A strict testament/covenant or Calvinist/Federalist interpretation of Reformed federal thought is insisted upon by the Federalist scholars. This entails a monopolizing of federal thought by the ...Zurich line of theology. They further identify Rhineland theology with the later Cocceian-historical federalism. In turn, the testament/covenant division is made conceptually identical to the system/history dichotomy. As Puritan federalism reflects both the Genevan and Zurich influences, however, the present study suggests that the diversity within the Reformed covenant tradition does not stem from a dogmatic division of testament/covenant, but from a methodological distinction of logic/history. This is a more constructive and inclusive approach, that recognizes loci and heilsgeschichtlich as distinct yet complementary patterns of covenant thought sanctioned by Scripture. The thesis of the present study is to identify the loci motifs in the federal thought of two eminent Puritans, William Perkins (1558-1602) and John Preston (1587-1628), and thereby to present Puritan loci federalism as a biblically sanctioned alternative to historical federalism. The thesis is developed in three parts. First, Perkins' covenant thought is construed mainly in terms of redemptive logic, not redemptive history. It represents a beginning of the Reformed covenant tradition which stated covenant theology from a ordo salutis, rather than historia salutis, perspective. While recognizing the latter, our study affirms loci federal thought as an alternative pattern which, characterized by its logical and decretal outlook, sees all God's covenantal dealings with man as a systematic whole. Second, the loci pattern gave rise to a covenantal piety which struck a balance between the "transcendence" and the "immanence" of the covenant. Construed largely in terms of soteriology and personal piety, the loci pattern sees the covenant as a dogmatic link between the decree and faith. This "piety-in-system" synthesis is the hallmark of Perkins' federal thought. Third, the loci covenant pattern undergirds the Puritan federal thought of the seventeenth-century, viz. John Preston. The Federalist scholars assert that Preston, along with the Cocceians, rediscovered the "lost" federalism of Rhineland. Puritan federal thought is thus argued to be Federalist, not Calvinist. Our study shows that Preston's covenant thought is a further development and flowering of the Perkinsian loci federal thought expressed in "piety-in-system." He understands the covenant to be the outward means of the double decree, expresses it in the form of the ordo salutis, and uses its bilateral aspect as the universal gospel offer.
Altered lipid metabolism is increasingly recognized as a signature of cancer cells. Enabled by label-free Raman spectromicroscopy, we performed quantitative analysis of lipogenesis at single-cell ...level in human patient cancerous tissues. Our imaging data revealed an unexpected, aberrant accumulation of esterified cholesterol in lipid droplets of high-grade prostate cancer and metastases. Biochemical study showed that such cholesteryl ester accumulation was a consequence of loss of tumor suppressor PTEN and subsequent activation of PI3K/AKT pathway in prostate cancer cells. Furthermore, we found that such accumulation arose from significantly enhanced uptake of exogenous lipoproteins and required cholesterol esterification. Depletion of cholesteryl ester storage significantly reduced cancer proliferation, impaired cancer invasion capability, and suppressed tumor growth in mouse xenograft models with negligible toxicity. These findings open opportunities for diagnosing and treating prostate cancer by targeting the altered cholesterol metabolism.
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•Aberrant cholesteryl ester accumulation is found in advanced human prostate cancer•Cholesteryl ester accumulation is induced by PTEN loss and PI3K/AKT activation•Inhibition of cholesterol esterification impairs cancer aggressiveness
Altered lipid metabolism is a signature of cancer cells. Yue et al. apply label-free Raman spectromicroscopy to reveal an aberrant accumulation of esterified cholesterol, induced by PTEN loss and PI3K/AKT activation, in advanced human prostate cancer. Inhibition of cholesterol esterification impairs cancer aggressiveness.
Although low vegetation productivity has been observed in karst regions, whether and how bedrock geochemistry contributes to the low karstic vegetation productivity remain unclear. In this study, we ...address this knowledge gap by exploring the importance of bedrock geochemistry on vegetation productivity based on a critical zone investigation across a typical karst region in Southwest China. We show silicon and calcium concentrations in bedrock are strongly correlated with the regolith water loss rate (RWLR), while RWLR can predict vegetation productivity more effectively than previous models. Furthermore, the analysis based on 12 selected karst regions worldwide further suggest that lithological regulation has the potential to obscure and distort the influence of climate change. Our study implies that bedrock geochemistry could exert effects on vegetation growth in karst regions and highlights that the critical role of bedrock geochemistry for the karst region should not be ignored in the earth system model.
We propose a deep learning approach to free-hand sketch recognition that achieves state-of-the-art performance, significantly surpassing that of humans. Our superior performance is a result of ...modelling and exploiting the unique characteristics of free-hand sketches, i.e., consisting of an ordered set of strokes but lacking visual cues such as colour and texture, being highly iconic and abstract, and exhibiting extremely large appearance variations due to different levels of abstraction and deformation. Specifically, our deep neural network, termed Sketch-a-Net has the following novel components: (i) we propose a network architecture designed for sketch rather than natural photo statistics. (ii) Two novel data augmentation strategies are developed which exploit the unique sketch-domain properties to modify and synthesise sketch training data at multiple abstraction levels. Based on this idea we are able to both significantly increase the volume and diversity of sketches for training, and address the challenge of varying levels of sketching detail commonplace in free-hand sketches. (iii) We explore different network ensemble fusion strategies, including a re-purposed joint Bayesian scheme, to further improve recognition performance. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photos or sketches. Furthermore, through visualising the learned filters, we offer useful insights in to where the superior performance of our network comes from.
