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PurposeThe purpose of this paper is to examine network failures and the main reasons why network organizations, intentionally developed by a group of actors to pursue specific goals, become unfruitful and fail in their goals and e...
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PurposeThe purpose of this paper is to examine network failures and the main reasons why network organizations, intentionally developed by a group of actors to pursue specific goals, become unfruitful and fail in their goals and expectations of creating collective value. The goal of this paper is thus to contribute a better understanding of the reasons network organizations encounter problems in their dynamics that prevent them from reaching the expected outcomes.Design/methodology/approachThe study is firstly based on a literature review finalized to identify the main variables considered as potentially impacting on network failures. Secondly, the paper is based on a survey conducted on 189 strategic networks that highlighted difficulties in achieving their goals. An analysis of the 24 questionnaires returned generated the results discussed. The empirical study concerns strategic networks intentionally created and signed by Italian SMEs according to a specific law designed to promote the development of inter-firm cooperation ("network contracts").FindingsThe results of the research highlight the role of specific key items related to individual, structural, legitimacy, interaction and governance variables in explaining failures in network organizations. According to the data, failure can occur immediately before the network start-up, resulting in a blocked network or in a subsequent developmental stage, resulting in a dormant network. The empirical research demonstrated that the items affecting network failure differ between blocked and dormant networks. The authors explain such differences, considering them according to the expected goals declared by the two different types of networks.Originality/valueThe question of why networks fail is relevant in times of disruption and digitalization when new forms of organization are needed to link businesses and various stakeholders and thereby develop innovative and sustainable ideas for an entrepreneurial future. However, very few studies have examined network failure. The study contributes to this field of research by investigating the dynamics of networks intentionally developed to reach shared goals. The findings can be useful to both companies that decide to start up a strategic network and the policymakers that promote, finance and monitor inter-firm collaboration.
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Abstract Microsatellite instability (MSI) status is an important prognostic marker for various cancers. Furthermore, because immune checkpoint inhibitors are much more effective in tumors with high level of MSI (MSI‐H), MSI statu...
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Abstract Microsatellite instability (MSI) status is an important prognostic marker for various cancers. Furthermore, because immune checkpoint inhibitors are much more effective in tumors with high level of MSI (MSI‐H), MSI status is routinely tested in multiple cancer types. Therefore, many studies have tested the feasibility of deep learning (DL)‐based prediction of MSI status from hematoxylin and eosin (H&E)‐stained tissue slides. In the present study, we attempted a fully automated classification of MSI status in gastric cancer (GC) tissue slides. For frozen and formalin‐fixed paraffin‐embedded (FFPE) GC tissues from The Cancer Genome Atlas (TCGA), the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.893 and 0.902, respectively. The classifier trained with the TCGA FFPE tissues performed well on an external validation Asian FFPE cohort, with an AUC of 0.874. However, the DL‐based classifier seems incompatible with cancers from different organs because morphologic features of MSI‐H tissues are different. Analysis of histomorphologic features of MSI‐H GC tissues suggested that MSI‐H GC could largely be divided into two groups: intestinal type tumors with moderate to poor differentiation and diffuse type mucinous tumors. However, the recognizable morphologic features cannot completely explain the good performance of the DL‐based classifier. These results indicate that DL could automatically learn the optimal features for discrimination of MSI status in GC tissue slides. This study demonstrated the potential of a DL‐based MSI classifier as a screening tool for definitive cases.
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Aims Most validation studies on digital pathology diagnostics have been performed in single institutes. Because rapid consultation on cases with extramural experts is one of the most important uses for digital pathology laboratory...
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Aims Most validation studies on digital pathology diagnostics have been performed in single institutes. Because rapid consultation on cases with extramural experts is one of the most important uses for digital pathology laboratory networks, the aim of this study was to validate a whole‐slide image‐based teleconsultation network between three independent laboratories. Methods and results Each laboratory contributed 30 biopsies and/or excisions, totalling 90 specimens (776 slides) of varying difficulty and covering a wide variety of organs and subspecialties. All slides were scanned centrally at ×40 scanning magnification and uploaded, and subsequently assessed digitally by 16 pathologists using the same image management system and viewer. Each laboratory was excluded from digital assessment of their own cases. Concordance rates between the two diagnostic modalities (light microscopic versus digital) were compared. Loading speed of the images, zooming latency and focus quality were scored. Leaving out eight minor discrepancies without any clinical significance, the concordance rate between remote digital and original microscopic diagnoses was 97.8%. The two cases with a major discordance (for which the light microscopic diagnoses were deemed to be the better ones) resulted from a different interpretation of diagnostic criteria in one case and an image quality issue in the other case. Average scores for loading speed of the images, zooming latency and focus quality were 2.37 (on a scale up to 3), 2.39 (scale up to 3) and 3.06 (scale up to 4), respectively. Conclusions This validation study demonstrates the suitability of a teleconsultation network for remote digital consultation using whole‐slide images. Such networks may contribute to faster revision and consultation in pathology while maintaining diagnostic standards.
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High levels of microsatellite instability (MSI-H) occurs in about 15% of sporadic colorectal cancer (CRC) and is an important predictive marker for response to immune checkpoint inhibitors. To test the feasibility of a deep learni...
