Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are crucial for having the right inventory available at the right time and in the right place. This paper proposes DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an autoregressive recurrent neural network model on a large number of related time series. We demonstrate how the application of deep learning techniques to forecasting can overcome many of the challenges that are faced by widely-used classical approaches to the problem. By means of extensive empirical evaluations on several real-world forecasting datasets, we show that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work. <<<

Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which the hippocampal/entorhinal system (H/E-S) has been optimized over the course of evolution. We have developed a biologically-inspired SLAM architecture based on latent variable generative modeling within the Free Energy Principle and Active Inference (FEP-AI) framework, which affords flexible navigation and planning in mobile robots. We have primarily focused on attempting to reverse engineer H/E-S ‘design’ properties, but here we consider ways in which SLAM principles from robotics may help us better understand nervous systems and emergent minds. After reviewing LatentSLAM and notable features of this control architecture, we consider how the H/E-S may realize these functional properties not only for physical navigation, but also with respect to high-level cognition understood as generalized simultaneous localization and mapping (G-SLAM). We focus on loop-closure, graph-relaxation, and node duplication as particularly impactful architectural features, suggesting these computational phenomena may contribute to understanding cognitive insight (as proto-causal-inference), accommodation (as integration into existing schemas), and assimilation (as category formation). All these operations can similarly be describable in terms of structure/category learning on multiple levels of abstraction. However, here we adopt an ecological rationality perspective, framing H/E-S functions as orchestrating SLAM processes within both concrete and abstract hypothesis spaces. In this navigation/search process, adaptive cognitive equilibration between assimilation and accommodation involves balancing tradeoffs between exploration and exploitation; this dynamic equilibrium may be near optimally realized in FEP-AI, wherein control systems governed by expected free energy objective functions naturally balance model simplicity and accuracy. With respect to structure learning, such a balance would involve constructing models and categories that are neither too inclusive nor exclusive. We propose these (generalized) SLAM phenomena may represent some of the most impactful sources of variation in cognition both within and between individuals, suggesting that modulators of H/E-S functioning may potentially illuminate their adaptive significances as fundamental cybernetic control parameters. Finally, we discuss how understanding H/E-S contributions to G-SLAM may provide a unifying framework for high-level cognition and its potential realization in artificial intelligences. <<<

Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts — especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series. <<<

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most serious malignancies. The main features of HCC are vascular invasion and drug resistance. Ferroptosis is a novel cell program that is involved in several diseases, such as cancer. Heat shock protein beta 1 (HSPB1) is a major component of heat shock proteins. A recent study showed that HSPB1 could be a new therapeutic target for colorectal cancer with 5-fluorouracil-acquired resistance. However, the functional role of HSPB1 in HCC remains unclear.AIM: The aim of this study is to clarify HSPB1 expression in HCC and its potential therapeutic and prognostic value.METHODS: We collected data on HSPB1 expression levels in HCC and normal liver tissues from The Cancer Genome Atlas and Gene Expression Omnibus databases. We then validated it using immunohistochemistry (IHC). Receiver operating characteristic and Kaplan-Meier survival curves were used to investigate the role of HSPB1 in the prognosis analysis of HCC. Further, we used the online Search Tool for the Retrieval of Interacting Genes/Proteins website, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes to conduct enrichment analysis and identify the predictive signaling pathways. Meanwhile, we used the TIMER and GSVA package of R (v3.6.3) to analyze the association between HSPB1 and immunocyte infiltration.RESULTS: Compared to normal tissues, there was differential expression of HSPB1 in pan-cancers. HSPB1 expression was higher in HCC tissues than in normal tissues (p<0.05). There was an evident significant difference between HSPB1 mRNA levels and histologic grade, vascular invasion, and alpha-fetoprotein level (all p values<0.05). Univariate analysis indicated that HCC patients with high HSPB1 levels had shorter overall survival rates than those with low HSPB1 levels (p<0.05). MAPK14, HSPA8, MAPKAPK3, MAPKAPK5, and MAPKAPK2 are essential proteins that interact with HSPB1. There was a significant correlation between HSPB1 expression levels and immune cell infiltration, including CD4+ T cells (r=0.203, p<0.05).CONCLUSION: High HSPB1 expression is closely associated with a worse prognosis in HCC patients, and HSPB1 may be a target of immunotherapy in HCC. <<<

