The mechanisms and key factors involved in tumor environments for lung metastasis of CRC are still unclear. Here, using clinical samples from lung metastases of CRC patients, we found that intestinal immune network for IgA production was significantly dysregulated in lung metastases of CRC. Single-cell RNA sequencing discovered a subtype of B cells positive for Erbin, one member of the leucine-rich repeat and PDZ domain (LAP) family, was involved in the lung metastases. Erbin deletion in B cells suppressed lung metastasis of CRC in vivo. And, deletion of Erbin in B cells enhanced the killing effects of CD8 T cells on tumor cells. Mechanistically, Erbin knockout attenuated TGFβ-mediated suppression of migration of CXCR5 IgA cells and STAT6-mediated PD1 expression. Our study uncovered a key role of Erbin in regulating PD1 IgA B cells in lung metastasis of CRC. Targeting Erbin as well as combined use of neutralizing B cells and antibodies neutralizing PD1 suppresses lung metastasis of CRC in mice, suggesting the potential option for treatment of lung metastasis of CRC. <<<

Hai-Liang Zhang, Bing-Xin Hu, Zhi-Ling Li, Tian Du, Jia-Lu Shan, Zhi-Peng Ye, Xiao-Dan Peng, Xuan Li, Yun Huang, Xian-Ying Zhu, Yu-Hong Chen, Gong-Kan Feng, Dajun Yang, Rong Deng, Xiao-Feng Zhu <<<

The accumulation of lipid peroxides is recognized as a determinant of the occurrence of ferroptosis. However, the sensors and amplifying process of lipid peroxidation linked to ferroptosis remain obscure. Here we identify PKCβII as a critical contributor of ferroptosis through independent genome-wide CRISPR-Cas9 and kinase inhibitor library screening. Our results show that PKCβII senses the initial lipid peroxides and amplifies lipid peroxidation linked to ferroptosis through phosphorylation and activation of ACSL4. Lipidomics analysis shows that activated ACSL4 catalyses polyunsaturated fatty acid-containing lipid biosynthesis and promotes the accumulation of lipid peroxidation products, leading to ferroptosis. Attenuation of the PKCβII-ACSL4 pathway effectively blocks ferroptosis in vitro and impairs ferroptosis-associated cancer immunotherapy in vivo. Our results identify PKCβII as a sensor of lipid peroxidation, and the lipid peroxidation-PKCβII-ACSL4 positive-feedback axis may provide potential targets for ferroptosis-associated disease treatment. <<<

Diffusion weighted (DW) MRI facilitates non-invasive quantification of tissue microstructure and, in combination with appropriate signal processing, three-dimensional estimates of fibrous orientation. In recent years, attention has shifted from the diffusion tensor model, which assumes a unimodal Gaussian diffusion displacement profile to recover fibre orientation (with various well-documented limitations), towards more complex high angular resolution diffusion imaging (HARDI) analysis techniques. Spherical deconvolution (SD) approaches assume that the fibre orientation density function (fODF) within a voxel can be obtained by deconvolving a 'common' single fibre response function from the observed set of DW signals. In practice, this common response function is not known a priori and thus an estimated fibre response must be used. Here the establishment of this single-fibre response function is referred to as 'calibration'. This work examines the vulnerability of two different SD approaches to inappropriate response function calibration: (1) constrained spherical harmonic deconvolution (CSHD)--a technique that exploits spherical harmonic basis sets and (2) damped Richardson-Lucy (dRL) deconvolution--a technique based on the standard Richardson-Lucy deconvolution. Through simulations, the impact of a discrepancy between the calibrated diffusion profiles and the observed ('Target') DW-signals in both single and crossing-fibre configurations was investigated. The results show that CSHD produces spurious fODF peaks (consistent with well known ringing artefacts) as the discrepancy between calibration and target response increases, while dRL demonstrates a lower over-all sensitivity to miscalibration (with a calibration response function for a highly anisotropic fibre being optimal). However, dRL demonstrates a reduced ability to resolve low anisotropy crossing-fibres compared to CSHD. It is concluded that the range and spatial-distribution of expected single-fibre anisotropies within an image must be carefully considered to ensure selection of the appropriate algorithm, parameters and calibration. Failure to choose the calibration response function carefully may severely impact the quality of any resultant tractography. <<<