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High levels of microsatellite instability (MSI-H) occurs in about 15% of sporadic colorectal cancer (CRC) and is an important predictive marker for response to immune checkpoint inhibitors. To test the feasibility of a deep learning (DL)-based classifier as a screening tool for MSI status, we built a fully automated DL-based MSI classifier using pathology whole-slide images (WSIs) of CRCs. On small image patches of The Cancer Genome Atlas (TCGA) CRC WSI dataset, tissue/non-tissue, normal/ tumor and MSS/MSI-H classifiers were applied sequentially for the fully automated prediction of the MSI status. The classifiers were also tested on an independent cohort. Furthermore, to test how the expansion of the training data affects the performance of the DL-based classifier, additional classifier trained on both TCGA and external datasets was tested. The areas under the receiver operating characteristic curves were 0.892 and 0.972 for the TCGA and external datasets, respectively, by a classifier trained on both datasets. The performance of the DL-based classifier was much better than that of previously reported histomorphology-based methods. We speculated that about 40% of CRC slides could be screened for MSI status without molecular testing by the DL-based classifier. These results demonstrated that the DL-based method has potential as a screening tool to discriminate molecular alteration in tissue slides.
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Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed ba...
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Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed based on the data from multiple medical centers. This requirement makes it a challenging task to normalize the color variance of histopathological images from different medical centers. In this paper, we propose a novel color standardization module named stain standardization capsule based on the capsule network and the corresponding dynamic routing algorithm. The proposed module can learn and generate uniform stain separation outputs for histopathological images in various color appearance without the reference to manually selected template images. The proposed module is light and can be jointly trained with the application-driven CNN model. The proposed method was validated on three histopathology datasets and a cytology dataset, and was compared with state-of-the-art methods. The experimental results have demonstrated that the SSC module is effective in improving the performance of histopathological image analysis and has achieved the best performance in the compared methods.
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This paper considers the problem of assigning the tasks of a distributed application to the processors of a distributed system such that the sum of execution and communication costs is minimized. Previous work has shown this probl...
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This paper considers the problem of assigning the tasks of a distributed application to the processors of a distributed system such that the sum of execution and communication costs is minimized. Previous work has shown this problem to be tractable for a system of two processors or a linear array of N processors, and for distributed programs of serial parallel structures. Here we focus on the assignment problem on a homogeneous network, which is composed of N functionally-identical processors, each with its own memory. Some processors in the network may have unique resources, such as data files or certain peripheral devices. Certain tasks may have to use these unique resources; they are called attached tasks. The tasks of a distributed program should therefore be assigned so as to make use of specific resources located at certain processors in the network while minimizing the amount of interprocessor communication. The assignment problem in such a homogeneous network is known to be NP-hard even for N=3, thus making it intractable for a network with a medium to large number of processors. We therefore focus on task assignment in general array networks, such as linear arrays, meshes, hypercubes, and trees. We first develop a modeling technique that transforms the assignment problem in an array or tree into a minimum-cut maximum-flow problem. The assignment problem is then solved for a general array or tree network in polynomial time.
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There is currently a lack of understanding of the structure of personality disorder (PD) trait facets. The network approach may be useful in providing additional insights, uncovering the unique association of each PD trait facet w...
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There is currently a lack of understanding of the structure of personality disorder (PD) trait facets. The network approach may be useful in providing additional insights, uncovering the unique association of each PD trait facet with every other facet. A unique feature of network analysis is centrality, which indicates the importance of the role a trait facet plays in the context of other trait facets. Using data from 1,940 community Dutch adolescents, we applied network analysis to the 25 trait facets from the 100-item Personality Inventory for DSM-5 Short-Form (PID-5-SF) to explore their associations. We found that some trait facets only seem to be core indicators of their pre-ordained domains, whereas we observed that other trait facets were strongly associated with trait facets outside of their hypothesized domains. Importantly, anxiousness and callousness were identified as highly central facets, being uniquely associated with many other trait facets. Future longitudinal network studies could therefore further examine the possibility of anxiousness and callousness as risk marker trait facets among other PD trait facets.
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The current volume of articles focused on extracellular matrix (ECM) presents somerecent developments in biology, bioengineering, and pathology that reiterate the centraltheme of our previous volume. Collectively, these articles p...
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The current volume of articles focused on extracellular matrix (ECM) presents somerecent developments in biology, bioengineering, and pathology that reiterate the centraltheme of our previous volume. Collectively, these articles provide multidisciplinaryevidence that the ECM operates as a highly dynamic functional framework inphysiology and pathology. Furthermore, they underscore the opportunities availablefor future explorations that can help develop more robust models of ECM structure andfunction for human health and disease.
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During development of the spinal cord, a precise interaction occurs between descending projections and sensory afferents, with spinal networks that lead to expression of coordinated motor output. In the rodent, during the last emb...
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During development of the spinal cord, a precise interaction occurs between descending projections and sensory afferents, with spinal networks that lead to expression of coordinated motor output. In the rodent, during the last embryonic week, motor output first occurs as regular bursts of spontaneous activity, progressing to stochastic patterns of episodes that express bouts of coordinated rhythmic activity perinatally. Locomotor activity becomes functionally mature in the 2nd postnatal wk and is heralded by the onset of weight-bearing locomotion on the 8th and 9th postnatal day. Concomitantly, there is a maturation of intrinsic properties and key conductances mediating plateau potentials. In this review, we discuss spinal neuronal excitability, descending modulation, and afferent modulation in the developing rodent spinal cord. In the adult, plastic mechanisms are much more constrained but become more permissive following neurotrauma, such as spinal cord injury. We discuss parallel mechanisms that contribute to maturation of network function during development to mechanisms of pathological plasticity that contribute to aberrant motor patterns, such as spasticity and clonus, which emerge following central injury.
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