Relative to the merits of authoritative parenting, potential adverse outcomes are well documented for authoritarian and permissive parenting. However, conclusions are typically drawn from single informants. The ability of youths’ emotion regulation skills to mediate outcomes in emerging adults has also not been fully explored. This study investigated whether emotion regulation mediated parenting style history and potential outcomes of mental health and delinquency. Parenting style history and emerging adults’ emotion regulation ability were reported by 110 youth and their caregivers; youth reported on mental health functioning and delinquency. Emerging adults’ emotion regulation ability partially mediated the association between authoritative parenting history and mental health functioning and authoritative parenting history was indirectly related to delinquency through emotion regulation; however, based on all reporters, emotion regulation ability was not associated with authoritarian or permissive parenting style history. Results support that the merits of authoritative parenting may lie in fostering better emotion regulation skills. <<<

To date, only three studies have examined the role of emotion socialization in the emotional functioning of youth with neurodevelopmental disorders. As such, this review article with pilot data sought to provide a call to action and first step in addressing this limited research body. Pilot data was collected with 18 adolescents (Mage = 13.5, SD = 1.6; 70% male) with a neurodevelopmental disorder and their primary caregiver. All adolescents were diagnosed with attention-deficit/hyperactivity disorder and displayed a range of comorbid disorders: autism spectrum disorder (27.8%), anxiety (66.7%), depression (44.4%), and disruptive behavior disorders (50%). Adolescents and caregivers completed a conflict discussion task while physiological, observational, and self-report measures of emotion socialization and emotional functioning were measured. Observed supportive parent emotion socialization behaviors were significantly associated with more observed adaptive emotion regulation strategies, and decreased observed and adolescent-reported negative affect, whereas non-supportive emotion socialization behaviors were associated with more observed negative affect and less observed adaptive emotion regulation strategies. Our pilot findings support growing research suggesting that adaptive parent emotion socialization practices can help foster less negative emotionality and better emotion regulation in youth with neurodevelopment disorders. We make a call to action for more emotion socialization research focused on youth with neurodevelopmental disorders, and propose four important directions for future research: 1) Research examining emotion socialization behaviors during daily life, 2) Understanding the nuanced role of emotion socialization practices, 3) Considering diversity in emotion socialization practices with clinical populations, and 4) Longitudinal and intervention research studies. <<<

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit. <<<

在本文当中，我们首先定义密契经验的内涵、特质以及说明为何选择此四人的主要原因。其次，则分别阐析王重阳等四人之生平大要、开悟的详细经过及开悟的内涵与特质。最后，则进一步分析及比较此四人之密契经验的异同以及我们可以从此探讨当中归纳出的重要结论。藉由如是的探讨，除了可以具体了解中印宗教、哲学的内涵、特质以及其形成与发展的一种重要途径外，也可使我们对于哲学、宗教学及心理学等学科中的重要问题，带来关键的启发。最后，密契经验做为一种了悟真理及彻底解决生命问题的可能方式，对其探讨，亦能给人类的生命，带来重要的启示及指引。 <<<

Ugur Sahin, Petra Oehm, Evelyna Derhovanessian, Robert A Jabulowsky, Mathias Vormehr, Maike Gold, Daniel Maurus, Doreen Schwarck-Kokarakis, Andreas N Kuhn, Tana Omokoko, Lena M Kranz, Mustafa Diken, Sebastian Kreiter, Heinrich Haas, Sebastian Attig, Richard Rae, Katarina Cuk, Alexandra Kemmer-Brück, Andrea Breitkreuz, Claudia Tolliver, Janina Caspar, Juliane Quinkhardt, Lisa Hebich, Malte Stein, Alexander Hohberger, Isabel Vogler, Inga Liebig, Stephanie Renken, Julian Sikorski, Melanie Leierer, Verena Müller, Heidrun Mitzel-Rink, Matthias Miederer, Christoph Huber, Stephan Grabbe, Jochen Utikal, Andreas Pinter, Roland Kaufmann, Jessica C Hassel, Carmen Loquai, Özlem Türeci <<<

