Alberto Santos, Ana R Colaço, Annelaura B Nielsen, Lili Niu, Maximilian Strauss, Philipp E Geyer, Fabian Coscia, Nicolai J Wewer Albrechtsen, Filip Mundt, Lars Juhl Jensen, Matthias Mann <<<

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making. <<<

Longlong Si, Huan Xu, Xueying Zhou, Ziwei Zhang, Zhenyu Tian, Yan Wang, Yiming Wu, Bo Zhang, Zhenlan Niu, Chuanling Zhang, Ge Fu, Sulong Xiao, Qing Xia, Lihe Zhang, Demin Zhou <<<

The conversion of life-threatening viruses into live but avirulent vaccines represents a revolution in vaccinology. In a proof-of-principle study, we expanded the genetic code of the genome of influenza A virus via a transgenic cell line containing orthogonal translation machinery. This generated premature termination codon (PTC)-harboring viruses that exerted full infectivity but were replication-incompetent in conventional cells. Genome-wide optimization of the sites for incorporation of multiple PTCs resulted in highly reproductive and genetically stable progeny viruses in transgenic cells. In mouse, ferret, and guinea pig models, vaccination with PTC viruses elicited robust humoral, mucosal, and T cell-mediated immunity against antigenically distinct influenza viruses and even neutralized existing infecting strains. The methods presented here may become a general approach for generating live virus vaccines that can be adapted to almost any virus. <<<

Anxiety is the most common form of psychopathology, and it is often characterized by chronic impairment across the lifespan. Researchers have identified core neural markers that confer risk for anxious outcomes. An increased error-related negativity (ERN) in anxious individuals has been shown to prospectively predict onset of anxiety disorders across development. Hence, it is critical to examine environmental factors that may shape the ERN. In the current study, we use a large sample of 170 female adolescents aged 10-17 to investigate whether the ERN mediates the relationship between parenting style and anxiety diagnostic status. This study replicates previous findings, and it extends previous work by suggesting that this relationship is more robust in young children as compared to adolescents. Interventions targeting the ERN via parenting may be most effective during childhood. <<<

Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability. Deep-learning accelerators aim to perform deep learning energy-efficiently, usually targeting the inference phase and often by exploiting physical substrates beyond conventional electronics. Approaches so far have been unable to apply the backpropagation algorithm to train unconventional novel hardware in situ. The advantages of backpropagation have made it the de facto training method for large-scale neural networks, so this deficiency constitutes a major impediment. Here we introduce a hybrid in situ-in silico algorithm, called physics-aware training, that applies backpropagation to train controllable physical systems. Just as deep learning realizes computations with deep neural networks made from layers of mathematical functions, our approach allows us to train deep physical neural networks made from layers of controllable physical systems, even when the physical layers lack any mathematical isomorphism to conventional artificial neural network layers. To demonstrate the universality of our approach, we train diverse physical neural networks based on optics, mechanics and electronics to experimentally perform audio and image classification tasks. Physics-aware training combines the scalability of backpropagation with the automatic mitigation of imperfections and noise achievable with in situ algorithms. Physical neural networks have the potential to perform machine learning faster and more energy-efficiently than conventional electronic processors and, more broadly, can endow physical systems with automatically designed physical functionalities, for example, for robotics, materials and smart sensors. <<<

Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language. <<<

Brain development in the first 2 years after birth is extremely dynamic and likely plays an important role in neurodevelopmental disorders, including autism and schizophrenia. Knowledge regarding this period is currently quite limited. We studied structural brain development in healthy subjects from birth to 2. Ninety-eight children received structural MRI scans on a Siemens head-only 3T scanner with magnetization prepared rapid gradient echo T1-weighted, and turbo spin echo, dual-echo (proton density and T2 weighted) sequences: 84 children at 2-4 weeks, 35 at 1 year and 26 at 2 years of age. Tissue segmentation was accomplished using a novel automated approach. Lateral ventricle, caudate, and hippocampal volumes were also determined. Total brain volume increased 101% in the first year, with a 15% increase in the second. The majority of hemispheric growth was accounted for by gray matter, which increased 149% in the first year; hemispheric white matter volume increased by only 11%. Cerebellum volume increased 240% in the first year. Lateral ventricle volume increased 280% in the first year, with a small decrease in the second. The caudate increased 19% and the hippocampus 13% from age 1 to age 2. There was robust growth of the human brain in the first two years of life, driven mainly by gray matter growth. In contrast, white matter growth was much slower. Cerebellum volume also increased substantially in the first year of life. These results suggest the structural underpinnings of cognitive and motor development in early childhood, as well as the potential pathogenesis of neurodevelopmental disorders. <<<