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific--it is what it is by how it differs from alternative experiences; integration says that it is unified--irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as "differences that make a difference" within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure (MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information ΦMax its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true "zombies"--unconscious feed-forward systems that are functionally equivalent to conscious complexes. <<<

高效健全的银行体系是实体经济高质量发展的基石,本文基于2004—2017年105家商业银行数据构建时变"银行—股东"网络,从社会网络这一非正式制度视角考察网络结构对银行效率的影响和微观机制。研究发现:时变"银行—股东"网络中心度提升对银行效率具有积极影响,网络位置越中心,网络广度、中介程度越高,银行效率相对越高;在纳入表内外风险资产作为非期望产出进行效率估算后,网络中心度与银行效率的这一正向关系依然成立,但作用幅度有所降低;在传导机制上,网络中心度的提升通过"竞争机制"和"资源共享机制"改善银行整体效率。同时,银行的交叉持股水平、产权性质及银行类型会对网络结构与银行效率间的关系产生异质性影响。本研究为商业银行进行效率管理提供了来自非正式制度的全新分析视角,也为有关部门制定银行体系的发展战略提供了一定参考。 <<<

Stefanie Warnat-Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N Ahmad Aziz, Sofia Ktena, Florian Tran, Michael Bitzer, Stephan Ossowski, Nicolas Casadei, Christian Herr, Daniel Petersheim, Uta Behrends, Fabian Kern, Tobias Fehlmann, Philipp Schommers, Clara Lehmann, Max Augustin, Jan Rybniker, Janine Altmüller, Neha Mishra, Joana P Bernardes, Benjamin Krämer, Lorenzo Bonaguro, Jonas Schulte-Schrepping, Elena De Domenico, Christian Siever, Michael Kraut, Milind Desai, Bruno Monnet, Maria Saridaki, Charles Martin Siegel, Anna Drews, Melanie Nuesch-Germano, Heidi Theis, Jan Heyckendorf, Stefan Schreiber, Sarah Kim-Hellmuth, COVID-19 Aachen Study (COVAS), Jacob Nattermann, Dirk Skowasch, Ingo Kurth, Andreas Keller, Robert Bals, Peter Nürnberg, Olaf Rieß, Philip Rosenstiel, Mihai G Netea, Fabian Theis, Sach Mukherjee, Michael Backes, Anna C Aschenbrenner, Thomas Ulas, Deutsche COVID-19 Omics Initiative (DeCOI), Monique M B Breteler, Evangelos J Giamarellos-Bourboulis, Matthijs Kox, Matthias Becker, Sorin Cheran, Michael S Woodacre, Eng Lim Goh, Joachim L Schultze <<<

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. <<<

Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Considering the huge success of ResNet in deep learning, it would be natural to train deep SNNs with residual learning. Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning. In this paper, we propose the spike-element-wise (SEW) ResNet to realize residual learning in deep SNNs. We prove that the SEW ResNet can easily implement identity mapping and overcome the vanishing/exploding gradient problems of Spiking ResNet. We evaluate our SEW ResNet on ImageNet, DVS Gesture, and CIFAR10-DVS datasets, and show that SEW ResNet outperforms the state-of-the-art directly trained SNNs in both accuracy and time-steps. Moreover, SEW ResNet can achieve higher performance by simply adding more layers, providing a simple method to train deep SNNs. To our best knowledge, this is the first time that directly training deep SNNs with more than 100 layers becomes possible. <<<

Volunteer role identity is regarded the direct and proximal cause of sustained volunteerism. Self-determination theory suggests that the quality of motivation greatly affects performance and well-being in various contexts. Therefore, this study investigated cross-lagged effects (over a time period of 16 months) between self-determined and controlled motivation, on the one hand, and two types of volunteer role identities, on the other hand: general role identity (GRI) and organization-specific role identity (OSRI). Analyses confirmed positive time-lagged effects of self-determined motivation on both GRI and OSRI. Time-lagged effects in opposite direction were significantly weaker; only OSRI showed a positive impact on subsequent self-determined motivation. OSRI (but not GRI) also had a positive lagged effect on controlled motivation. Overall, this study supported the idea that self-determined motivation represents a causal antecedent of volunteer role identities. <<<