Treating patients who have cancer with vaccines that stimulate a targeted immune response is conceptually appealing, but cancer vaccine trials have not been successful in late-stage patients with treatment-refractory tumours. We are testing melanoma FixVac (BNT111)-an intravenously administered liposomal RNA (RNA-LPX) vaccine, which targets four non-mutated, tumour-associated antigens that are prevalent in melanoma-in an ongoing, first-in-human, dose-escalation phase I trial in patients with advanced melanoma (Lipo-MERIT trial, ClinicalTrials.gov identifier NCT02410733). We report here data from an exploratory interim analysis that show that melanoma FixVac, alone or in combination with blockade of the checkpoint inhibitor PD1, mediates durable objective responses in checkpoint-inhibitor (CPI)-experienced patients with unresectable melanoma. Clinical responses are accompanied by the induction of strong CD4 and CD8 T cell immunity against the vaccine antigens. The antigen-specific cytotoxic T-cell responses in some responders reach magnitudes typically reported for adoptive T-cell therapy, and are durable. Our findings indicate that RNA-LPX vaccination is a potent immunotherapy in patients with CPI-experienced melanoma, and suggest the general utility of non-mutant shared tumour antigens as targets for cancer vaccination. <<<

Lena M Kranz, Mustafa Diken, Heinrich Haas, Sebastian Kreiter, Carmen Loquai, Kerstin C Reuter, Martin Meng, Daniel Fritz, Fulvia Vascotto, Hossam Hefesha, Christian Grunwitz, Mathias Vormehr, Yves Hüsemann, Abderraouf Selmi, Andreas N Kuhn, Janina Buck, Evelyna Derhovanessian, Richard Rae, Sebastian Attig, Jan Diekmann, Robert A Jabulowsky, Sandra Heesch, Jessica Hassel, Peter Langguth, Stephan Grabbe, Christoph Huber, Özlem Türeci, Ugur Sahin <<<

Lymphoid organs, in which antigen presenting cells (APCs) are in close proximity to T cells, are the ideal microenvironment for efficient priming and amplification of T-cell responses. However, the systemic delivery of vaccine antigens into dendritic cells (DCs) is hampered by various technical challenges. Here we show that DCs can be targeted precisely and effectively in vivo using intravenously administered RNA-lipoplexes (RNA-LPX) based on well-known lipid carriers by optimally adjusting net charge, without the need for functionalization of particles with molecular ligands. The LPX protects RNA from extracellular ribonucleases and mediates its efficient uptake and expression of the encoded antigen by DC populations and macrophages in various lymphoid compartments. RNA-LPX triggers interferon-α (IFNα) release by plasmacytoid DCs and macrophages. Consequently, DC maturation in situ and inflammatory immune mechanisms reminiscent of those in the early systemic phase of viral infection are activated. We show that RNA-LPX encoding viral or mutant neo-antigens or endogenous self-antigens induce strong effector and memory T-cell responses, and mediate potent IFNα-dependent rejection of progressive tumours. A phase I dose-escalation trial testing RNA-LPX that encode shared tumour antigens is ongoing. In the first three melanoma patients treated at a low-dose level, IFNα and strong antigen-specific T-cell responses were induced, supporting the identified mode of action and potency. As any polypeptide-based antigen can be encoded as RNA, RNA-LPX represent a universally applicable vaccine class for systemic DC targeting and synchronized induction of both highly potent adaptive as well as type-I-IFN-mediated innate immune mechanisms for cancer immunotherapy. <<<

Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph. <<<

Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill this gap, we present a deep learning (DL)-based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We built the DL-based, survival-sensitive model on 360 HCC patients' data using RNA sequencing (RNA-Seq), miRNA sequencing (miRNA-Seq), and methylation data from The Cancer Genome Atlas (TCGA), which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL-based model provides two optimal subgroups of patients with significant survival differences ( = 7.13e-6) and good model fitness [concordance index (C-index) = 0.68]. More aggressive subtype is associated with frequent inactivation mutations, higher expression of stemness markers ( and ) and tumor marker , and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort ( = 230, C-index = 0.75), NCI cohort ( = 221, C-index = 0.67), Chinese cohort ( = 166, C-index = 0.69), E-TABM-36 cohort ( = 40, C-index = 0.77), and Hawaiian cohort ( = 27, C-index = 0.82). This is the first study to employ DL to identify multi-omics features linked to the differential survival of patients with HCC. Given its robustness over multiple cohorts, we expect this workflow to be useful at predicting HCC prognosis prediction. . <<<