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. <<<

Erick Riquelme, Yu Zhang, Liangliang Zhang, Maria Montiel, Michelle Zoltan, Wenli Dong, Pompeyo Quesada, Ismet Sahin, Vidhi Chandra, Anthony San Lucas, Paul Scheet, Hanwen Xu, Samir M Hanash, Lei Feng, Jared K Burks, Kim-Anh Do, Christine B Peterson, Deborah Nejman, Ching-Wei D Tzeng, Michael P Kim, Cynthia L Sears, Nadim Ajami, Joseph Petrosino, Laura D Wood, Anirban Maitra, Ravid Straussman, Matthew Katz, James Robert White, Robert Jenq, Jennifer Wargo, Florencia McAllister <<<

Most patients diagnosed with resected pancreatic adenocarcinoma (PDAC) survive less than 5 years, but a minor subset survives longer. Here, we dissect the role of the tumor microbiota and the immune system in influencing long-term survival. Using 16S rRNA gene sequencing, we analyzed the tumor microbiome composition in PDAC patients with short-term survival (STS) and long-term survival (LTS). We found higher alpha-diversity in the tumor microbiome of LTS patients and identified an intra-tumoral microbiome signature (Pseudoxanthomonas-Streptomyces-Saccharopolyspora-Bacillus clausii) highly predictive of long-term survivorship in both discovery and validation cohorts. Through human-into-mice fecal microbiota transplantation (FMT) experiments from STS, LTS, or control donors, we were able to differentially modulate the tumor microbiome and affect tumor growth as well as tumor immune infiltration. Our study demonstrates that PDAC microbiome composition, which cross-talks to the gut microbiome, influences the host immune response and natural history of the disease. <<<

The success of checkpoint inhibitors has accelerated the clinical implementation of a vast mosaic of single agents and combination immunotherapies. However, the lack of clinical translation for a number of immunotherapies as monotherapies or in combination with checkpoint inhibitors has clarified that new strategies must be employed to advance the field. The next chapter of immunotherapy should examine the immuno-oncology therapeutic failures, and consider the complexity of immune cell-cancer cell interactions to better design more effective anticancer drugs. Herein, we briefly review the history of immunotherapy and checkpoint blockade, highlighting important clinical failures. We discuss the critical aspects - beyond T cell co-receptors - of immune processes within the tumour microenvironment (TME) that may serve as avenues along which new therapeutic strategies in immuno-oncology can be forged. Emerging insights into tumour biology suggest that successful future therapeutics will focus on two key factors: rescuing T cell homing and dysfunction in the TME, and reappropriating mononuclear phagocyte function for TME inflammatory remodelling. New drugs will need to consider the complex cell networks that exist within tumours and among cancer types. <<<

Alexander Bagaev, Nikita Kotlov, Krystle Nomie, Viktor Svekolkin, Azamat Gafurov, Olga Isaeva, Nikita Osokin, Ivan Kozlov, Felix Frenkel, Olga Gancharova, Nava Almog, Maria Tsiper, Ravshan Ataullakhanov, Nathan Fowler <<<

The clinical use of molecular targeted therapy is rapidly evolving but has primarily focused on genomic alterations. Transcriptomic analysis offers an opportunity to dissect the complexity of tumors, including the tumor microenvironment (TME), a crucial mediator of cancer progression and therapeutic outcome. TME classification by transcriptomic analysis of >10,000 cancer patients identifies four distinct TME subtypes conserved across 20 different cancers. The TME subtypes correlate with patient response to immunotherapy in multiple cancers, with patients possessing immune-favorable TME subtypes benefiting the most from immunotherapy. Thus, the TME subtypes act as a generalized immunotherapy biomarker across many cancer types due to the inclusion of malignant and microenvironment components. A visual tool integrating transcriptomic and genomic data provides a global tumor portrait, describing the tumor framework, mutational load, immune composition, anti-tumor immunity, and immunosuppressive escape mechanisms. Integrative analyses plus visualization may aid in biomarker discovery and the personalization of therapeutic regimens. <<<

Dongming Wei, Sahar Ahmad, Yuyu Guo, Liyun Chen, Yunzhi Huang, Lei Ma, Zhengwang Wu, Gang Li, Li Wang, Weili Lin, Pew-Thian Yap, Dinggang Shen, Qian Wang <<<

Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional infant brain MR images of different subjects, due to fast brain development during infancy. In this paper, we propose a recurrently usable deep neural network for the registration of infant brain MR images. There are three main highlights of our proposed method. (i) We use brain tissue segmentation maps for registration, instead of intensity images, to tackle the issue of rapid contrast changes of brain tissues during the first year of life. (ii) A single registration network is trained in a one-shot manner, and then recurrently applied in inference for multiple times, such that the complex deformation field can be recovered incrementally. (iii) We also propose both the adaptive smoothing layer and the tissue-aware anti-folding constraint into the registration network to ensure the physiological plausibility of estimated deformations without degrading the registration accuracy. Experimental results, in comparison to the state-of-the-art registration methods, indicate that our proposed method achieves the highest registration accuracy while still preserving the smoothness of the deformation field. The implementation of our proposed registration network is available online https://github.com/Barnonewdm/ACTA-Reg-Net. <<<

We propose an echo planar imaging (EPI) distortion correction method (DR-BUDDI), specialized for diffusion MRI, which uses data acquired twice with reversed phase encoding directions, often referred to as blip-up blip-down acquisitions. DR-BUDDI can incorporate information from an undistorted structural MRI and also use diffusion-weighted images (DWI) to guide the registration, improving the quality of the registration in the presence of large deformations and in white matter regions. DR-BUDDI does not require the transformations for correcting blip-up and blip-down images to be the exact inverse of each other. Imposing the theoretical "blip-up blip-down distortion symmetry" may not be appropriate in the presence of common clinical scanning artifacts such as motion, ghosting, Gibbs ringing, vibrations, and low signal-to-noise. The performance of DR-BUDDI is evaluated with several data sets and compared to other existing blip-up blip-down correction approaches. The proposed method is robust and generally outperforms existing approaches. The inclusion of the DWIs in the correction process proves to be important to obtain a reliable correction of distortions in the brain stem. Methods that do not use DWIs may produce a visually appealing correction of the non-diffusion weighted images, but the directionally encoded color maps computed from the tensor reveal an abnormal anatomy of the white matter pathways. <<<

To develop and validate a radiomics model for evaluating pathologic complete response (pCR) to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC). We enrolled 222 patients (152 in the primary cohort and 70 in the validation cohort) with clinicopathologically confirmed LARC who received chemoradiotherapy before surgery. All patients underwent T2-weighted and diffusion-weighted imaging before and after chemoradiotherapy; 2,252 radiomic features were extracted from each patient before and after treatment imaging. The two-sample test and the least absolute shrinkage and selection operator regression were used for feature selection, whereupon a radiomics signature was built with support vector machines. Multivariable logistic regression analysis was then used to develop a radiomics model incorporating the radiomics signature and independent clinicopathologic risk factors. The performance of the radiomics model was assessed by its calibration, discrimination, and clinical usefulness with independent validation. The radiomics signature comprised 30 selected features and showed good discrimination performance in both the primary and validation cohorts. The individualized radiomics model, which incorporated the radiomics signature and tumor length, also showed good discrimination, with an area under the receiver operating characteristic curve of 0.9756 (95% confidence interval, 0.9185-0.9711) in the validation cohort, and good calibration. Decision curve analysis confirmed the clinical utility of the radiomics model. Using pre- and posttreatment MRI data, we developed a radiomics model with excellent performance for individualized, noninvasive prediction of pCR. This model may be used to identify LARC patients who can omit surgery after chemoradiotherapy. . <<<

During the first 2 postnatal years, cortical thickness of the human brain develops dynamically and spatially heterogeneously and likely peaks between 1 and 2 y of age. The striking development renders this period critical for later cognitive outcomes and vulnerable to early neurodevelopmental disorders. However, due to the difficulties in longitudinal infant brain MRI acquisition and processing, our knowledge still remains limited on the dynamic changes, peak age, and spatial heterogeneities of cortical thickness during infancy. To fill this knowledge gap, in this study, we discover the developmental regionalization of cortical thickness, i.e., developmentally distinct regions, each of which is composed of a set of codeveloping cortical vertices, for better understanding of the spatiotemporal heterogeneities of cortical thickness development. We leverage an infant-dedicated computational pipeline, an advanced multivariate analysis method (i.e., nonnegative matrix factorization), and a densely sampled longitudinal dataset with 210 serial MRI scans from 43 healthy infants, with each infant being scheduled to have 7 longitudinal scans at around 1, 3, 6, 9, 12, 18, and 24 mo of age. Our results suggest that, during the first 2 y, the whole-brain average cortical thickness increases rapidly and reaches a plateau at about 14 mo of age and then decreases at a slow pace thereafter. More importantly, each discovered region is structurally and functionally meaningful and exhibits a distinctive developmental pattern, with several regions peaking at varied ages while others keep increasing in the first 2 postnatal years. Our findings provide valuable references and insights for early brain development. <<<