Devos , John E. Flintham , James Beales , Leslie J. Fish , Anthony J. Worland , Fatima Pelica , Duraialagaraja Sudhakar , Paul Christou , John W. Snape , Michael D. Gale , Nicholas P. Harberd <<<

World wheat grain yields increased substantially in the 1960s and 1970s because farmers rapidly adopted the new varieties and cultivation methods of the so-called ‘green revolution’1,2,3,4. The new varieties are shorter, increase grain yield at the expense of straw biomass, and are more resistant to damage by wind and rain3,4. These wheats are short because they respond abnormally to the plant growth hormone gibberellin. This reduced response to gibberellin is conferred by mutant dwarfing alleles at one of two Reduced height-1 (Rht-B1 and Rht-D1) loci4,5. Here we show that Rht-B1/Rht-D1 and maize dwarf-8 (d8)6,7 are orthologues of the Arabidopsis Gibberellin Insensitive (GAI) gene8,9. These genes encode proteins that resemble nuclear transcription factors and contain an SH2-like10 domain, indicating that phosphotyrosine may participate in gibberellin signalling. Six different orthologous dwarfing mutant alleles encode proteins that are altered in a conserved amino-terminal gibberellin signalling domain. Transgenic rice plants containing a mutant GAI allele give reduced responses to gibberellin and are dwarfed, indicating that mutant GAI orthologues could be used to increase yield in a wide range of crop species. <<<

Gene transcription is a highly regulated process in all animals. In Drosophila, two major transcriptional programs, housekeeping and developmental, have promoters with distinct regulatory compatibilities and nucleosome organization. However, it remains unclear how the differences in chromatin structure relate to the distinct regulatory properties and which chromatin remodelers are required for these programs. Using rapid degradation of core remodeler subunits in Drosophila melanogaster S2 cells, we demonstrate that developmental gene transcription requires SWI/SNF-type complexes, primarily to maintain distal enhancer accessibility. In contrast, wild-type-level housekeeping gene transcription requires the Iswi and Ino80 remodelers to maintain nucleosome positioning and phasing at promoters. These differential remodeler dependencies relate to different DNA-sequence-intrinsic nucleosome affinities, which favor a default ON state for housekeeping but a default OFF state for developmental gene transcription. Overall, our results demonstrate how different transcription-regulatory strategies are implemented by DNA sequence, chromatin structure, and remodeler activity. <<<

Probabilistic circuits (PCs) represent a probability distribution as a computational graph. Enforcing structural properties on these graphs guarantees that several inference scenarios become tractable. Among these properties, structured decomposability is a particularly appealing one: it enables the efficient and exact computations of the probability of complex logical formulas, and can be used to reason about the expected output of certain predictive models under missing data. This paper proposes Strudel, a simple, fast and accurate learning algorithm for structured-decomposable PCs. Compared to prior work for learning structured-decomposable PCs, Strudel delivers more accurate single PC models in fewer iterations, and dramatically scales learning when building ensembles of PCs. It achieves this scalability by exploiting another structural property of PCs, called determinism, and by sharing the same computational graph across mixture components. We show these advantages on standard density estimation benchmarks and challenging inference scenarios. <<<