在家庭教育投资中,除现金性的显性教育支出外,家庭也会通过放弃潜在收入并增加子女陪护时间的方式进行隐性教育投资。这种看不见的家庭教育投资行为尚未得到学术界的广泛关注,但其对理解中国家庭的教育行为具有重要现实意义。本文利用子女升学压力对家庭教育重视程度的外生冲击作为识别框架,估计了母亲由于子女教育压力而放弃的潜在收入,为识别家庭隐性教育投资行为提供了微观证据。本文实证结果表明,子女升学压力会使母亲月收入显著下降19%,同时父亲收入没有显著变化。异质性分析发现,高学历、非国有行业、育有男孩或独生子女的母亲收入更容易受到子女升学压力的影响。机制分析表明,在子女升学压力下母亲会通过降低劳动参与率、降低工作时长以及增加家庭照料时间等方式进行隐性教育投资。进一步利用地区重点高校录取率反映地区升学压力,发现子女升学压力越大的地区,母亲收入下降幅度越大。本文研究识别了子女升学压力下的家庭隐性教育投资行为,为更好理解中国人力资本积累和家庭劳动供给决策提供了新的观察视角。 <<<

Mitotic cell division increases tumour mutation burden and copy number load, predictive markers of the clinical benefit of immunotherapy. Cell division correlates also with genomic demethylation involving methylation loss in late-replicating partial methylation domains. Here we find that immunomodulatory pathway genes are concentrated in these domains and transcriptionally repressed in demethylated tumours with CpG island promoter hypermethylation. Global methylation loss correlated with immune evasion signatures independently of mutation burden and aneuploidy. Methylome data of our cohort (n = 60) and a published cohort (n = 81) in lung cancer and a melanoma cohort (n = 40) consistently demonstrated that genomic methylation alterations counteract the contribution of high mutation burden and increase immunotherapeutic resistance. Higher predictive power was observed for methylation loss than mutation burden. We also found that genomic hypomethylation correlates with the immune escape signatures of aneuploid tumours. Hence, DNA methylation alterations implicate epigenetic modulation in precision immunotherapy. <<<

Adaptive T-cell immunotherapy holds great promise for the successful treatment of leukemia, as well as other types of cancers. More recently, it was also shown to be an effective treatment option for chronic virus infections in immunosuppressed patients. Autologous or allogeneic T cells used for immunotherapy are usually genetically modified to express novel T-cell or chimeric antigen receptors. The production of such cells was significantly simplified with the CRISPR/Cas system, allowing for the deletion or insertion of novel genes at specific locations within the genome. In this review, we describe recent methodological breakthroughs that were important for the conduction of these genetic modifications, summarize crucial points to be considered when conducting such experiments, and highlight the potential pitfalls of these approaches. <<<

De Huang, Yan Wang, J Will Thompson, Tao Yin, Peter B Alexander, Diyuan Qin, Poorva Mudgal, Haiyang Wu, Yaosi Liang, Lianmei Tan, Christopher Pan, Lifeng Yuan, Ying Wan, Qi-Jing Li, Xiao-Fan Wang <<<

Many cancers have an unusual dependence on glutamine. However, most previous studies have focused on the contribution of glutamine to metabolic building blocks and the energy supply. Here, we report that cancer cells with aberrant expression of glutamate decarboxylase 1 (GAD1) rewire glutamine metabolism for the synthesis of γ-aminobutyric acid (GABA)-a prominent neurotransmitter-in non-nervous tissues. An analysis of clinical samples reveals that increased GABA levels predict poor prognosis. Mechanistically, we identify a cancer-intrinsic pathway through which GABA activates the GABA receptor to inhibit GSK-3β activity, leading to enhanced β-catenin signalling. This GABA-mediated β-catenin activation both stimulates tumour cell proliferation and suppresses CD8 T cell intratumoural infiltration, such that targeting GAD1 or GABAR in mouse models overcomes resistance to anti-PD-1 immune checkpoint blockade therapy. Our findings uncover a signalling role for tumour-derived GABA beyond its classic function as a neurotransmitter that can be targeted pharmacologically to reverse immunosuppression. <<<

Senjie Lin, Shifeng Cheng, Bo Song, Xiao Zhong, Xin Lin, Wujiao Li, Ling Li, Yaqun Zhang, Huan Zhang, Zhiliang Ji, Meichun Cai, Yunyun Zhuang, Xinguo Shi, Lingxiao Lin, Lu Wang, Zhaobao Wang, Xin Liu, Sheng Yu, Peng Zeng, Han Hao, Quan Zou, Chengxuan Chen, Yanjun Li, Ying Wang, Chunyan Xu, Shanshan Meng, Xun Xu, Jun Wang, Huanming Yang, David A Campbell, Nancy R Sturm, Steve Dagenais-Bellefeuille, David Morse <<<