We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme, *forking-sequences*, is designed for sequential nets to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future planned event spikes and cold-starts in real life large-scale forecasting. The performance of the framework is demonstrated in an application to predict the future demand of items sold on this http URL, and in a public probabilistic forecasting competition to predict electricity price and load. <<<

Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/. <<<

We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. Because gradients can be ill-defined and hard to estimate when the data resides on low-dimensional manifolds, we perturb the data with different levels of Gaussian noise, and jointly estimate the corresponding scores, i.e., the vector fields of gradients of the perturbed data distribution for all noise levels. For sampling, we propose an annealed Langevin dynamics where we use gradients corresponding to gradually decreasing noise levels as the sampling process gets closer to the data manifold. Our framework allows flexible model architectures, requires no sampling during training or the use of adversarial methods, and provides a learning objective that can be used for principled model comparisons. Our models produce samples comparable to GANs on MNIST, CelebA and CIFAR-10 datasets, achieving a new state-of-the-art inception score of 8.87 on CIFAR-10. Additionally, we demonstrate that our models learn effective representations via image inpainting experiments. <<<

Denise M Wolf, Christina Yau, Julia Wulfkuhle, Lamorna Brown-Swigart, Rosa I Gallagher, Pei Rong Evelyn Lee, Zelos Zhu, Mark J Magbanua, Rosalyn Sayaman, Nicholas O'Grady, Amrita Basu, Amy Delson, Jean Philippe Coppé, Ruixiao Lu, Jerome Braun, I-SPY2 Investigators, Smita M Asare, Laura Sit, Jeffrey B Matthews, Jane Perlmutter, Nola Hylton, Minetta C Liu, Paula Pohlmann, W Fraser Symmans, Hope S Rugo, Claudine Isaacs, Angela M DeMichele, Douglas Yee, Donald A Berry, Lajos Pusztai, Emanuel F Petricoin, Gillian L Hirst, Laura J Esserman, Laura J van 't Veer <<<

Using pre-treatment gene expression, protein/phosphoprotein, and clinical data from the I-SPY2 neoadjuvant platform trial (NCT01042379), we create alternative breast cancer subtypes incorporating tumor biology beyond clinical hormone receptor (HR) and human epidermal growth factor receptor-2 (HER2) status to better predict drug responses. We assess the predictive performance of mechanism-of-action biomarkers from ∼990 patients treated with 10 regimens targeting diverse biology. We explore >11 subtyping schemas and identify treatment-subtype pairs maximizing the pathologic complete response (pCR) rate over the population. The best performing schemas incorporate Immune, DNA repair, and HER2/Luminal phenotypes. Subsequent treatment allocation increases the overall pCR rate to 63% from 51% using HR/HER2-based treatment selection. pCR gains from reclassification and improved patient selection are highest in HR subsets (>15%). As new treatments are introduced, the subtyping schema determines the minimum response needed to show efficacy. This data platform provides an unprecedented resource and supports the usage of response-based subtypes to guide future treatment prioritization. <<<

In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the "here-and-now." Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as a hierarchy, ranging from the concrete to the abstract; however, we argue that there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged, diversity. Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into: modality-specific representations, instantiated on perceptual similarity; multimodal representations, instantiated primarily on the discovery of spatiotemporal contiguity; and categorical representations, instantiated primarily on social interaction. These elements serve as the building blocks of complex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e., predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans' predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology. <<<