Human sketches are unique in being able to capture both the spatial topology of a visual object, as well as its subtle appearance details. Fine-grained sketch-based image retrieval (FG-SBIR) ...importantly leverages on such fine-grained characteristics of sketches to conduct instance-level retrieval of photos. Nevertheless, human sketches are often highly abstract and iconic, resulting in severe misalignments with candidate photos which in turn make subtle visual detail matching difficult. Existing FG-SBIR approaches focus only on coarse holistic matching via deep cross-domain representation learning, yet ignore explicitly accounting for fine-grained details and their spatial context. In this paper, a novel deep FG-SBIR model is proposed which differs significantly from the existing models in that: (1) It is spatially aware, achieved by introducing an attention module that is sensitive to the spatial position of visual details: (2) It combines coarse and fine semantic information via a shortcut connection fusion block: and (3) It models feature correlation and is robust to misalignments between the extracted features across the two domains by introducing a novel higher-order learnable energy function (HOLEF) based loss. Extensive experiments show that the proposed deep spatial-semantic attention model significantly outperforms the state-of-the-art.
Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense ...illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction in the number of raw images required for super-resolution SIM, and generate images under extreme low light conditions (at least 100× fewer photons). We validate the performance of deep neural networks on different cellular structures and achieve multi-color, live-cell super-resolution imaging with greatly reduced photobleaching.
Previous studies have observed evoked response latency as well as gamma band superior temporal gyrus (STG) auditory abnormalities in individuals with autism spectrum disorders (ASD). A limitation of ...these studies is that associations between these two abnormalities, as well as the full extent of oscillatory phenomena in ASD in terms of frequency and time, have not been examined. Subjects were presented pure tones at 200, 300, 500, and 1,000 Hz while magnetoencephalography assessed activity in STG auditory areas in a sample of 105 children with ASD and 36 typically developing controls (TD). Findings revealed a profile such that auditory STG processes in ASD were characterized by pre-stimulus abnormalities across multiple frequencies, then early high-frequency abnormalities followed by low-frequency abnormalities. Increased pre-stimulus activity was a ‘core’ abnormality, with pre-stimulus activity predicting post-stimulus neural abnormalities, group membership, and clinical symptoms (CELF-4 Core Language Index). Deficits in synaptic integration in the auditory cortex are associated with oscillatory abnormalities in ASD as well as patient symptoms. Increased pre-stimulus activity in ASD likely demonstrates a fundamental signal-to-noise deficit in individuals with ASD, with elevations in oscillatory activity suggesting an inability to maintain an appropriate ‘neural tone’ and an inability to rapidly return to a resting state prior to the next stimulus.
We aim to learn a domain generalizable person re-identification (ReID) model. When such a model is trained on a set of source domains (ReID datasets collected from different camera networks), it can ...be directly applied to any new unseen dataset for effective ReID without any model updating. Despite its practical value in real-world deployments, generalizable ReID has seldom been studied. In this work, a novel deep ReID model termed Domain-Invariant Mapping Network (DIMN) is proposed. DIMN is designed to learn a mapping between a person image and its identity classifier, i.e., it produces a classifier using a single shot. To make the model domain-invariant, we follow a meta-learning pipeline and sample a subset of source domain training tasks during each training episode. However, the model is significantly different from conventional meta-learning methods in that: (1) no model updating is required for the target domain, (2) different training tasks share a memory bank for maintaining both scalability and discrimination ability, and (3) it can be used to match an arbitrary number of identities in a target domain. Extensive experiments on a newly proposed large-scale ReID domain generalization benchmark show that our DIMN significantly outperforms alternative domain generalization or meta-learning methods.
The problem of fine-grained sketch-based image retrieval (FG-SBIR) is defined and investigated in this paper. In FG-SBIR, free-hand human sketch images are used as queries to retrieve photo images ...containing the same object instances. It is thus a cross-domain (sketch to photo) instance-level retrieval task. It is an extremely challenging problem because (i) visual comparisons and matching need to be executed under large domain gap, i.e., from black and white line drawing sketches to colour photos; (ii) it requires to capture the fine-grained (dis)similarities of sketches and photo images while free-hand sketches drawn by different people present different levels of deformation and expressive interpretation; and (iii) annotated cross-domain fine-grained SBIR datasets are scarce, challenging many state-of-the-art machine learning techniques, particularly those based on deep learning. In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based object instance retrieval application. Specifically, a new large-scale FG-SBIR database is introduced which is carefully designed to reflect the real-world application scenarios. A deep cross-domain matching model is then formulated to solve the intrinsic drawing style variability, large domain gap issues, and capture instance-level discriminative features. It distinguishes itself by a carefully designed attention module. Extensive experiments on the new dataset demonstrate the effectiveness of the proposed model and validate the need for a rigorous definition of the FG-SBIR problem and collecting suitable datasets.
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