Dinoflagellates are important components of marine ecosystems and essential coral symbionts, yet little is known about their genomes. We report here on the analysis of a high-quality assembly from the 1180-megabase genome of Symbiodinium kawagutii. We annotated protein-coding genes and identified Symbiodinium-specific gene families. No whole-genome duplication was observed, but instead we found active (retro)transposition and gene family expansion, especially in processes important for successful symbiosis with corals. We also documented genes potentially governing sexual reproduction and cyst formation, novel promoter elements, and a microRNA system potentially regulating gene expression in both symbiont and coral. We found biochemical complementarity between genomes of S. kawagutii and the anthozoan Acropora, indicative of host-symbiont coevolution, providing a resource for studying the molecular basis and evolution of coral symbiosis. <<<

Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations such as XGBoost and pGBRT. Although many engineering optimizations have been adopted in these implementations, the efficiency and scalability are still unsatisfactory when the feature dimension is high and data size is large. A major reason is that for each feature, they need to scan all the data instances to estimate the information gain of all possible split points, which is very time consuming. To tackle this problem, we propose two novel techniques: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). With GOSS, we exclude a significant proportion of data instances with small gradients, and only use the rest to estimate the information gain. We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size. With EFB, we bundle mutually exclusive features (i.e., they rarely take nonzero values simultaneously), to reduce the number of features. We prove that finding the optimal bundling of exclusive features is NP-hard, but a greedy algorithm can achieve quite good approximation ratio (and thus can effectively reduce the number of features without hurting the accuracy of split point determination by much). We call our new GBDT implementation with GOSS and EFB LightGBM. Our experiments on multiple public datasets show that, LightGBM speeds up the training process of conventional GBDT by up to over 20 times while achieving almost the same accuracy. <<<

Leore T Geller, Michal Barzily-Rokni, Tal Danino, Oliver H Jonas, Noam Shental, Deborah Nejman, Nancy Gavert, Yaara Zwang, Zachary A Cooper, Kevin Shee, Christoph A Thaiss, Alexandre Reuben, Jonathan Livny, Roi Avraham, Dennie T Frederick, Matteo Ligorio, Kelly Chatman, Stephen E Johnston, Carrie M Mosher, Alexander Brandis, Garold Fuks, Candice Gurbatri, Vancheswaran Gopalakrishnan, Michael Kim, Mark W Hurd, Matthew Katz, Jason Fleming, Anirban Maitra, David A Smith, Matt Skalak, Jeffrey Bu, Monia Michaud, Sunia A Trauger, Iris Barshack, Talia Golan, Judith Sandbank, Keith T Flaherty, Anna Mandinova, Wendy S Garrett, Sarah P Thayer, Cristina R Ferrone, Curtis Huttenhower, Sangeeta N Bhatia, Dirk Gevers, Jennifer A Wargo, Todd R Golub, Ravid Straussman <<<

Growing evidence suggests that microbes can influence the efficacy of cancer therapies. By studying colon cancer models, we found that bacteria can metabolize the chemotherapeutic drug gemcitabine (2',2'-difluorodeoxycytidine) into its inactive form, 2',2'-difluorodeoxyuridine. Metabolism was dependent on the expression of a long isoform of the bacterial enzyme cytidine deaminase (CDD), seen primarily in Gammaproteobacteria. In a colon cancer mouse model, gemcitabine resistance was induced by intratumor Gammaproteobacteria, dependent on bacterial CDD expression, and abrogated by cotreatment with the antibiotic ciprofloxacin. Gemcitabine is commonly used to treat pancreatic ductal adenocarcinoma (PDAC), and we hypothesized that intratumor bacteria might contribute to drug resistance of these tumors. Consistent with this possibility, we found that of the 113 human PDACs that were tested, 86 (76%) were positive for bacteria, mainly Gammaproteobacteria. <<<

Gram-negative bacteria harness multiple protein secretion systems and secrete a large proportion of the proteome. Proteins can be exported to periplasmic space, integrated into membrane, transported into extracellular milieu, or translocated into cytoplasm of contacting cells. It is important for accurate, genome-wide annotation of the secreted proteins and their secretion pathways. In this review, we systematically classified the secreted proteins according to the types of secretion systems in Gram-negative bacteria, summarized the known features of these proteins, and reviewed the algorithms and tools for their prediction. <<<