Bioactivities, such as freshness maintenance, whitening, and prebiotics, of marine neoagaro-oligosaccharides (NAOS) with 4-12 degrees of polymerization (DPs) have been proven. However, NAOS produced by most marine β-agarases always possess low DPs (≤6) and limited categories; thus, a strategy that can efficiently produce NAOS especially with various DPs ≥8 must be developed. In this study, 60 amino acid residues with no functional annotation result were removed from the C-terminal of agarase AgaM1, and truncated recombinant AgaM1 (trAgaM1) was found to have the ability to produce NAOS with various DPs (4-12) under certain conditions. The catalytic efficiency and stability of trAgaM1 were obviously lower than the wild type (rAgaM1), which probably endowed trAgaM1 with the ability to produce NAOS with various DPs. The optimum conditions for various NAOS production included mixing 1% agarose (w/v) with 10.26 U/ml trAgaM1 and incubating the mixture at 50°C in deionized water for 100 min. This strategy produced neoagarotetraose (NA4), neoagarohexaose (NA6), neoagarooctaose (NA8), neoagarodecaose (NA10), and neoagarododecaose (NA12) at final concentrations of 0.15, 1.53, 1.53, 3.02, and 3.02 g/L, respectively. The NAOS served as end-products of the reaction. The conditions for trAgaM1 expression in a shake flask and 5 L fermentation tank were optimized, and the yields of trAgaM1 increased by 56- and 842-fold compared with those before optimization, respectively. This study provides numerous substrate sources for production and activity tests of NAOS with high DPs and offers a foundation for large-scale production of NAOS with various DPs at a low cost. <<<

AID/APOBEC family enzymes are best known for deaminating cytosine bases to uracil in single-stranded DNA, with characteristic sequence preferences that can produce mutational signatures in targets such as retroviral and cancer cell genomes. These deaminases have also been proposed to function in DNA demethylation via deamination of either 5-methylcytosine (mC) or TET-oxidized mC bases (ox-mCs), which include 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxylcytosine. One specific family member, APOBEC3A (A3A), has been shown to readily deaminate mC, raising the prospect of broader activity on ox-mCs. To investigate this claim, we developed a novel assay that allows for parallel profiling of activity on all modified cytosines. Our steady-state kinetic analysis reveals that A3A discriminates against all ox-mCs by >3700-fold, arguing that ox-mC deamination does not contribute substantially to demethylation. A3A is, by contrast, highly proficient at C/mC deamination. Under conditions of excess enzyme, C/mC bases can be deaminated to completion in long DNA segments, regardless of sequence context. Interestingly, under limiting A3A, the sequence preferences observed with targeting unmodified cytosine are further exaggerated when deaminating mC. Our study informs how methylation, oxidation, and deamination can interplay in the genome and suggests A3A's potential utility as a biotechnological tool to discriminate between cytosine modification states. <<<

BACKGROUND: Artesunate (ART) has been used extensively as anti-malarial drugs worldwide. Besides, it has also been reported to have anti-cancer activities. This study was aimed to explore the anti-cancer activity of ART in combination with cisplatin (CIS) on A549 cells.METHODS: Cells were cultured with different concentrations of ART and/or CIS for 24, 48, or 72 h to test the anti-proliferative effects by CCK-8 assay. Colony formation assay and EdU staining were also performed. TUNEL staining was used to illustrate the morphologic changes. Cell cycle and apoptosis were determined by flow cytometry assay, and Western blot analysis was conducted to detect the expression of apoptosis- and proliferation-related proteins. Caspase activities were determined by colorimetric assay kit. Moreover, the synergistic effect of ART with CIS in A549 cell xenograft model was also determined.RESULTS: ART significantly inhibited cell proliferation in dose- and time-dependent manners. Collectively, the combination treatment remarkably decreased colony formation rates and increased the rates of TUNEL-positive cells compared with mono-treatment. Mechanistically, the combination treatment influenced the expression of Bcl-2, Bax, p-P53, Caspase-3/7, Caspase-9, CyclinB1, P34, P21, and synergistically regulated the activity of P38/JNK/ERK1/2 MAPK pathway. In mice A549 xenograft tumors, the combination strategy significantly increased the anti-cancer efficacy of ART and CIS alone, consistent with the in vitro observations.CONCLUSIONS: ART exhibited significant anti-tumor effect on A549 cells and this efficiency could be enhanced by combination with CIS. <<<